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Applied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs
DATE
24 - 26 Apr, 2024
LOCATION
To Be Determined;
Why Attend?
Applied Data Analysis Masterclass: Visualization, Statistics and Advanced Programs
Course Objectives
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By the end of the course, participants will be able to:
- 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
Target Audience
Applied Data Analysis is the foundation for all Machine Learning and Artificial Intelligence (AI) practitioners. It is prerequisite knowledge that is applicable in all industries and data related functions.
- Data visualization and descriptive statistics
- The different types of Data
- Data sources
- Data
- Variables
- Data visualization
- Pies, Doughnuts, Bars
- Histograms, Lines, Scatter plots
- Heat maps and Tuckey boxes
- Geographical maps
- Central tendency measurements
- Average
- Median
- Mode
- Scatter tendency measurements
- Quartile
- Variance
- Standard deviation
- Estimations
- Punctual
- Confidence Interval
- The different types of Data
- Comparing two groups
- Two mean test
- Equal variances (t-test)
- Unequal variances (t-test – Welch correction)
- Two variance test (F-Test)
- Two proportion test (Chi Square test)
- Two distribution test (Chi Square test)
- Attraction – Repulsion Matrix
- Vertical and horizontal profiling
- Two mean test
- Comparing multiple groups
- Multiple mean test
- Equal variances (F-Test and ANOVA Table)
- Unequal variances (F-Test – Welch Correction)
- Multiple Variance test
- Levene test
- Chi Square test
- Multiple proportion test (Chi Square test)
- Multiple distribution test (Chi Square test)
- Attraction – Repulsion Matrix
- Vertical and horizontal profiling
- Mean pair comparisons methods:
- General
- Bonferroni
- Tukey - Kramer
- Multiple mean test
- 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
- Simple linear regression
- 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
- Data analysis project best practices
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