Course overview:

• Introduction to Data Science
  • Descriptive Statistics (mean, median, mode, variance, standard deviation)
  • Exploratory Data Analysis (Univariate, Bivariate analysis, etc.)
  • Visualisation: Box-Plot, Histogram, Scatter plots and etc.
  • Missing Data: Imputation methods
  • Multicollinearity, Expectation, Variance, Correlation & Covariance, Outliers
  • Confounding variables & Interaction effects
  • Normal Distributions, Standardize Normal distribution & Central Limit Theorem 
• Inferential Statistics​​​​​​​
  • Descriptive Statistics vs Inferential statistics
  • Populations, parameters, samples in inferential statistics
  • Point Estimates and Confidence Intervals
  • One-way and two-way ANOVA, MANOVA
  • Hypothesis Testing : Null and Alternative hypothesis, Decision Making
  • T-test for one sample and two sample proportion, Paired T-tests
• Nonparametric Statistics​​​​​​​
  • What is nonparametric statistics? 
  • The Sign Test
  • The Wilcoxon Signed-Rank Tests
  • The Kruskal Wallis Test
  • Spearman Rank Correlation Test
• Linear Regression Modelling​​​​​​​
  • Simple & Multiple Linear Regression Models
  • Assumptions of the Multiple Linear Regression
  • Standard Deviation of random errors, Coefficients
  • Model Validation techniques (Stepwise Regression,  Goodnes-of-Fit test, Cross Validation)
  • Nonlinear Regression
• Introduction to Machine Learning ​​​​​​​
  • Supervised & Unsupervised learning
  • Classification
  • Logistic Regression
  • Decision Trees, XGBoost and Random Forest
  • Clustering Analysis: K-means clustering
  • K Nearest Neighbors