This document advertises an analytics conference and promotes advanced analytics skills. It summarizes:
- The conference will take place May 2-4 in San Jose and focus on business analytics skills and technologies to stay competitive.
- The keynote speaker, Daniel Fylstra, will discuss how analytics builds on business intelligence and the three levels of analytics: descriptive, predictive, and prescriptive.
- Analytic models can provide significant business benefits like cost savings, avoiding risks, and better decision making. Excel is positioned as a tool to build analytic models and gain these benefits.
- Attendees will learn how to access and prepare data, apply predictive algorithms, create simulation and optimization models, and interpret
Prof. Nikhat Fatma Mumtaz Husain Shaikh gave a guest lecture on business intelligence and analytics. She began by defining business intelligence and how analytics builds on it by using data to understand business performance and answer higher-value questions. She then discussed the three levels of analytics - descriptive, predictive, and prescriptive - and gave examples of the business payoffs that can result from building analytic models in each area. The rest of the lecture covered how to build analytic models using tools like Excel, Power BI, data mining software, simulation, and optimization. She recommended textbooks and online courses for learning more and provided examples of free tools to get started with analytics.
Data Refinement: The missing link between data collection and decisionsVivastream
The document discusses the importance of data refinement between data collection and decision making. It emphasizes the need to transform raw data into useful insights through techniques like data summarization, categorization, and predictive modeling in order to provide accurate marketing answers and improve targeting, costs, and results. Specifically, it recommends structuring data into a model-ready environment, creating descriptive variables from transaction histories, matching data to the appropriate analytical goals and levels, and categorizing non-numeric attributes.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
10 Tips for women to build a career in data scienceCarol Hargreaves
This presentation highlights the 10 things women should focus on when building a career in Data Science. Starting with the business question is key. Talking to the business users, business managers. stakeholders to understand the business question and how the results will impact the different employee roles is most important. Next is using only the relevant data to solve the business problem. After that, we should have good evaluation methods to ensure the analytical solution is sound. And lastly, but not least, show how the analytical results and models impact business in terms of its revenue, profitability, and operational efficiency.
This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Business analytics refers to the skills, technologies, and practices used to continuously gain insights and understand business performance based on data and statistical analysis. It combines data, information technology, statistical analysis, and quantitative methods to provide decision makers with scenarios to make well-researched decisions. Business analytics has evolved from operations research used in WWII to management science and now includes business intelligence, predictive analysis, and prescriptive analysis to describe past performance, predict future outcomes, and recommend actions respectively. It impacts organizations by improving profitability, increasing revenue and market share, and reducing costs.
Deep learning is a subset of machine learning that uses neural networks to enable computers to learn from large amounts of data. It can be used to solve problems involving data dependencies, huge data volumes, and highly accurate prediction and classification models. Deep learning has applications in computer vision, natural language processing, building chatbots, marketing, banking, and more. Common deep learning architectures include convolutional neural networks, recurrent neural networks, self-organizing maps, and autoencoders. A case study describes how a bank used deep learning to develop a predictive model to identify customers likely to close their accounts and the key factors driving this, in order to reduce business risk and retain customers.
Prof. Nikhat Fatma Mumtaz Husain Shaikh gave a guest lecture on business intelligence and analytics. She began by defining business intelligence and how analytics builds on it by using data to understand business performance and answer higher-value questions. She then discussed the three levels of analytics - descriptive, predictive, and prescriptive - and gave examples of the business payoffs that can result from building analytic models in each area. The rest of the lecture covered how to build analytic models using tools like Excel, Power BI, data mining software, simulation, and optimization. She recommended textbooks and online courses for learning more and provided examples of free tools to get started with analytics.
