Many resources discuss machine learning and data analytics from a technology deployment perspective. From the business standpoint, however, the real value of analytics is in the methodology for solving some systemic holistic problems, rather than a specific technology or platform.
In this presentation, the focus is shifted from the technology deployment to the analytics methodology for solving some holistic business problems. Two examples will be covered in detail:
(i) Analysis of the performance and the optimal staffing of a team of doctors, nurses, and technicians for a large local hospital unit using discrete event simulation with a live demonstration. This simulation methodology is not included in most Machine Learning algorithms libraries.
(ii) Identifying a few factors (or variables) that contribute most to the financial outcome of a local hospital using principal component decomposition (PCD) of the large observational dataset of population demographic and disease prevalence.
The purpose of this presentation is providing an overview of the main approaches in using big data: data focus vs. business analytics focus. The following topics will be covered:
- Why getting data should not be a starting point in business analytics, and why more data not always result in more accurate predictions
- The simulation analytics methodology in comparison to machine learning and data science approach
- Examples of two business cases:
(i) Healthcare: Pediatric Triage in a Severe Pandemic-Maximizing Population Survival by Establishing Admission Thresholds
(ii) Banking & Finance: Analysis of the staffing and utilization of a team of mutual fund analysts for timely producing ‘buy-sell’ reports
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Case Study 1 (Job Data)
Below is the structure of the table with the definition of each column that you must work on:
Table-1: job_data
job_id: unique identifier of jobs
actor_id: unique identifier of actor
event: decision/skip/transfer
language: language of the content
time_spent: time spent to review the job in seconds
org: organization of the actor
ds: date in the yyyy/mm/dd format. It is stored in the form of text and we use presto to run. no need for date function
Use the dataset attached in the Dataset section below the project images then answer the questions that follows
Number of jobs reviewed: Amount of jobs reviewed over time.
Your task: Calculate the number of jobs reviewed per hour per day for November 2020?
Throughput: It is the no. of events happening per second.
Your task: Let’s say the above metric is called throughput. Calculate 7 day rolling average of throughput? For throughput, do you prefer daily metric or 7-day rolling and why?
Percentage share of each language: Share of each language for different contents.
Your task: Calculate the percentage share of each language in the last 30 days?
Duplicate rows: Rows that have the same value present in them.
Your task: Let’s say you see some duplicate rows in the data. How will you display duplicates from the table?
Case Study 2 (Investigating metric spike)
The structure of the table with the definition of each column that you must work on is present in the project image
Table-1: users
This table includes one row per user, with descriptive information about that user’s account.
Table-2: events
This table includes one row per event, where an event is an action that a user has taken. These events include login events, messaging events, search events, events logged as users progress through a signup funnel, events around received emails.
Table-3: email_events
This table contains events specific to the sending of emails. It is similar in structure to the events table above.
Use the dataset attached in the Dataset section below the project images then answer the questions that follows
User Engagement: To measure the activeness of a user. Measuring if the user finds quality in a product/service.
Your task: Calculate the weekly user engagement?
User Growth: Amount of users growing over time for a product.
Your task: Calculate the user growth for product?
Weekly Retention: Users getting retained weekly after signing-up for a product.
Your task: Calculate the weekly retention of users-sign up cohort?
Weekly Engagement: To measure the activeness of a user. Measuring if the user finds quality in a product/service weekly.
Your task: Calculate the weekly engagement per device?
Email Engagement: Users engaging with the email service.
Your task: Calculate the email engagement metrics?
Default Prediction & Analysis on Lending Club Loan DataDeep Borkar
This document analyzes lending club loan data to predict loan defaults and calculate default probabilities using models like gradient boosting, neural networks, and logistic regression. The goal is to make informed decisions about future loans to assess profitability. Various machine learning models are trained and tested on the data, with gradient boosting achieving the best results. The loans are then segmented by default risk to analyze the net present value of the portfolio under various hypothetical default rates.
Prediction of customer propensity to churn - Telecom IndustryPranov Mishra
- A logistic regression model was found to best predict customer churn with the highest AUC and accuracy.
- The top variables increasing churn risk were credit class, handset price, average monthly calls, billing adjustments, household subscribers, call waiting ranges, and dropped/blocked calls.
- Cost and billing variables like charges and usage were significant, validating an independent survey.
- A lift chart showed targeting the highest risk 30% of customers could identify 33% of potential churners. The model allows prioritizing retention efforts on the 20% riskiest customers.
Data visualization is the graphical representation of information and data. It is used to communicate data or information clearly and effectively to readers by leveraging the human mind's receptiveness to visual information. Effective data visualization can improve transparency and communication, answer questions, discover trends, find patterns, see data in context, support calculations, and present or tell a story. Common tools for data visualization include charts, graphs, maps, and diagrams. Specialized roles involved in data visualization include data visualization experts, data analysts, business intelligence consultants, tool-specific consultants, business analysts, and data scientists.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
The purpose of this presentation is providing an overview of the main approaches in using big data: data focus vs. business analytics focus. The following topics will be covered:
- Why getting data should not be a starting point in business analytics, and why more data not always result in more accurate predictions
- The simulation analytics methodology in comparison to machine learning and data science approach
- Examples of two business cases:
(i) Healthcare: Pediatric Triage in a Severe Pandemic-Maximizing Population Survival by Establishing Admission Thresholds
(ii) Banking & Finance: Analysis of the staffing and utilization of a team of mutual fund analysts for timely producing ‘buy-sell’ reports
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Case Study 1 (Job Data)
Below is the structure of the table with the definition of each column that you must work on:
Table-1: job_data
job_id: unique identifier of jobs
actor_id: unique identifier of actor
event: decision/skip/transfer
language: language of the content
time_spent: time spent to review the job in seconds
org: organization of the actor
ds: date in the yyyy/mm/dd format. It is stored in the form of text and we use presto to run. no need for date function
Use the dataset attached in the Dataset section below the project images then answer the questions that follows
Number of jobs reviewed: Amount of jobs reviewed over time.
Your task: Calculate the number of jobs reviewed per hour per day for November 2020?
Throughput: It is the no. of events happening per second.
Your task: Let’s say the above metric is called throughput. Calculate 7 day rolling average of throughput? For throughput, do you prefer daily metric or 7-day rolling and why?
Percentage share of each language: Share of each language for different contents.
Your task: Calculate the percentage share of each language in the last 30 days?
Duplicate rows: Rows that have the same value present in them.
Your task: Let’s say you see some duplicate rows in the data. How will you display duplicates from the table?
Case Study 2 (Investigating metric spike)
The structure of the table with the definition of each column that you must work on is present in the project image
Table-1: users
This table includes one row per user, with descriptive information about that user’s account.
Table-2: events
This table includes one row per event, where an event is an action that a user has taken. These events include login events, messaging events, search events, events logged as users progress through a signup funnel, events around received emails.
Table-3: email_events
This table contains events specific to the sending of emails. It is similar in structure to the events table above.
Use the dataset attached in the Dataset section below the project images then answer the questions that follows
User Engagement: To measure the activeness of a user. Measuring if the user finds quality in a product/service.
Your task: Calculate the weekly user engagement?
User Growth: Amount of users growing over time for a product.
Your task: Calculate the user growth for product?
Weekly Retention: Users getting retained weekly after signing-up for a product.
Your task: Calculate the weekly retention of users-sign up cohort?
Weekly Engagement: To measure the activeness of a user. Measuring if the user finds quality in a product/service weekly.
Your task: Calculate the weekly engagement per device?
Email Engagement: Users engaging with the email service.
Your task: Calculate the email engagement metrics?
Default Prediction & Analysis on Lending Club Loan DataDeep Borkar
This document analyzes lending club loan data to predict loan defaults and calculate default probabilities using models like gradient boosting, neural networks, and logistic regression. The goal is to make informed decisions about future loans to assess profitability. Various machine learning models are trained and tested on the data, with gradient boosting achieving the best results. The loans are then segmented by default risk to analyze the net present value of the portfolio under various hypothetical default rates.
