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Course 2 : Statistics Essentials – Primer

Course Curriculum

Essential stats for DS
In this comprehensive primer, we lay the groundwork for a solid understanding of statistical principles, making complex ideas accessible to learners at all levels.

  • Essential stats for DS : why stats and maths – overview
    19:20
  • MCQ : statistics overview
  • Data Types, Tables, and Feature Types Explored
    16:20
  • MCQs : Data tables & feature types
  • Exploring Simple and Advanced Sampling Strategies: with Python Demos
    40:30
  • Central Measures in Data: From Basics to Winsorizing
    23:26
  • Exploring Data Dispersion Measures
    36:02
  • Data Distributions: A Comprehensive Guide, with hands on python code
    01:01:23
  • Exercise : Calculate PDF of a score (dummy data)
  • Exploring Statistical Measures: Kurtosis, Skewness, and Symmetry
    16:01
  • Covariances in feature engineering (data science/ machine learning
    13:48
  • Correlations and Multi-collinearity in Feature Engineering
    20:30
  • Exploring Correlation Measures in Data Science
    15:14
  • quick test on PDF/CDF
  • Quick test on PDF/CDF

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Material Includes

  • self-learning videos
  • MCQs - at lesson end
  • Python notebook file
  • Datasets (links)
  • Course end - Quiz
  • Course end - Projects