Data Refinement: The missing link between data collection and decisionsVivastream
The document discusses the importance of data refinement between data collection and decision making. It emphasizes the need to transform raw data into useful insights through techniques like data summarization, categorization, and predictive modeling in order to provide accurate marketing answers and improve targeting, costs, and results. Specifically, it recommends structuring data into a model-ready environment, creating descriptive variables from transaction histories, matching data to the appropriate analytical goals and levels, and categorizing non-numeric attributes.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
10 Tips for women to build a career in data scienceCarol Hargreaves
This presentation highlights the 10 things women should focus on when building a career in Data Science. Starting with the business question is key. Talking to the business users, business managers. stakeholders to understand the business question and how the results will impact the different employee roles is most important. Next is using only the relevant data to solve the business problem. After that, we should have good evaluation methods to ensure the analytical solution is sound. And lastly, but not least, show how the analytical results and models impact business in terms of its revenue, profitability, and operational efficiency.
This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Business analytics refers to the skills, technologies, and practices used to continuously gain insights and understand business performance based on data and statistical analysis. It combines data, information technology, statistical analysis, and quantitative methods to provide decision makers with scenarios to make well-researched decisions. Business analytics has evolved from operations research used in WWII to management science and now includes business intelligence, predictive analysis, and prescriptive analysis to describe past performance, predict future outcomes, and recommend actions respectively. It impacts organizations by improving profitability, increasing revenue and market share, and reducing costs.
Deep learning is a subset of machine learning that uses neural networks to enable computers to learn from large amounts of data. It can be used to solve problems involving data dependencies, huge data volumes, and highly accurate prediction and classification models. Deep learning has applications in computer vision, natural language processing, building chatbots, marketing, banking, and more. Common deep learning architectures include convolutional neural networks, recurrent neural networks, self-organizing maps, and autoencoders. A case study describes how a bank used deep learning to develop a predictive model to identify customers likely to close their accounts and the key factors driving this, in order to reduce business risk and retain customers.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
H2O World - Advanced Analytics at Macys.com - Daqing ZhaoSri Ambati
The document discusses advanced analytics at Macys.com. It outlines the challenges of big data predictive modeling such as scaling models, ensuring timely models, integrating models, and testing models. It describes Macys.com's advanced analytics team which includes data scientists with backgrounds in quantitative fields. The team works on projects such as personalized site recommendations, response propensity models, customer acquisition/retention modeling, and experimentation platforms. It provides examples of Macys.com's real-time site personalization and customer segmentation work.
Business analytics combines data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with possible scenarios to make well-informed decisions. It refers to the skills, technologies, and practices for developing new insights into business performance based on data analysis and statistics. Business analytics has evolved over time from operations research during WWII to management science, business intelligence, decision support systems, and software tools today. It impacts organizations by improving profitability, market share, costs, and key performance indicators. Descriptive analytics describes past data, predictive analytics forecasts future events, and prescriptive analytics recommends optimal solutions.
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
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How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
The document discusses how artificial intelligence and analytics are being used successfully in business, providing examples of applications of machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning to solve problems in fraud detection, healthcare, equity research, customer loyalty, and personalization. It also outlines the typical analytics process and different types of analytics that are commonly employed, from descriptive to predictive to prescriptive.
Data science involves analyzing data to extract meaningful insights. It uses principles from fields like mathematics, statistics, and computer science. Data scientists analyze large amounts of data to answer questions about what happened, why it happened, and what will happen. This helps generate meaning from data. There are different types of data analysis including descriptive analysis, which looks at past data, diagnostic analysis, which finds causes of past events, and predictive analysis, which forecasts future trends. The data analysis process involves specifying requirements, collecting and cleaning data, analyzing it, interpreting results, and reporting findings. Tools like SAS, Excel, R and Python are used for these tasks.
Machine Learning and Analytics Breakout SessionSplunk
This document discusses operationalizing machine learning with Splunk. It begins with an overview of machine learning and the challenges of applying it to real-time data. It then provides examples of machine learning use cases in IT operations, security, and customer analytics. The document outlines the machine learning process of getting data, exploring it, fitting and validating models, predicting outcomes, and operationalizing results. It highlights machine learning capabilities in Splunk products like the ML Toolkit, UBA, and ITSI and provides next steps for audiences to learn more.
