Course Objectives

• This Course Objective Hasn't Been Provided Yet

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
• 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

This course hasn't been scheduled yet, however you can request for In-House training