Prediction of customer propensity to churn - Telecom IndustryPranov Mishra
- A logistic regression model was found to best predict customer churn with the highest AUC and accuracy.
- The top variables increasing churn risk were credit class, handset price, average monthly calls, billing adjustments, household subscribers, call waiting ranges, and dropped/blocked calls.
- Cost and billing variables like charges and usage were significant, validating an independent survey.
- A lift chart showed targeting the highest risk 30% of customers could identify 33% of potential churners. The model allows prioritizing retention efforts on the 20% riskiest customers.
Data visualization is the graphical representation of information and data. It is used to communicate data or information clearly and effectively to readers by leveraging the human mind's receptiveness to visual information. Effective data visualization can improve transparency and communication, answer questions, discover trends, find patterns, see data in context, support calculations, and present or tell a story. Common tools for data visualization include charts, graphs, maps, and diagrams. Specialized roles involved in data visualization include data visualization experts, data analysts, business intelligence consultants, tool-specific consultants, business analysts, and data scientists.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
This document discusses analytics and its classifications and types. It defines analytics as a fact-based approach used for business planning that includes data validation, root cause analysis, and strategic prediction. Analytics in business is described as a continuous iterative process that investigates past performance to provide better insight for planning. The document outlines descriptive analytics methods like association analysis and clustering, as well as predictive analytics roles in generating theories, measures, and models. It also discusses the rapid growth of the analytics industry and its major domains like marketing, IT, and customer analytics. Finally, the document notes both views on the impact of analytics and differences between analytics and scientific approaches.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This document provides an introduction to predictive analytics. It defines analytics and predictive analytics, comparing their purposes and differences. Analytics uses past data to understand trends while predictive analytics anticipates the future. Business intelligence involves using data to support decision making and aims to provide historical, current and predictive views of business. As technologies advanced, business intelligence evolved from being organized under IT to potentially being aligned under strategy management. Effective communication between business and analytics professionals is important for organizations to benefit from predictive analytics. The business case for predictive analytics includes enabling strategic planning, competitive analysis, and improving business processes to work smarter.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...Simplilearn
This presentation about Decision Tree Tutorial will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
This document discusses data visualization. It begins by defining data visualization as conveying information through visual representations and reinforcing human cognition to gain knowledge about data. The document then outlines three main functions of visualization: to record information, analyze information, and communicate information to others. Finally, it discusses various frameworks, tools, and examples of inspiring data visualizations.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
1. F A Using S P S S1 (Saq.Sav) Q Ti AZoha Qureshi
The document describes conducting a factor analysis on SPSS to measure different aspects of student anxiety towards learning SPSS. A 23-item questionnaire was administered to over 2,500 students. Initial analysis of the correlation matrix found no issues with multicollinearity. The document then provides instructions for running the factor analysis in SPSS, including extracting factors, rotating the factors, and interpreting the output.
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
The document discusses the business view of IT applications. It outlines the core business functions of a typical enterprise, including sales, marketing, product development, and accounting. It also describes common business processes like "idea to offering" and "issue to resolution". The document introduces the Malcolm Baldrige framework for representing businesses and highlights the importance of measurement, analysis, and knowledge management. It then covers different types of IT applications, characteristics of Internet-ready applications, and how technology has evolved from 1965 to 2000 to the present. Finally, it discusses typical enterprise application architecture and information user requirements.
Neelkanth Drugs Pvt. Ltd (NDPL) is a leading pharmaceutical distributor in Delhi and Uttar Pradesh with 205 employees and 47 outlets. NDPL has first mover advantage with no major competition in a non-competitive environment allowing for further expansion. NDPL implemented information sharing and IT systems from 1998-2012 to increase operational efficiency from 25% to 100% and reduce manual processes and HR requirements. However, issues arose with cloud computing implementation including limited experience of the software provider and connectivity and availability problems. An ERP system would have been a better solution than cloud computing for NDPL's needs.
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
This document discusses creating data visualizations with low-cost tools. It begins by outlining the objectives of understanding the purpose of a visualization, principles of communicating through data, choosing the right visualization, and determining if Excel is suitable. It then covers the eight principles of communicating through data, such as defining the question, using accurate data, and tailoring the visualization to the audience. Next, it discusses choosing the right visualization type based on the purpose, such as line charts, bar charts or tables. The document considers when Excel may not be suitable and introduces specialist tools like Tableau, Microsoft Power BI, and coding options. It concludes with additional resources for data visualization.
Essential Excel for Business Analysts and ConsultantsAsen Gyczew
Excel is the most often used first-choice tool of every business analyst and consultant. Maybe it is not the most fancy or sophisticated one, yet it is universally understood by everybody especially your boss and your customers.
Excel is still pretty advanced tool with countless number of features and functions. I have mastered quite a lot of them during my studies and while working. After some time in consulting I discovered that most of them are not that useful; some of them bring more problems than solutions. On top of that there are features that we are taught at university that are not flexible and pretty time consuming. While working as a business analyst I developed my own set of tricks to deal with Excel I learned how to make my analyses idiot-proven and extremely universal.
I will NOT teach you the entire Excel as it is simply not efficient (and frankly you don’t need it). This course is organized around 80/20 rule and I want to teach you the most useful (from business analyst / consultant perspective) formulas as fast as possible. I want you also to acquire thanks to the course good habits in Excel that will save you loads of time.
If done properly, this course will transform you in 1 day into pretty good business analyst that knows how to use Excel in the smart way. It is based on my 12 years of experience as a consultant in top consulting companies and as a Board Member responsible for strategy, improvement and turn-arounds in biggest companies from FMCG, SMG, B2B sector that I worked for. On the basis of what you will find in this course I have trained over 100 business analysts who now are Investment Directors, Senior Analyst, Directors in Consulting Companies, Board Members etc.
I teach step by step on the basis of Excel files that will be attached to the course. To make the best out of the course you should follow my steps and repeat what I do with the data after every lecture. Don’t move to the next lecture if you have not done what I show in the lecture that you have gone through.
I assume that you know basic Excel so the basic features (i.e. how to write formula in Excel) are not explained in this course. I concentrate on intermediate and advanced solutions and purposefully get rid of some things that are advanced yet later become very inflexible and useless (i.e. naming the variables). At the end, I will show 4 full blown analyses in Excel that use the tricks that I show in the lectures.
To every lecture you will find attached (in additional resources) the Excel shown in the Lecture so as a part of this course you will also get a library of ready-made analyses that can, with certain modification, be applied by you in your work.
DEA is a technique that measures the efficiency of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. It defines an efficiency score for each DMU as a weighted sum of outputs divided by a weighted sum of inputs, with all scores restricted to a range of 0 to 1. DEA calculates efficiency scores by choosing input/output weights that maximize each DMU's score, presenting it in the best possible light relative to its peers. Strengths of DEA include its ability to handle multiple inputs/outputs without assuming a functional form and directly compare DMUs against peers or combinations of peers.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
This document discusses analytics and its classifications and types. It defines analytics as a fact-based approach used for business planning that includes data validation, root cause analysis, and strategic prediction. Analytics in business is described as a continuous iterative process that investigates past performance to provide better insight for planning. The document outlines descriptive analytics methods like association analysis and clustering, as well as predictive analytics roles in generating theories, measures, and models. It also discusses the rapid growth of the analytics industry and its major domains like marketing, IT, and customer analytics. Finally, the document notes both views on the impact of analytics and differences between analytics and scientific approaches.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This document provides an introduction to predictive analytics. It defines analytics and predictive analytics, comparing their purposes and differences. Analytics uses past data to understand trends while predictive analytics anticipates the future. Business intelligence involves using data to support decision making and aims to provide historical, current and predictive views of business. As technologies advanced, business intelligence evolved from being organized under IT to potentially being aligned under strategy management. Effective communication between business and analytics professionals is important for organizations to benefit from predictive analytics. The business case for predictive analytics includes enabling strategic planning, competitive analysis, and improving business processes to work smarter.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...Simplilearn
This presentation about Decision Tree Tutorial will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
This document discusses data visualization. It begins by defining data visualization as conveying information through visual representations and reinforcing human cognition to gain knowledge about data. The document then outlines three main functions of visualization: to record information, analyze information, and communicate information to others. Finally, it discusses various frameworks, tools, and examples of inspiring data visualizations.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
1. F A Using S P S S1 (Saq.Sav) Q Ti AZoha Qureshi
The document describes conducting a factor analysis on SPSS to measure different aspects of student anxiety towards learning SPSS. A 23-item questionnaire was administered to over 2,500 students. Initial analysis of the correlation matrix found no issues with multicollinearity. The document then provides instructions for running the factor analysis in SPSS, including extracting factors, rotating the factors, and interpreting the output.