Video: https://youtu.be/ky3159dqQ_o?t=30
Advances in Data science, Machine Learning, AI, Optimization and prediction are revolutionizing the way financial professionals are taking decisions. From sifting through large amounts of data to designing strategies to optimizing execution, technology has played a major role in changing the investment game! The 21st Century Financial Professional needs to be cognizant of the tsunami of changes that are changing the industry.
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The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
What is data mining? The process of analyzing data to discover hidden patterns and relationships that can help you manage and improve your business.
Check out: www.eleaderstochange.com
Follow #eleaders2change
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine intelligence data science methodology 060420Jeremy Lehman
Machine learning and artificial intelligence project methodology that focuses on business results, builds alignment across the entire business, and forms enduring capabilities.
Big Data Analysis and Business IntelligenceDaqing Zhao
- The document discusses big data analytics and business intelligence processes for analyzing large datasets.
- It provides an overview of big data characteristics and challenges, and how cloud computing enables analyzing massive amounts of data.
- Examples of big data analytics applications are described, including customer profiling, predictive modeling, and optimization of online marketing campaigns. Lessons are discussed for effective modeling, including the importance of domain expertise and identifying key data transformations and variables.
The document summarizes a data science project on bank marketing data using various tools in IBM Watson Studio. The project followed a standard methodology of data exploration, feature engineering, model selection, training and evaluation. Random forest, XGBoost, LightGBM and deep learning models were tested. LightGBM performed best with a 95.1% ROC AUC score from AutoAI hyperparameter tuning. The best model was deployed to IBM Watson Machine Learning for production use. Overall, the project demonstrated the effectiveness of the Watson Studio platform and tools in developing performant models from structured data.
Machine Learning and Analytics Breakout SessionSplunk
This document discusses operationalizing machine learning with Splunk. It begins with an overview of machine learning and the challenges of applying it to real-time data. Examples are given of using machine learning for predictive maintenance, security, and customer churn prediction. The process of exploring data, building models, applying and validating models is described. Finally, next steps for operationalizing machine learning workflows with Splunk are outlined, including leveraging the machine learning toolkit and Splunk ITSI/UBA products.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
H2O World - Advanced Analytics at Macys.com - Daqing ZhaoSri Ambati
The document discusses advanced analytics at Macys.com. It outlines the challenges of big data predictive modeling such as scaling models, ensuring timely models, integrating models, and testing models. It describes Macys.com's advanced analytics team which includes data scientists with backgrounds in quantitative fields. The team works on projects such as personalized site recommendations, response propensity models, customer acquisition/retention modeling, and experimentation platforms. It provides examples of Macys.com's real-time site personalization and customer segmentation work.
Business analytics combines data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with possible scenarios to make well-informed decisions. It refers to the skills, technologies, and practices for developing new insights into business performance based on data analysis and statistics. Business analytics has evolved over time from operations research during WWII to management science, business intelligence, decision support systems, and software tools today. It impacts organizations by improving profitability, market share, costs, and key performance indicators. Descriptive analytics describes past data, predictive analytics forecasts future events, and prescriptive analytics recommends optimal solutions.
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
Who is accountable for building and expanding your competitive moat?
Investing in foundational data infrastructure, training, logging, and tools
Fostering executive support for exploration and innovation, including user-facing data product and algorithm development
How to evaluate new machine intelligence projects and develop a portfolio that delivers
How AI product management differs from traditional product management
How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
The document discusses how artificial intelligence and analytics are being used successfully in business, providing examples of applications of machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning to solve problems in fraud detection, healthcare, equity research, customer loyalty, and personalization. It also outlines the typical analytics process and different types of analytics that are commonly employed, from descriptive to predictive to prescriptive.
Data science involves analyzing data to extract meaningful insights. It uses principles from fields like mathematics, statistics, and computer science. Data scientists analyze large amounts of data to answer questions about what happened, why it happened, and what will happen. This helps generate meaning from data. There are different types of data analysis including descriptive analysis, which looks at past data, diagnostic analysis, which finds causes of past events, and predictive analysis, which forecasts future trends. The data analysis process involves specifying requirements, collecting and cleaning data, analyzing it, interpreting results, and reporting findings. Tools like SAS, Excel, R and Python are used for these tasks.
