Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
● Experience in creating aggregates, hierarchies, filters, quick filters, calculated measures.
● Merge multiple data sources by joining multiple tables and using data blending.
● Experience in creating interactive dashboards by using Actions .
● By eliminating the unwanted data in worksheets by using filters.
● Designed and deployed reports with Drill up and Drop down menu option and Parameterized and Linked reports using Tableau.
● Experience in Creating Derived fields using calculated functions and parameters in Tableau
● By using context filter improve performance of tableau report .
● Designed a report to displaying the bottom of five customer dynamically by using sets
● Worked on the development of Dashboards for Key performance indicators for the top management.
● Experience in Tableau report developing, testing and deploying reporting solutions using Tableau Server.
● Scheduling the reports in Tableau Server.
Uncertainty & Probability
Baye's rule
Choosing Hypotheses- Maximum a posteriori
Maximum Likelihood - Baye's concept learning
Maximum Likelihood of real valued function
Bayes optimal Classifier
Joint distributions
Naive Bayes Classifier
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
The Nested process coordinates strategy, planning and execution. Market Segment Analysis and Product Definition are explicitly aligned. This is a plan for planning.
● Experience in creating aggregates, hierarchies, filters, quick filters, calculated measures.
● Merge multiple data sources by joining multiple tables and using data blending.
● Experience in creating interactive dashboards by using Actions .
● By eliminating the unwanted data in worksheets by using filters.
● Designed and deployed reports with Drill up and Drop down menu option and Parameterized and Linked reports using Tableau.
● Experience in Creating Derived fields using calculated functions and parameters in Tableau
● By using context filter improve performance of tableau report .
● Designed a report to displaying the bottom of five customer dynamically by using sets
● Worked on the development of Dashboards for Key performance indicators for the top management.
● Experience in Tableau report developing, testing and deploying reporting solutions using Tableau Server.
● Scheduling the reports in Tableau Server.
Uncertainty & Probability
Baye's rule
Choosing Hypotheses- Maximum a posteriori
Maximum Likelihood - Baye's concept learning
Maximum Likelihood of real valued function
Bayes optimal Classifier
Joint distributions
Naive Bayes Classifier
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
The Nested process coordinates strategy, planning and execution. Market Segment Analysis and Product Definition are explicitly aligned. This is a plan for planning.
Predictive Planning is an element of Oracle EPM's focus on Intelligent Performance Management, which is automating as much as possible in order to free up humans to do the real thinking. Predictive Planning is advanced statistical forecasting made easy and tightly integrated into EPBCS. It includes methods such as linear regression, exponential smoothing, and seasonality. For each forecast, it tests many different techniques and creates a forecast using the best one. You might use the results as your primary forecast, you might use them as your forecast seed, or you might use them to compare to and validate human-made forecasts. You don’t need a PhD in statistics. In fact, it’s a good way to learn more about statistical forecasting techniques (aka data science).
So how exactly does it work, and how can you use it to improve your forecasts? This presentation provides a quick overview of the statistical techniques and error measures. It identifies some potential use cases from finance, sales, and HR. Finally, it digs into some examples of how to set up and implement the cube. This presentation is intended for EPBCS admins and developers, as well as Finance, Sales, and HR planners who want to improve their forecasting and analytics.
Continuing our board meeting theme from last week, in this week's webinar, our own Gainsight Admin, Will Robins, dives into how he leveraged Gainsight's capabilities to prepare the charts and graphs we used in our most recent board meeting.
AVATA Webinar: Solutions to Common Demantra & ASCP ChallengesAVATA
As a leading provider of SCP solutions and a 15 year focus with Oracle Supply Chain Solutions, join AVATA as we examine the most common challenges when implementing and configuring Oracle’s Demantra and ASCP planning solutions.
AVATA is adding to their express solutions suite with “IBP express”, a hosted service offering that provides the framework for conducting the S&OP/IBP process with supported dashboard reports and KPI’s. IBP express will allow for a rapid deployment enabling your first S&OP/IBP cycle within 90-days.
IBP express is both a technology tool and service offering that supports advancing your current S&OP process or implementing S&OP/IBP for the first time. IBP express includes the required Education, Workshops, Coaching & Technology that will deliver a rapid ROI.
Dorman’s Journey towards Integrated Demand Planning leveraging SAP APO DP and...Mitesh Verma
Presentation from the ASUG Fall Focus Conference 2017 held in Chicago.
Learn how Dorman Products transformed their demand planning process and associated analytics leveraging SAP Advanced Planning and Optimization (APO) Demand Planning (DP) and SAP HANA as the enterprise data warehouse. In this session, we will share how Dorman Products partnered with Bristlecone to develop an integrated demand planning process in SAP APO-DP and supporting analytics leveraging the power of SAP HANA.
An OBIEE Success Story: How a Regional Utility Created Visibility in Supply Chain provides an overview about a project utilizing both OBIEE and Business Intelligence Analytics products. The project’s goal was to provide timely data and reporting to Supply Chain for aiding in strategic decision making. The result was a reduction in overall operational costs, performance and productivity tracking, inventory management in partnership with business operations and the initiation of basic governance practices for the data within the Oracle E-Business Suite.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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3. Executive Summary
• After data preparation and partition, three models are built in SAS
studio, EM, and DataRobot
• The same test dataset is scored by these models
• The model built in EM has the best performance
4. Introduction
• Can we predict Income level based on age, gender, education, etc.?
• What is my income level after I graduate?
5. Purpose
• Figure out the best predictive model for Income dataset
• Predict my Income level
• Practice skills for preparing data, building model, and model assessment
6. Data Selection
• Income dataset is originally extracted from 1994 Census bureau database
• Downloaded from Kaggle.com
• Reasons for choosing it:
• Target variable, Income, is categorical variable
• Medium size: 10+ columns and 30K+ rows
• Used in Macro and DataRobot projects
7. Exploration
• Using SAS studio to explore data
• 32,561 observations
• 15 variables: 6 Num, 9 Char
• Num: Age Capitalgain Capitalloss Weekhour Edunum Fnlwgt
• Char: Income Relationship Education Occupation Sex Marital
Workclass Race Nativecountry
• Target: Income (“>50K” , “<=50k”)
25. Preparation & Transformations
• Solutions:
• Imputing missing value using subject matter knowledge:
impute missing value for Workclass and Occupation with “Unemployeed”
• Imputing missing value using mode value:
impute missing value for Nativecountry with “United-States”
26. Preparation & Transformations
• Solutions:
• Coverting Capitalgain and Capitalloss from Num to Char
• Binning multiple-level variables: Education Marital Workclass
28. Preparation & Transformations
• Reasons for dropping variable Fnlwgt:
• It is the weight on the Current Population Survey files, not original data from Census
• It shows near zero importance in last week DataRobot project
29. Preparation & Transformations
• Reasons for not handling with variable Occupation:
• 15 levels
• Do not have a sound criterion
• Reasons for not handling with variable Race and Relationship:
• 5-6 Levels
• Each level is meaningful
50. Options and Recommendations
• Factors which may cause these differences:
• Dropping variable Fnlwgt
• Reducing levels
• Variable transformation: Capitalgain Capitalloss
• Increase speed, but decrease model performance
51. Options
• Using DataRobot to build models without handling “data issues”
• Keep trying in SAS studio
52. Summary
• We can predict Income level based on these characteristics
• For Income dataset, DataRobot is most robust to build models
• Be aware of unexpected outcomes for data preparing
• Back and forth, until getting an ideal result