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
The document discusses the business view of IT applications. It outlines the core business functions of a typical enterprise, including sales, marketing, product development, and accounting. It also describes common business processes like "idea to offering" and "issue to resolution". The document introduces the Malcolm Baldrige framework for representing businesses and highlights the importance of measurement, analysis, and knowledge management. It then covers different types of IT applications, characteristics of Internet-ready applications, and how technology has evolved from 1965 to 2000 to the present. Finally, it discusses typical enterprise application architecture and information user requirements.
Neelkanth Drugs Pvt. Ltd (NDPL) is a leading pharmaceutical distributor in Delhi and Uttar Pradesh with 205 employees and 47 outlets. NDPL has first mover advantage with no major competition in a non-competitive environment allowing for further expansion. NDPL implemented information sharing and IT systems from 1998-2012 to increase operational efficiency from 25% to 100% and reduce manual processes and HR requirements. However, issues arose with cloud computing implementation including limited experience of the software provider and connectivity and availability problems. An ERP system would have been a better solution than cloud computing for NDPL's needs.
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
This document discusses creating data visualizations with low-cost tools. It begins by outlining the objectives of understanding the purpose of a visualization, principles of communicating through data, choosing the right visualization, and determining if Excel is suitable. It then covers the eight principles of communicating through data, such as defining the question, using accurate data, and tailoring the visualization to the audience. Next, it discusses choosing the right visualization type based on the purpose, such as line charts, bar charts or tables. The document considers when Excel may not be suitable and introduces specialist tools like Tableau, Microsoft Power BI, and coding options. It concludes with additional resources for data visualization.
Essential Excel for Business Analysts and ConsultantsAsen Gyczew
Excel is the most often used first-choice tool of every business analyst and consultant. Maybe it is not the most fancy or sophisticated one, yet it is universally understood by everybody especially your boss and your customers.
Excel is still pretty advanced tool with countless number of features and functions. I have mastered quite a lot of them during my studies and while working. After some time in consulting I discovered that most of them are not that useful; some of them bring more problems than solutions. On top of that there are features that we are taught at university that are not flexible and pretty time consuming. While working as a business analyst I developed my own set of tricks to deal with Excel I learned how to make my analyses idiot-proven and extremely universal.
I will NOT teach you the entire Excel as it is simply not efficient (and frankly you don’t need it). This course is organized around 80/20 rule and I want to teach you the most useful (from business analyst / consultant perspective) formulas as fast as possible. I want you also to acquire thanks to the course good habits in Excel that will save you loads of time.
If done properly, this course will transform you in 1 day into pretty good business analyst that knows how to use Excel in the smart way. It is based on my 12 years of experience as a consultant in top consulting companies and as a Board Member responsible for strategy, improvement and turn-arounds in biggest companies from FMCG, SMG, B2B sector that I worked for. On the basis of what you will find in this course I have trained over 100 business analysts who now are Investment Directors, Senior Analyst, Directors in Consulting Companies, Board Members etc.
I teach step by step on the basis of Excel files that will be attached to the course. To make the best out of the course you should follow my steps and repeat what I do with the data after every lecture. Don’t move to the next lecture if you have not done what I show in the lecture that you have gone through.
I assume that you know basic Excel so the basic features (i.e. how to write formula in Excel) are not explained in this course. I concentrate on intermediate and advanced solutions and purposefully get rid of some things that are advanced yet later become very inflexible and useless (i.e. naming the variables). At the end, I will show 4 full blown analyses in Excel that use the tricks that I show in the lectures.
To every lecture you will find attached (in additional resources) the Excel shown in the Lecture so as a part of this course you will also get a library of ready-made analyses that can, with certain modification, be applied by you in your work.
DEA is a technique that measures the efficiency of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. It defines an efficiency score for each DMU as a weighted sum of outputs divided by a weighted sum of inputs, with all scores restricted to a range of 0 to 1. DEA calculates efficiency scores by choosing input/output weights that maximize each DMU's score, presenting it in the best possible light relative to its peers. Strengths of DEA include its ability to handle multiple inputs/outputs without assuming a functional form and directly compare DMUs against peers or combinations of peers.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
This document discusses how businesses can achieve analytics success by putting themselves on the path of data-driven decisions. It notes that frustrations with a lack of usable data had led organizations to their current state. The document outlines assessing an organization's current data capabilities and envisioning where more data and analytics are needed. It proposes developing an executable plan to guide organizations from their current state to success through tangible benefits within 90 days.
This document describes a study conducted at Froedtert Hospital to develop a predictive model of emergency department operations and the effect of patient length of stay on ED diversion. The study analyzed patient length of stay data, developed an ED simulation model, and used the model to test scenarios with different upper limits on length of stay. The model predicted that ED diversion could be reduced to around 0.5% by limiting discharged patients' length of stay to 5 hours and admitted patients' length of stay to 6 hours.
This document describes using process modeling simulation to analyze the effect of daily leveling of elective surgeries on ICU diversion rates at a hospital. The simulation models the patient flow through different units like the ICU, OR, and ED. Currently, elective surgeries are scheduled without considering ICU capacity, leading to periods of high utilization and ICU diversion. The simulation analyzes scenarios where elective case limits are set each day, smoothing out utilization across days and reducing ICU diversion times. Initial results show imposing daily caps of 5 cases for one unit and 4 for another reduces scheduling variability by around 20-28% compared to the current practice.
The document discusses the changing face of business intelligence (BI) and a proposed BI strategy. It outlines key BI pain points such as lack of standardized data definitions and metrics. It proposes a BI vision of trusted data delivered effectively to create an information-driven vs. data-driven organization. The strategy would automate BI delivery using a single platform to enable self-service BI and address issues like data quality, delivery timeliness, and mobility.
The document discusses the value drivers for data including volume, velocity, variety, and veracity. It explains that Omnitracs generates a variety of data from 500+ million daily transactions including GPS, video, and text. The "4Vs" help define a company's data analytics approach. The presentation focuses on using data's value to drive a strategy and prioritizing "smart" predictive analytics using customized models over industry models to create more value from big, fast, and smart data approaches. Questions were asked from panelists at Omnitracs.
The document discusses data analytics from the perspectives of different stakeholders and outlines some of the challenges that can arise from a lack of alignment. It notes that when vendors, technology teams, business analytics, and executive leaders do not share a unified vision of data analytics, it can lead to issues like unhealthy competition, redundancy, and hindered collaboration. The document emphasizes the importance of good management of data analytics to impact business goals and establish consistent performance metrics across teams.
The document summarizes churn rates and customer cancellation reasons for a subscription-based company. It finds that 67% of cancellations can potentially be addressed, with the largest reasons being insufficient value proposition, sales/marketing practices, and product confusion. Implementing strategies to reduce churn by 30% could increase revenue by $X million annually. Various projects are outlined to analyze churn drivers and test retention strategies with the goal of optimizing churn rates.