Machine Learning and Analytics Breakout SessionSplunk
This document discusses operationalizing machine learning with Splunk. It begins with an overview of machine learning and the challenges of applying it to real-time data. It then provides examples of machine learning use cases in IT operations, security, and customer analytics. The document outlines the machine learning process of getting data, exploring it, fitting and validating models, predicting outcomes, and operationalizing results. It highlights machine learning capabilities in Splunk products like the ML Toolkit, UBA, and ITSI and provides next steps for audiences to learn more.
Video: https://youtu.be/ky3159dqQ_o?t=30
Advances in Data science, Machine Learning, AI, Optimization and prediction are revolutionizing the way financial professionals are taking decisions. From sifting through large amounts of data to designing strategies to optimizing execution, technology has played a major role in changing the investment game! The 21st Century Financial Professional needs to be cognizant of the tsunami of changes that are changing the industry.
In this webinar, Sri Krishnamurthy, CFA, the President of QuantUniversity shares five key trends every financial professional needs to know about. Sri along with Dr.Gustavo Vicentini and Anish Shah, CFA will be leading a full day workshop on the theme on Feb 6th.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
What is data mining? The process of analyzing data to discover hidden patterns and relationships that can help you manage and improve your business.
Check out: www.eleaderstochange.com
Follow #eleaders2change
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine intelligence data science methodology 060420Jeremy Lehman
Machine learning and artificial intelligence project methodology that focuses on business results, builds alignment across the entire business, and forms enduring capabilities.
Big Data Analysis and Business IntelligenceDaqing Zhao
- The document discusses big data analytics and business intelligence processes for analyzing large datasets.
- It provides an overview of big data characteristics and challenges, and how cloud computing enables analyzing massive amounts of data.
- Examples of big data analytics applications are described, including customer profiling, predictive modeling, and optimization of online marketing campaigns. Lessons are discussed for effective modeling, including the importance of domain expertise and identifying key data transformations and variables.
The document summarizes a data science project on bank marketing data using various tools in IBM Watson Studio. The project followed a standard methodology of data exploration, feature engineering, model selection, training and evaluation. Random forest, XGBoost, LightGBM and deep learning models were tested. LightGBM performed best with a 95.1% ROC AUC score from AutoAI hyperparameter tuning. The best model was deployed to IBM Watson Machine Learning for production use. Overall, the project demonstrated the effectiveness of the Watson Studio platform and tools in developing performant models from structured data.
Machine Learning and Analytics Breakout SessionSplunk
This document discusses operationalizing machine learning with Splunk. It begins with an overview of machine learning and the challenges of applying it to real-time data. Examples are given of using machine learning for predictive maintenance, security, and customer churn prediction. The process of exploring data, building models, applying and validating models is described. Finally, next steps for operationalizing machine learning workflows with Splunk are outlined, including leveraging the machine learning toolkit and Splunk ITSI/UBA products.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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1. • Get the essential business and data analytics skills and
technologies to stay ahead of the curve
• Uncover new information and value to remain
competitive
• 4 tracks designed to follow the Analyst’s Journey
May 2-4, San Jose, California
Register at passbaconference.com today!
