<aside> 💡

Topics to be covered:

</aside>

• Basics: Descriptive vs inferential statistics, continuous vs discrete data, nominal vs ordinal data
• Linear Algebra: Vectors, Metrices, Eigenvalues and Eigenvectors
• Calculus: Basics of integral and differential calculus
• Basic plots: Histograms, pie charts, bar charts, scatter plot etc.
• Probability basics
  • Measures of central tendency: mean, median, mode
  • Measures of dispersion: variance, standard deviation
  • Distributions: Normal distribution
  • Correlation and covariance
  • Central limit theorem
  • Hypothesis testing: p value, confidence interval, type 1 vs type 2 error, Z test

<aside> 💡

Resources

</aside>

Mathematics, statistics for data science and machine learning

Khan Academy

Vectors | Chapter 1, Essence of linear algebra

The essence of calculus

Differential equations, a tourist's guide | DE1