The document discusses various quality tools and statistical methods that can be used in investigations. It begins by defining what an investigation is and the main purposes, which are to find the root cause of issues and enhance understanding. A number of investigative tools are then presented, including flowcharts, brainstorming, cause-and-effect diagrams, boxplots, Pareto charts, and hypothesis testing. Examples are provided for each tool to illustrate how it can be applied to gather and analyze data during an investigation.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
The document discusses using discrete event simulation (DES) to analyze capacity and plan renovations for a hospital's surgical suite. It provides an example where DES was used to simulate different scenarios for renovating the Children's Hospital of Wisconsin's surgical facilities. The simulation analyzed patient wait times and resource needs under each scenario. The output recommended scenario 3 and reallocating beds to meet performance criteria for wait times.
Ensuring the feasibility of a $31 million OR expansion project: Capacity plan...SIMUL8 Corporation
Ensuring the feasibility of a $31 million OR expansion project: Capacity planning, system design, and patient flow
Presenter: Todd Roberts, Memorial Health System
The second workshop in our series will look at a recent project at Memorial Health System (MHS) in Illinois.
Todd Roberts, System Director of Operations Improvement at MHS will discuss and demonstrate the use of discrete simulation modeling to analyze floor design and throughput for a new Rapid Clinical Examination provider model for a 70,000 annual visit, Level I trauma center emergency department at a 507 bed, tertiary, urban, academic medical center and flow for all aspects of architectural design proposal for $31 million dollar operating room expansion project, including pre-op admission, transport to OR, OR time, and post-anesthesia care units (PACU) for admitted and outpatient surgery.
Through the use of discrete simulation modeling, Memorial has reduced length to stay for non-admitted patients in the emergency department by 27%, reduced percentage of patients leaving by without treatment by 50%, and released admit hold time by 37% while improving patient satisfaction from the 57th to 99th percentile (Press Ganey).
In addition, Memorial has used simulation to determine the appropriate facilities layout for its new OR expansion project, determining that optimizing the flow of traffic will lead to a reduction of 30 minutes per case in wasted movement and waiting.
The document discusses Lean Six Sigma and how it applies in healthcare. It provides an overview of Lean Six Sigma, including definitions of Lean and Six Sigma. It then gives examples of Lean Six Sigma projects at St. Elizabeth Regional Health, such as reducing door-to-balloon time for heart attack patients and improving operating room turnover times. The presentation aims to show how Lean Six Sigma principles can help healthcare organizations improve quality, safety, efficiency and patient satisfaction.
Healthcare Delivery Reimagined: Patient Flow and Care Coordination AnalyticsAdrish Sannyasi
Come to learn how Splunk’s data analytics platform could be utilized to solve many high impact business problems in healthcare delivery systems to reduce cost, improve patient outcome and safety, and enhance care coordination experience. Analyze observed behavior from healthcare event data and metadata to discover patterns, monitor compliance, and optimize the workflow. Furthermore 80% of healthcare data is unstructured (clinical free text and documentation), or semi-structured and many new data sources are such as tele health, mobile health, sensors, and devices are getting integrated in many healthcare systems specifically in the area of chronic disease management. So, one need analytics software that can harvest, interpret, enrich, normalize, and model diverse structured and unstructured data and analytics approaches that embrace the “data turmoil” by relying less on standardized data items and more on the capability to process data in any format.
Flow queue analysis co4.pptx business process management21120061
This document discusses quantitative process analysis techniques including flow analysis, queuing analysis, and simulation. Flow analysis is used to calculate processing times, cycle times, and other time-related metrics by analyzing the sequence and probabilities of activities in a process model. Queuing analysis uses concepts from queuing theory to analyze waiting times and resource utilization, especially for service systems. It models systems as queues (M/M/c) to determine metrics like average queue length and time in system. Simulation is introduced as a technique that can address the limitations of flow and queuing analysis when modeling more complex, multi-stage processes.
This project report describes the development of a Hospital Management System. The system allows hospitals to automate processes like maintaining patient records, generating prescriptions and bills, and providing test reports. It includes functionality for indoor and outdoor patients. The system aims to improve organization, accuracy, reliability and immediate retrieval/storage of information compared to a manual system. It was developed using VB 6.0 with an MS Access backend database.
The document discusses using discrete event simulation to model patient flow and reduce wait times in a surgery clinic. It describes:
1) Modeling current clinic processes and resource allocation using business process modeling notation to identify bottlenecks.
2) Collecting time data from patient records and fitting distributions to activity processing times.
3) Simulating clinic operations using actual schedules and time distributions to analyze wait times.
4) Applying a shifting bottleneck heuristic to generate improved schedules, reducing surgical wait times by up to 88.9% in simulations.
Speaker Presentation from U.S. News Healthcare of Tomorrow leadership summit, Nov. 1-3, 2017 in Washington, DC. Find out more about this forum at www.usnewshot.com.
This is my College Project Documentation on Hospital Management System. Which includes mainly Problem Definition, Existing System, Proposed System, Requirement Analysis, Scope of the System, Feasibility Study, Hardware & Software Requirement, ER Diagram, DFD Diagram, Data Dictionary for Project, Sample Output Screenshots, Conclusion
This project report describes a Hospital Management System (HMS) that was developed to automate operations at hospitals. The HMS allows for maintaining patient records, storing diagnosis and test information, providing test facilities for doctors, and generating bills. It has two user levels - administrator and user. The report provides an introduction to the system, describes overall goals and requirements, presents data flow diagrams and database tables, and includes screenshots of the system interface. It concludes that the HMS aims to efficiently digitize hospital workflows and storage of patient information.
This exhaustive and vibrant PowerPoint has around 90 slides and explains in detail all the must know concepts of Management in Healthcare. These slides have enough information to use it for 3 hour seminar (2 sessions) on Modern Management Techniques and its application in Healthcare. The session can be further extended if the concepts are explained with appropriate examples.
The Hospital is an Institute which provides to people best health services.
That Provides Facility for hospitalization.
The patient is admitted in hospital with the exception that he or she will be in the
hospital for more Than 24 hours.
The Patient is assigned a room /bed.
The Hospital Provide Medical care.
The document summarizes a project to develop a queue management system called QMRAS to improve the experience of students requesting assistance from teaching assistants in computer labs. The system aims to 1) enhance the student experience when requesting help and 2) improve efficiency in allocating teaching resources. User testing found the system accurately estimated wait times for 85% of users and streamlined the help request process. While students found it helpful, teaching staff found refreshing the page frustrating. Overall, the project was successful in meeting its goals of enhancing the student experience and improving resource allocation efficiency.
Sharing a New Ideal: How Tomorrow’s Understaffed, Multi-Site Lab Organization...mhartman1309
This presentation was presented by Chris Christopher at the Lab Quality Confab Conference on Nov 2, 2010. It shows how medical laboratories are using automation, technology and lean sigma improvement methodologies to meet organizational needs.
090528 Miller Process Forensics Talk @ Asqrwmill9716
Talk presented to local ASQ chapter. It dealt with process improvement: continuous measurement system validation and utilizing capability metrics for process forensics. Further, a program was introduced that\'s been used to optimize spare parts inventory based on a resampling approach to historical data.
This document summarizes the implementation of a demand flow system at Mercy Hospital to improve their supply chain operations. Key outcomes of the new system included reducing annual medical supply expenses by $1 million, clinical hours spent on supply chain by 28,000 hours, and warehouse space by 50%. Staff satisfaction also increased to 91% and the fill rate for supplies hospital-wide improved to 97% from 82%. The new visual system unified operations and removed silos across departments.
Industrial Engineering is concerned with designing integrated systems involving people, materials, equipment and energy. Some significant events in its development include the division of labor, standardized parts, scientific management, the assembly line, and quality control methods. Productivity is a measure of output over input, with higher productivity indicating more output is generated from the same level of inputs. Factors like technology, capacity utilization, and training can affect productivity levels in an organization.