(Use discount code BAMARA for $100 off registration)
3. DANIEL FYLSTRA
• President of Frontline Systems
• Developer of Solver in Excel
• 25 Years in Advanced Analytics
• Data/Text Mining - XLMiner
• Optimization – Premium Solver
• Simulation – Risk Solver
• Marketer of VisiCalc, the First
Spreadsheet on Apple II
3
www.facebook.com
/FrontlineSolvers
twitter.com/FrontlineSolver
www.linkedin.com/company
/frontline-systems-inc
4. MAY 2-4, SAN JOSE, CA
Advanced Analytics for Excel Users:
Learn How to Do It Yourself
Daniel Fylstra, Frontline Systems
5. 5
Goals for Today’s Session
• Know how Analytics builds on Business Intelligence
• Know why you’d build an analytic model: business payoffs
• Know what kinds of results you can get from analytic models
• Know how you’d build your own analytic model, and how
to get data into your model
• Know what to do next, if you want to learn
6. 6
How Analytics Builds on Business Intelligence
“Analytics are a subset of … business intelligence: a set of technologies and processes that use
data to understand business performance … The questions that analytics can answer represent the
higher-value and more proactive end of this spectrum.” – Tom Davenport, Competing on Analytics
7. 7
Analytics: The Three Levels
• Descriptive Analytics: Classic BI
• Quantitative Assessment of Past Business Results
• Statistics, Exploratory Data Analysis, Visualization
• Predictive Analytics
• Quantitative Methods to Predict New Outcomes
• Forecasting, Prediction, Classification, Association
• Prescriptive Analytics
• Quantitative Methods to Make Better Decisions
• Decision Trees, Monte Carlo Simulation, Optimization
8. 8
Why Build Analytic Models: Example Payoffs
• Two Frontline Systems Customer Examples
• Excel model to optimally deploy 83 employees with different skill
sets across 24 stations saved $1.9 million per year in overtime.
• Excel simulation model showed major chemical company why a
plant was missing goals, and how to solve the problem without
any new investment.
• U.S. Air Force Air Logistics Center
• C-5 Galaxy transport maintenance hub reduced turnaround time
from 360 to 160 days, saving taxpayers $50 million and saving
soldiers’ lives.
• Memorial Sloan-Kettering Cancer Center
• Optimizing radiation beams reduced side-effects of treating cancer
– improving quality of life and saving $459 million per year on
prostate cancer alone.
9. 9
Can This Help in Your Work or Career?
• Optimization models can deliver huge cost savings
• Simulation/risk analysis models can help avoid disaster
• But very few business analysts have the skills to do this
• If you can do this, your value to your company will rise
• Some analytic models address operations, others
address strategic decisions
• Ex. whether to build a new plant, and where to locate it
• Be prepared to present your work to senior management
10. 10
Descriptive Analytics: Excel & Power BI
• Key task: Data access / shaping – Power Query does this
• Excel + Power Pivot data model holds Past Business Results
• Pivot charts, Power View, Power BI for data visualization
• Formulas: Sum, Count, Average, Min, Max, Var, StdDev
11. 11
Predictive Analytics: Data Mining
• Key tasks: Data shaping, applying predictive models
• Data mining algorithms “fit” analytic model to past data
• Trained/fitted models are applied to newly arriving data
• Classify: ex. Good/Poor credit risk, Likely/Unlikely to churn
• Predict: ex. stock price, house price, exchange rate
• Forecast a time series: ex. next sales from past sales history
• Associate: ex. People who bought this item also bought...
• Tools: Azure ML, XLMiner, Predixion, SAS, SPSS, R, others
12. 12
Prescriptive Analytics: Optimization, Simulation
• Key task: Create a model – A person (you) must do this
• Model must capture essential features of the business situation
• Larger models often get their data from BI / Descriptive Analytics
• A “What If” model is the starting point – Excel is a natural tool!
