Course Methodology
Each statistical tool or methodology used during the course is supported
by its own case study with step by step outputs that go in parallel with multi
stage analysis.
In addition to group discussions, all analysis tools are detailed and
demonstrated with sequential screen shot applications on comparative
technologies (EXCEL – STATISTICA and SAS – R and Python).
Course Objectives
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.
Target Competencies
- Project Design
- Findings Visualization
- Data Analysis
- Problem Solving using analytical tools
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
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
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
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