From an operational perspective, yield management is most effective under whi...johann11371
FOR MORE CLASSES VISIT
www.ops571help.com
1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
The document discusses Tom Brimeyer's Hypothyroidism Revolution program, which is a comprehensive guide for reversing hypothyroidism naturally and permanently in three phases. The first phase focuses on eliminating food sensitivities and toxins. The second phase introduces a thyroid-supporting diet. The third phase incorporates a healthy lifestyle including special exercises. The program contains over 160 pages explaining the three phases in detail. It aims to help sufferers of hypothyroidism achieve optimal health through natural means.
Similar to Data Analytics for Real-World Business Problems (20)
This document provides an outline and overview of a course on healthcare administration and delivery systems. It discusses the following key points:
- The course will introduce quantitative decision-making methods in healthcare management and apply techniques like forecasting, optimization, and simulation to address challenges in the healthcare system.
- Traditional management has relied on intuition but incorporating quantitative methods can help address problems in a systematic way.
- The roles and responsibilities of healthcare managers have become more visible and important given issues around costs, access, and quality in the system.
- A background in both healthcare and business administration is valuable for medical and health services managers.
This document provides details about a graduate course on healthcare administration and delivery systems, including its objectives, topics, assignments, and evaluation criteria. The course uses lectures, discussions, and exercises to teach students how to apply quantitative techniques like forecasting, optimization, simulation, and analytics to decision-making in healthcare. The goal is to help students develop skills in using data-driven methods for planning, managing, and evaluating healthcare programs and organizations. The course meets weekly and includes a midterm and final exam that evaluate students' problem-solving abilities and understanding of operational challenges in healthcare settings.
This document discusses various frameworks for optimizing healthcare staffing levels with variable patient demand. It begins by outlining different approaches including the newsvendor framework, linear optimization, and discrete event simulation. The newsvendor framework is then explained in more detail, showing how to calculate optimal staffing levels by balancing the costs of over- and under-staffing based on historical demand data. Key points are that the optimal level may be higher or lower than the average depending on costs, and it provides a trade-off between having too many or too few nurses on staff at a given time.
The document discusses data science, data analytics, and their application in hospital operations management. It states that data science and analytics strive to transform raw data into actionable business decisions using quantitative methods. Various types of analytics are described like descriptive, predictive, and prescriptive analytics. Examples of applying different analytical methods to common business problems in healthcare are provided, such as using simulation for capacity planning and optimization for resource allocation. The key is integrating analytics into decision-making processes to create value for customers.
Primary care clinics-managing physician patient panelsAlexander Kolker
OUTLINE
• Traditional scheduling and the advanced
access at a primary care clinic
• Uncertainties that should be considered when
patients are scheduled
• Decisions that need to be made for designing an
appointment system
• Practice on using the panel size calculator
•Emerging Trends in Primary Care:
Staffing with variable demand in healthcare settingsAlexander Kolker
Outline
Main Concept and Some Definitions.
The “newsvendor” framework approach.
Staffing a nursing unit with variable census (demand)
Linear optimization framework approach.
Minimizing staffing cost subject to variable constraints
Discrete event simulation framework approach.
Staffing a unit with cross-trained staff
Key Points and Conclusions
Staffing Decision-Making Using Simulation ModelingAlexander Kolker
The use of Management Engineering methodology for
staffing decision-making.
• Part 1 - Quality and Cost: Outpatient Flu Clinic.
• Part 2 - Quality and Cost : Optimal PACU Nursing
Staffing.
• Summary of Fundamental Management Engineering
This document discusses using management engineering principles to analyze healthcare delivery systems. It provides an example analysis of a hospital system modeled as interdependent subsystems, including the emergency department, intensive care unit, operating rooms, and nursing units. Simulation of the mathematical model revealed important relationships between the subsystems that could inform management decisions. The conclusion advocates using objective data analysis and simulation rather than subjective opinions alone for healthcare management decisions.
Effect Of Interdependency On Hospital Wide Patient FlowAlexander Kolker
This document discusses using simulation modeling to analyze the impact of interdependencies between key departments in a hospital system, including the emergency department (ED), intensive care unit (ICU), operating rooms (OR), and nursing units. It summarizes how modeling each department individually can identify factors influencing performance, such as patient length of stay in the ED and scheduling of elective surgeries in the ICU. The document also provides examples of operational performance criteria used to evaluate the OR and potential simulation models analyzing the impact of changes like adding OR capacity.
1) The Child Protection Center (CPC) evaluated children who may have been abused and aimed to reduce patient wait times which were perceived to be due to staff shortages.
2) A discrete event simulation model was developed to analyze current patient flow and identify bottlenecks. It found the sexual abuse exam room and medical assistants were causing most delays.
3) The best scenario found was adding 0.6 full-time equivalent medical assistant in the afternoon and changing the exam room configuration to one exam room and two sexual abuse exam rooms. This significantly reduced total patient wait times.
SHS_ASQ 2010 Conference: Poster The Use of Simulation for Surgical Expansion ...Alexander Kolker
Children's Hospital of Wisconsin is planning a major expansion and renovation of its surgical suite to increase capacity. Computer simulation models were developed to analyze three expansion scenarios and determine the optimal design. Model 3 was selected as the best option, as it would separate gastroenterology and pulmonary services into their own area with 2-3 procedure rooms and 8-11 pre/postoperative beds, while meeting all performance criteria for patient wait times and OR utilization through 2013. The simulations accounted for patient volume flow, limited system capacity, and the balance needed between these factors for efficient patient throughput.
SHS ASQ 2010 Conference Presentation: Hospital System Patient FlowAlexander Kolker
The document discusses using systems engineering principles to improve healthcare delivery. It describes modeling a hospital as interconnected subsystems like the emergency department, intensive care unit, operating rooms, and medical units. The emergency department is analyzed in depth as a case study. A simulation model of patient flow through the emergency department is created to predict how limiting patient length of stay would reduce times when the emergency department must be closed to new patients due to capacity issues. The document advocates applying mathematical modeling and analysis to make more informed management decisions compared to traditional intuitive approaches.
Advanced Process Simulation Methodology To Plan Facility RenovationAlexander Kolker
This document summarizes a case study on using simulation modeling to plan for a surgical suite renovation at Children's Hospital of Wisconsin. The hospital needed to increase surgical capacity to meet growing demand. A project team used simulation to evaluate options for allocating operating rooms and beds across services. Their model found that separating gastroenterology and pulmonary services into their own area with 2-3 procedure rooms and 8-11 beds would best meet goals of minimizing wait times while staying within budget. The renovation is projected to increase patient satisfaction and yield a positive return on investment within 15 years. Ongoing simulation will evaluate the new process over time.
Here is a high-level layout of the PACU simulation model:
- Inputs:
- Historical daily OR schedule with planned start/end times of surgeries
- Distributions of surgery durations
- Distributions of PACU length of stay for different surgery types
- Process:
- Simulate surgeries based on schedule and duration distributions
- Patients enter PACU after surgery based on OR schedule
- Patients spend time in PACU based on PACU length of stay distributions
- Patients discharge from PACU over time
- Outputs:
- PACU census (number of patients) tracked over time
- Staffing requirements calculated to maintain target nurse-to-patient ratios
The model simulates patient flows
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Challenges of Nation Building-1.pptx with more important
Data Analytics for Real-World Business Problems
1. DATA ANALYTICS FOR SOLVING BUSINESS
PROBLEMS:
SHIFTING FOCUS FROM THE TECHNOLOGY
DEPLOYMENT TO THE ANALYTICS METHODOLOGY
Alexander Kolker, PhD
March 7, 2017
Alexander Kolker. All rights reserved 1
2. Alexander Kolker. All rights reserved 2
"Making better business decisions using data“
a co-hosted event with Accelerate Madison and Big
Data Madison Meetup
Date: February 13, 2017
Time: 5:30 PM - 7:30 PM CST
Key point:
Focusing on business outcomes rather than on data and
technology per se is getting momentum …
3. Some professional highlights…
• 4 business consulting projects: US Bank, Boston Consulting Group,
Children’s Hospital of Wisconsin, Ohio Hospital Association
• 12 years at GE (General Electric) Healthcare: Data Scientist
• 3 years at Froedtert Hospital: Process Simulation Leader
• 5 years at Children’s Hospital of Wisconsin: Simulation and Data
Analytics
• UW-Milwaukee Lubar School of Business-Adjunct Faculty: A graduate
course Healthcare Delivery Systems-Data Analytics
• Lead Editor and Author of 2 books, 8 book chapters, 10 reviewed papers,
18 Conference presentation in the area of operations management,
process modeling and simulation, business analyticsAlexander Kolker. All rights reserved
4. BIG DATA AND ANALYTICS
BACKGROUND
Alexander Kolker. All rights reserved 4
5. A bold statement to start with:
Big data without actionable analytics and business
decision-making is a ‘sleeping giant’
Big Data is a 2-part deal
1. Technology for storing and managing large
amounts of data of various nature- the current
trend
2. Methodology for helping business decision-
making using modeling and data.
