Applied data Analysis

Who Should Attend

This course discusses key points in designing a smart data collection process, sampling best approach, validating the quality of the information stored for analysis, and understanding all the visualization possibilities and their corresponding descriptive statistical KPIs.  Moreover, this course explores all techniques and tools for comprehensive data analysis, prior to kicking off any work or even a career in the world of data.  The course also serves as a primer to any Machine Learning course/program.
In addition, this course is designed to make participants have a clear and complete understanding of data structuring for efficient data analysis, of profiling different groups scientifically by analyzing data smartly and efficiently, and of appropriately manipulating several technology tools now in the market.

·   Comprehend and plan the lifecycle of a good data analysis project
·   Translate any business into a comprehensive database
·   Evaluate data quality for analysis and reporting
·   Describe and interpret data basics with complete descriptive statistics
·   Explore the complete story behind data analysis  .

Data visualization and descriptive statistics
·   The different types of Data
·   Data visualization
·   Central tendency measurements
·   Scatter tendency measurements
·   Estimations
 Comparing Two groups
·   Two mean test
·   Two variance test (F-Test)
·   Two proportion test (Chi Square test)
·   Two distribution test (Chi Square test)
 Comparing multiple groups
·   Multiple mean test
·   Multiple Variance test
·   Multiple proportion test (Chi Square test)
·   Multiple distribution test (Chi Square test)
·   Mean pair comparisons methods
 Simple regressions
·   Simple linear regression
* Line equation
* Testing the regression line validity (t-nullity test)
* R vs. R Square interpretation
* ANOVA table analysis
·   Simple logistic regression
* Probabilistic model
* Testing the model validity (Chi Square test)
* Predicting classification
* Odds ratio interpretation
Data analysis project best practices
·   Data analysis project best practices
* Ask
* Design
* Preview
* Analyze
* Communicate
·   Sampling methods
* Random and systematic
* Multilevel, stratified and cluster
* Convenient, quota and judgmental
·   PMP for research projects overview
* Integration, cost, scope, time, cost, quality, communication
* Risk, procurement and stakeholders

Business Analysts, Project Managers, Business Managers, anyone involved in the creation, maintenance, or enforcement of the organizational information, practices and procedures.

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Duration: 5 Days
Level: All Level

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