• Given an appropriate model, we can:
• Ask “What are all the possible outcomes?” – simulation/risk analysis
• Ask “What’s the best outcome we can achieve?” – optimization
• Tools: Solver, Risk Solver, @RISK, Crystal Ball, IBM, SAS, others
13. 13
Results from an Analytic Model
• Results from a data mining model:
• Tool to classify or predict outcomes for new cases
• Assessment of accuracy / predictive power
• Results from a simulation model:
• Full range of outcomes and their likelihood
• Sensitivity analysis of input parameters vs. outcomes
• Results from an optimization model:
• Best attainable objective, values for decision variables
• Sensitivity analysis of decision variables & constraints
14. 14
Data Mining: What You Need, How You Do It
• What You Need: Tools to
• Access / shape data, explore / visualize data
• Train / “fit” models to data: machine learning
• Validate model results: statistics, Lift / ROC curves
• How You Do It
• Data “wrangling” / cleaning is usually the first step
• Use feature selection to identify variables that matter
• Try multiple algorithms: Regression, trees, neural nets
• Assess and think about results: Avoid over-fitting
15. 15
Simulation: What You Need, How You Do It
• What You Need: Tools to
• Create a “what if” model, calculating results of interest
• Define probability distributions for uncertain inputs
• Run Monte Carlo simulation, create statistics and charts
• How You Do It
• Define distributions by fitting data, or industry practice
• Define dependence among inputs: corr. matrices, copulas
• Run simulation, or multiple simulations with parameters
• Assess and think about results: stats, histograms, scatterplots
16. 16
Optimization: What You Need, How You Do It
• What You Need: Tools to
• Create a “what if” model, calculating results of interest
• Define decision variables for inputs under your control
• Define constraints and an objective to max / minimize
• Run an optimization for optimal values, sensitivity analysis
• How You Do It
• Define constraints for limited resources, physical conditions, policies
• Understand dependence between outputs and inputs: linear / nonlinear
• Run optimization, or multiple optimizations with parameters you vary
• Assess and think about results: understand “dual values,” sensitivity
17. 17
Can This Help in Your Work or Career?
• Optimization models can deliver huge cost savings
• Simulation/risk analysis models can help avoid disaster
• But very few business analysts have the skills to do this
• If you can do this, your value to your company will rise
• Some analytic models address operations, others
address strategic decisions
• Ex. whether to build a new plant, and where to locate it
• Be prepared to present your work to senior management
18. 18
Where to Learn More: Textbooks on Amazon
• Cliff Ragsdale Spreadsheet Modeling 7th Ed
• Powell & Baker Management Science 4th Ed
• Camm et al Essentials of Business Analytics
• James Evans Business Analytics
19. 19
Where to Learn More: Online Courses and Tools
• www.edx.org
• www.coursera.org
• www.solver.com
• www.xlminer.com
20. 20
Free Tools to Get Started in Excel and Excel Online
• Excel: Power Query, Power
Pivot, Power View, Solver
• Power BI: Free account,
Power BI Designer
• Excel Online Office Add-ins:
Solver, Risk Solver, XLMiner
• XLMiner.com, Rason.com:
Free accounts
21. MAY 2-4, SAN JOSE, CA
Thank You –See You in San Jose!
Daniel Fylstra, Frontline Systems
22. Like What You Heard?
Daniel Fylstra will be presenting at the
PASS Business Analytics Conference 2016!
Pre-Conference Session (full day)
• Advanced Analytics for Excel Users: Learn
How to Do it Yourself
Breakout Session (60 min)
• Prescriptive Analytics: Decision Models
with Real Business Payoffs
23. 23
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Editor's Notes
If you like what you hear in these webinars, make sure to attend the PASS Business Analytics Conference taking place May 2-4 in San Jose, California. With hands-on learning opportunities from data and business analytics experts and a variety of networking opportunities, the PASS Business Analytics Conference is a great opportunity to gain valuable skills and advance your career.
All webinar registrants get $100 off the two-day pass. Just use discount code BAMARA during registration. Visit passbaconference.com to register today!
If you are interested in accessing other free educational content, the PASS organization has a variety of Virtual Chapters which offer free webinars to their members every month. Check out upcoming webinars, and recordings of past webinars, from our Business Analytics, Excel BI, Business Intelligence, Big Data and Cloud Virtual Chapters by visiting www.sqlpass.org/vc
Like what you heard here?
Daniel will be presenting the pre-conference session Advanced Analytics for Excel Users: Learn How to Do it Yourself and the breakout session Prescriptive Analytics: Decision Models with Real Business Payoffs at the PASS Business Analytics Conference 2016.
This BA Marathon is brought to you by PASS, a not-for-profit organization which offers year-round learning opportunities to data professionals. If you’re not a member yet, join today, membership is free!