This is called Analytics, it is getting momentum…
Alexander Kolker. All rights reserved 5
This presentation focus
6. Key points:
• Analytics must help in developing:
New products
Operational efficiency
Business Decision support
Alexander Kolker. All rights reserved 6
$$$
7. 7
WHAT WILL BE COVERED NEXT…
1. The concept of simulation analytics for studying systemic
complex business problems
Use case 1:
Analysis of the optimal staffing of a team of medical providers
using simulation methodology (with a live demonstration)
2. Analytics methodology for identifying a few contributing
variables to the organization’s financial outcome:
Use case 2:
Principal components decomposition of the large
observational dataset and regression with principal
components
3. Appendix: Food for thought… from Pierre Laplace, 1795
Alexander Kolker. All rights reserved
8. Alexander Kolker. All rights reserved 8
SIMULATE!
• In general, simulation is a process of studying complex
systems using their mathematical representation called a
model or a digital twin, e.g.
• Flight simulator-the aircraft response to the cockpit input
controls
• Nuclear plant operators simulators-reactor output
response to the various operator inputs
• Surgical and physiology procedures simulators on
mannequins
•Our focus here is simulation of business operations
9. Alexander Kolker. All rights reserved 9
Key Point:
The most powerful and versatile simulation methodology for
analyzing manufacturing, finance, healthcare, military and
other business operations is Discrete Event Simulation
Taken from a LinkedIn post on Data Science Central
10. Alexander Kolker. All rights reserved 10
Discrete Event Simulation (DES) Methodology.
What is it?
•A discrete event simulation (DES) model mimics a
system’s dynamic behavior as the system transitions
from state to state
(compare to Data Science approach: map an output to the input through
a black box model or algorithm)
11. Alexander Kolker. All rights reserved 11
The validated model is used for predicting various
scenarios of the future system’s responses to the
random inputs in a virtual reality
Key points:
•The simulation model is not a ‘black box’. It is a scalable
digital twin of the reality
•The model reflects what’s actually happening in the system
• This capability gives a sense of the expected system’s
output before incurring the cost and risk of the
business solution implementation
(compare to Data Science validation and cross-validation of a ‘black box’ model for
predicting the future outcomes…)
12. Alexander Kolker. All rights reserved 12
Use case 1
Analysis of the performance and the
optimal staffing
in an Endoscopy Unit using
Discrete Event Simulation
Presented at the:
5-th International Conference on Healthcare Systems, October, 2008;
and
IEEE Workshop on HealthCare Modeling and Simulation, February 18-20, 2010,
Venice, Italy
13. Problem Description
• The inevitable variability of the admission, recovery and
procedure time due to unforeseen medical complications
and delays result in some unit performance issues:
a long patient wait time to schedule procedures
not meeting daily patient demand for procedures
underutilization of the available capacity and staff
overtime
dissatisfaction of patients and medical staff
There has also been a lower than anticipated revenue
stream
14. The objectives of this work were:
(i) to analyze the main factors that contribute to the
inefficient patient flow and process bottlenecks,
and
(ii) to propose a more efficient patient scheduling and
staffing allocation aimed at increasing the number of
served patients, reducing procedure delays, and staff
overtime
Business Problem - Project Goal
15. The Endoscopy Unit High Level Process
Patients arrive at the
admission area
Patients are seen by the
admission nurse
Patients are attended by the
procedure nurse
Assigned doctors perform
procedures
Patients move to the
recovery area where they
are attended by the
recovery nurse
18. High Level Model Outline
• Admission, procedure and the patient recovery
duration are random variables
• These variables are represented as the best fit
statistical distributions built into the simulation model
• Each patient is assigned his/her attributes:
scheduled arrival time
procedure type
assigned doctor’s name
20. What happens in the Exam Rooms?
if Proc_Type=col AND Doc_name=Bajaj AND Wk_Day=Fri Then
{
jointlyget (RN_WF and Tech_TF and D_Bajaj) OR (2 RN_WF and D_Bajaj)
Time (T(30,40,40) min)
Free all
}
else
if Proc_Type=egd AND Doc_name=Bajaj AND Wk_Day=Fri Then
{
jointlyget (RN_WF and Tech_TF and D_Bajaj) OR (2 RN_WF and D_Bajaj)
Time (T(10,20,20) min)
Free all
}
else
if Proc_Type=ERCP AND Doc_name=Dua AND Wk_Day=Fri Then
{
jointlyget (RN_WF and Tech_TF and D_Dua) OR (2 RN_WF and D_Dua)
Time (T(70,80,80) min)
Free all
}
Key Point:
Capturing multiple resources with different time distributions for different
procedures requires some coding…
21. Typical Input Data Format
Annual patient volume is ~10,000 patients
Alexander Kolker. All rights reserved 21
Key
Source
Destination
Nam
e
Action
Logic
W
eek
W
eekday
Tim
e
Quan
10 patient Late_Pt_arrival_adjustment_
Proc_Type=col
Doc_name=bajaj
Wk_day=Mon
1 Mon 7:00 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=egd
Doc_name=massey
Wk_day=Mon
1 Mon 7:00 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=col
Doc_name=johnson
Wk_day=Mon
1 Mon 7:00 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=egd
Doc_name=massey
Wk_day=Mon
1 Mon 7:20 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=col
Doc_name=bajaj
Wk_day=Mon
1 Mon 7:40 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=col
Doc_name=johnson
Wk_day=Mon
1 Mon 7:40 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=col
Doc_name=massey
Wk_day=Mon
1 Mon 7:40 AM 1
10 patient Late_Pt_arrival_adjustment_
Proc_Type=egd
Doc_name=bajaj
Wk_day=Mon
1 Mon 8:20 AM 1
22. Alexander Kolker. All rights reserved 22
Typical information (data) usually required to populate a
DES model:
• Arrival pattern and quantities: periodic, random,
scheduled, daily pattern, etc.
• The time that the entities spend in the activities, i.e.
service time.
This is usually not a fixed time but a statistical distribution.
• Capacity of each activity, i.e. the max number of entities
that can be processed concurrently in the activity
• Routing types that connect structural elements: %,
conditional, alternate, create, renege, etc.
•Resource assignments: quantity and scheduled shifts
23. Live simulation demonstration is included
here: call simulation ProcessModel
patient arrivals, shifts for nurses,
technicians and doctors, stat-fit distribution
24. Some Simulation Scenarios
Scenario 1- The Original Model –Baseline-used for model
validation and testing
Scenario 2 - One additional doctor scheduled part time for
11 hours per week
Scenario 3 - Change in the patient arrival schedule with
10% reduction in inter-arrival time with one additional
doctor
Scenario 4 - Cross-training of the admission and recovery
nurses
Scenario 5 - Adding a part-time nurse
Scenario 6 - Adding a part-time scope-cleaning tech
Scenario 7 – ladder nurse shifts, change breaks and lunch
time
Scenario 8 – combined Scenarios 2, 3 and 4, and all together
25. Simulation outcome example:
Scenario 1 vs. Scenario 2+Scenario 4 (additional part-time
doctor for 11 hours/week + cross-trained nurses):
39
34
29
35
23
44
40
30
40
23
0
5
10
15
20
25
30
35
40
45
50
Monday Tuesday Wednesday Thursday Friday
Days of the Week
NumberofPatients
Scenario I Scenario II
Weekly Total
Scenario I 160
Scenario II 177
The number of patients
increase: 17
Overtime, hours
Scenario I 28.2
Scenario II 20.9
Reduced doctors’
overtime: 7.3 hrs
26. Financial Cost-Benefit Estimate
Typical average colonoscopy patient charge is about $2,500
(Colonoscopy is a major GI procedure)
Nurse overtime rate is 1.5 times of the regular pay (about $30/hr)
Typical GI doctor’s annual pay is about $360,000, i.e. ~$360 / hr
Weekly revenue from additional 17 patients is 17 *$2,500 = $42,500
Reduced overtime cost for nurses and doctors is
7.3 hrs*($30*1.5+$360)= $2956
Cost of additional doctor (working 11 hrs): $360*11= $3960
Additional revenue that the additional doctor brings in is about
$42,500 + $2956 - $3960 = $41,496 per week
27. 27
Concluding Key Points:
So how can you tell if simulation is right for you?
• This is methodology of choice for analyzing the dynamic behavior
of the complex systems/processes with random components
• There is a big decision to make with high potential for failure or
reward
• Provides a framework for experimenting with the system
and testing various business scenarios
• Reveals unintended consequences of business solutions
• Commitment to use the findings and recommendations, even if
they are not what you want to hearAlexander Kolker. All rights reserved
28. Use case 2
Analytics methodology for identifying a few
contributing variables to the organization’s financial
outcome:
Principal components decomposition of the large
observational dataset and regression with Principal
components
Reference:
A. Kolker. Management Engineering for Effective Healthcare Delivery: Principles and
Applications, IGI-Global, 2011, Chapter 1.
A. Kolker. Healthcare Management Engineering. What Does this Fancy Term Really
Mean? Chapter 5. Springer-Briefs in Healthcare Management & Economics, NY, 2012
Alexander Kolker. All rights reserved 28
29. • The large local hospital plans a major market share
expansion to improve its long-term financial viability
Alexander Kolker. All rights reserved 29
Business Problem - Project Goal
• The management wants to know what population
demographic factors and population disease prevalence
specific to the local area zip codes are the most important
contributors to financial contribution margin (CM $)?
Note: Contribution margin is defined as the difference between all
payments collected from patients and the patient variable costs.
30. Plan of the problem attack
Alexander Kolker. All rights reserved 30
• Step 1
Demographics data matrix (total 38 variables) to be analyzed
for the top 10 ZIPs using Principal Component decomposition.
• Step 2
Regression analysis to be performed that relates $ CM and
principal components of the original data matrix.
• Step 3
By analyzing eigenvectors for only statistically significant principal
components, conclusions to be made which demographic variables
are the biggest contributors for the top 10 ZIPs
31. Alexander Kolker. All rights reserved 31
Description of Data
A set of population demographic data was collected for
local area zip codes and the corresponding median
contribution margin for each zip code (CM $).
The following groups of demographic variables and
disease prevalence data were collected for each zip
code as percentage of the total zip code population:
32. Alexander Kolker. All rights reserved 32
• 4 Age categories:
18-34
35-54
55-64
65+
• 4 Educational categories:
BS/BA degree and higher,
Associate/Professional degree,
high school diploma,
no high school diploma
33. Alexander Kolker. All rights reserved 33
• 4 Income categories:
less than $50K
$50 - $75K
$75K - 100K
$100K +
• 5 occupational categories:
Healthcare, Labor,
Professional/Administrative,
Public Service,
Service industry
• Gender: male, female
• 5 Race categories: African American, Native American, Asian,
White, Other
34. Alexander Kolker. All rights reserved 34
• 14 disease categories:
BMT
Medical Oncology
Surgical Oncology
Cardiology
Cardiothoracic surgical
Vascular surgical, Digestive
Medicine/Primary care
Musculoskeletal
Neurology
Transplant
Trauma, Unassigned
Women Health
• There are total 38 data variables included in the data base.
35. Alexander Kolker. All rights reserved 35
Issues with direct use of data for regression:
• In large observational data sets with the dozens variables
some of them are inevitably correlated
• Correlation means that some information is redundant
• This redundant information in the data makes it difficult
to attribute the contributions of each variable to the
output
This issue is called Multicollinearity!!
36. Alexander Kolker. All rights reserved 36
Illustration of some pairwise correlation:
Correlation coefficient of the variables
'No high school’ and ‘Annual income less $50K’: 0.93
vs.
Correlation coefficient of the variables
‘Professional Degree’ and ‘Annual income less $50K’: - 0.87
37. Alexander Kolker. All rights reserved 37
Illustration of the regression disaster with all original
data (38 variables)
CM $ =4130333+41195*18-24 years–39029*25-34 years+
11836*35-44years+2894*45-54 years+5507*55-59 years+
209919*60-64 years-142258*65-74 years+53373*75 years+ -
2665632*AD–2662185*BD-2620383*PhD- 2649374*HS - 2648440
Less HS - 2687756 MD - 2717506 ProD- 2665190 Some Coll -
2692213 Some HS - 2398380 Less $15K- 2386133 $15K to $25K
- 2493006 $25K to $35K - 2413833 $35K to $50K- 2398657
$50K to $75K - 2455023 $75K to $100K - 2434483 $100K to
$150K- 2404935*$150K to $250K - 2414342 $250K to $500K -
2393024 $500K+ 947225 Health Care + 954055 Labor + 966787
Professional/Administrative+ 954355 Public Service +
960649* Service Industry+………..
Regression diagnostics:
R-Sq = 67.1% R-Sq(adj) = 8.6%
Huge variances inflation factors VIF:
38. Alexander Kolker. All rights reserved 38
Predictor Coef SE Coef T P VIF
Constant 4130333 4378828 0.94 0.358
18--24 years 41195 32885 1.25 0.226 13.820
25--34 years -39029 24759 -1.58 0.132 23.274
35--44 years 11836 30294 0.39 0.701 9.458
45--54 years 2894 44603 0.06 0.949 25.180
55--59 years 5507 162937 0.03 0.973 89.682
60--64 years 209919 157301 1.33 0.199 65.101
65--74 years -142258 66336 -2.14 0.046 43.529
75 years+ 53373 36529 1.46 0.161 26.059
AD -2665632 3334182 -0.80 0.434 90827.662
BD -2662185 3342475 -0.80 0.436 2400778.419
PhD -2620383 3375609 -0.78 0.448 20953.952
HS -2649374 3333923 -0.79 0.437 1711185.583
Less HS -2648440 3329576 -0.80 0.437 575442.669
MD -2687756 3321036 -0.81 0.429 389134.963
ProD -2717506 3320805 -0.82 0.424 161574.141
Some Coll -2665190 3325834 -0.80 0.433 256129.161
Some HS -2692213 3334397 -0.81 0.430 1402053.683
Less $15K -2398380 2972893 -0.81 0.430 1398310.925
$15K to $25K -2386133 2983525 -0.80 0.434 429011.942
$25K to $35K -2493006 2994782 -0.83 0.416 281665.965
$35K to $50K -2413833 2973178 -0.81 0.427 253783.866
$50K to $75K -2398657 2980453 -0.80 0.431 371553.358
$75K to $100K -2455023 2994758 -0.82 0.423 541397.221
$100K to $150K -2434483 2980581 -0.82 0.425 953779.541
$150K to $250K -2404935 2982679 -0.81 0.431 330537.600
$250K to $500K -2414342 2994755 -0.81 0.431 71152.055
$500K+ -2393024 2989787 -0.80 0.434 36401.343
Health Care 947225 1810961 0.52 0.607 32674.125
Labor 954055 1801535 0.53 0.603 727911.597
Professional/Administrative 966787 1801311 0.54 0.598 501480.184
Public Service 954355 1807843 0.53 0.604 42387.891
Service Industry 960649 1803238 0.53 0.601 19069.682
VIF=1/(1-corr^2)
Corr is the
multiple
correlation of the
variable with the
remaining
independent
variables
39. Alexander Kolker. All rights reserved 39
• Paired correlation analysis for all 38 variables (703
pairs!!) is impractical.
• Knowing paired linear correlation coefficient does not
help in reducing redundant information and extracting
meaningful information for separate contributing
factors.
• Regression analysis with dozens of the original
variables from observational data sets usually
fails.
Key Points:
40. Alexander Kolker. All rights reserved 40
• It allows removing the redundant
variables that carry little or no information
while retaining only a few mutually
uncorrelated principal variables.
Why Principal components
decomposition?
41. The main idea of PCD
Alexander Kolker. All rights reserved 41
The purpose of PCD is determining r new variables
PCr that can best approximate variation in the p
original X variables as linear combinations
42. The principle of information
conservation
Alexander Kolker. All rights reserved 42
• The total amount of information in the original data
set is not changed because of its PC decomposition
• Rather, it is rearranged in the form of a few linear
combinations of the original variables as main
information holders (PCs)
• This significantly reduces the number of
independent variables but retain the same amount
of information that is contained in the original data
matrix
43. What’s the eigen value?
Alexander Kolker. All rights reserved 43
• The eigen value λj is a measure of how much
information is retained by the corresponding PC.
• A large value of λj (compared to 1) means that
there is a substantial amount of information retained
by the corresponding PC
• A small value means that there is little amount of
information retained by the corresponding PC
Remainder:
If the product of the data matrix A and the vector p can be presented as
A * p = λj * p
then λj are eigen values and the vector p is eigen vector of the matrix A.
44. Eigen value analysis of the demographic
data correlation matrix
Alexander Kolker. All rights reserved 44
Eigen
value
16.44 11.19 4.63 2.73 1.15 0.853 0.63 0.307 0.067
Propo
rtion
0.433 0.295 0.122 0.072 0.03 0.022 0.017 0.008 0.002
Cumu
lative
0.433 0.727 0.849 0.921 0.951 0.974 0.990 0.998 1.000
Key Point:
Only 9 principal components (9 linear combinations of the
original variables) are required to account for all 38 original
variables.
45. Alexander Kolker. All rights reserved 45
Why Regression with Principal components?
• Because PCs are mutually uncorrelated, the
variation of dependent variable (CM $) is accounted
for by each PC independently of other PC
• Contribution of each PC is directly defined by the
coefficients of the regression equation
Key Point:
Regression with totally uncorrelated PC is one of the
most powerful methodologies for identifying significant
contributing variables (factors).
46. The Best Subset Regression
Alexander Kolker. All rights reserved 46
• Best subsets regression identifies the best-fitting
regression models that can be constructed with as
few predictor variables as possible
• All possible subsets of the predictors are examined,
beginning with all models containing one predictor,
and then all models containing two predictors, and so
on.
• The two best models for each number of predictors
are displayed
47. Best subsets regression with PCs
Alexander Kolker. All rights reserved 47
Varia
bles
R-sq
(adj)
Mallow
Cp
PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9
3 87.0 128 X X X
3 64.9 349 X X X
4 90.1 83.0 X X X X
4 88.1 99.6 X X X X
5 92.2 54.4 X X X X X
5 91.3 60.3 X X X X X
6 94.5 31.7 X X X X X X
6 93.4 37.2 X X X X X X
7 97.4 14.5 X X X X X X X
7 94.0 26.2 X X X X X X X
8 99.4 9.0 X X X X X X X X
48. Final regression equation with PC
Alexander Kolker. All rights reserved 48
CM $ = 12.8 + 0.201*PC2 - 0.387*PC3 + 1.95*PC8
(compare to the original regression…)
Key Points:
• This equation accounts for R-sq(adj) = 99.4% of the response
function (CM $) variability.
• It contains only statistically significant terms (at 5% confidence
level)
49. Conclusion from the regression equation
Alexander Kolker. All rights reserved 49
• Eigen vector coefficients for PC2, PC3 and PC8
combined with PC coefficients represent the
contribution of each variable into the $CM output
Note:
In general, for not-normalized variables the relative contribution of the Xi is:
called the elasticity coefficient Ei= (dY/Y)/(∂Xi/Xi) = ai*Xi/Y
50. Alexander Kolker. All rights reserved 50
Variable PC2 PC3 PC8
Age 18-34 0.26 0.037 -0.034
Age 35-54 -0.084 0.331 0.037
Age 55-64 -0.229 -0.173 0.236
Age 65+ -0.058 -0.185 0.015
BS/BA+ degree -0.269 -0.137 0.049
Assoc/Prof degree -0.237 0.081 -0.18
High school 0.097 0.332 0.101
No high school 0.286 -0.084 -0.078
Income < $50K 0.275 -0.105 0.025
Income $50K-$75K -0.059 -0.013 0.256
Income $75-$100K -0.27 0.125 -0.183
Income $100K+ -0.259 0.097 -0.012
Occupation: Health -0.21 -0.176 -0.206
Labor 0.265 0.116 -0.133
Professional/Adm -0.275 -0.059 -0.104
Public Service 0.029 -0.328 0.463
Service Industry -0.125 0.264 0.542
% male 0.059 0.210 0.017
% female -0.059 -0.210 -0.017
Race: African American 0.235 -0.123 0.007
Asian 0.157 0.142 -0.337
Native American -0.033 -0.339 -0.253
Other 0.263 -0.114 0.158
White -0.252 0.128 -0.087
Disease: Cancer-BMT 0.012 0.108 0.002
Med Oncology 0.012 0.107 0.01
Surgical Oncology 0.011 0.108 0.012
Cardiology 0.014 0.103 0.012
Cardiothoracic Surgery 0.014 0.103 0.011
Vascular surgery 0.018 0.104 -0.001
Digestive disease 0.014 0.103 0.005
Medicine/Primary Care 0.015 0.103 0.01
Musculoskeletal 0.014 0.105 0.012
Neurology 0.014 0.104 0.013
Transplant 0.016 0.106 0.008
Trauma 0.015 0.104 0.006
Unassigned 0.014 0.103 0.000
Women Health 0.015 0.103 -0.002
Eigen vector coefficients
for PC2, PC3 and PC8
51. Conclusion from the regression with PC
Alexander Kolker. All rights reserved 51
The primary contributing variables (factors) to CM $ are:
Age 55-64
Annual income $50 K - $75 K
Occupations: Public Service and Service Industry
Race- Other
Relative contributions of diseases are:
neurology, cardiology and musculoskeletal
52. Concluding Remarks and Reflections
Alexander Kolker. All rights reserved 52
• As analytics professionals we are rewarded for help in solving
business problems
• Building analytics that influences business decision-making
requires attention to the non-technical side of the project
(organization’s internal politics and power-sharing)
• Analytics has no practical value for the organization if it does
not affect business decision-making, regardless of how much
a new trendy technology is used
So, how much of your work is about understanding and
addressing real business problems vs. the technology
deployment, coding and finding insights in the data?
53. Alexander Kolker. All rights reserved
53
.
Appendix
“We may regard the present state of the universe as the effect of its past
and the cause of its future (Predictive analytics?!)
An intellect which at a certain moment would know all forces that set
nature in motion, and all positions of all items of which nature is
composed, if this intellect were also vast enough to submit these data to
analysis, it would embrace in a single formula (algorithm?) the
movements of the greatest bodies of the universe and those of the
tiniest atom.
For such an intellect nothing would be uncertain and the future
(predictive analytics?) just like the past would be present before its
eyes.”
- Pierre Simon Laplace, A Philosophical Essay on Probabilities, 1795
Food for Thought:
Can the contemporary Big Data Technology function as that ‘intellect’
capable of analyzing all data and getting a single formula for the future?