Advanced Stats & Data Analytics

Session 1 :

Course Content : Introduction; data and presentations; descriptive statistics

ChatGPT Usage : Example to create Charts

Session 2 :

Course Content : sample spaces, set operations, axioms probability; conditional probability

ChatGPT Usage : Ask to know regarding Probability

Session 3 :

Course Content : Random variables, pmfs/pdfs/cdfs; Example – Uniform Distribution and one more from the Teacher (to show the students – how to formulate a distribution)

ChatGPT Usage : Ask ChatGPT for Concepts in details

Session 4 :

Course Content : Conditional random variables, variance/covariance/correlations; inequalities, String and Weak law of large numbers (LLN) – Intuition

ChatGPT Usage : Ask ChatGPT for the theory and example

Session 5 :

Course Content : Independence, Expectation, joint random variables, bit of Moment and MGF

ChatGPT Usage : Ask ChatGPT for detailed formulation and derivation (if interested)

Session 6 :

Course Content : Discrete named distributions (Binomial, Poisson, hypergeometric, negative Binomial)

ChatGPT Usage : Ask ChatGPT to show the formulation and derivations of all Moments

Session 7:

Course Content : Continuous named distributions (Normal, exponential, Poisson Chi-square);

ChatGPT Usage : Ask ChatGPT to show the formulation and derivations of all Moments

Session 8:

Course Content : Why Normal is important? Basic Conversion to Normality, Test of Normality (AD Test)

ChatGPT Usage : Ask ChatGPT to show Visualization for AD Test and its significance

Session 9:

Course Content : Parameters, likelihood, maximum likelihood estimation (MLE), find MLE with a Simple Distribution

ChatGPT Usage : Ask ChatGPT to derive MLE and show examples

Session 10:

Course Content : Central limit theorem (CLT), continuity corrections, general confidence intervals

ChatGPT Usage : Ask ChatGPT to interpret visualizations

Session 11:

Course Content : Important of z-scores, z-intervals, prediction intervals, How to use it?

ChatGPT Usage : Ask ChatGPT to interpret visualizations

Session 12:

Course Content : Sampling distribution of the mean estimator; t-intervals, binomial intervals

ChatGPT Usage : Ask ChatGPT to interpret visualizations

Session 13:

Course Content : Hypothesis testing, z-tests under different situations

Session 14:

Course Content : Comparison of Data – Notion of ANOVA; Example – one sample t-tests

Session 15:

Course Content : Extension – Two samples T test, paired, binomial tests

Session 16:

Course Content : Extension – MANOVA, Chi-square Goodness of Fit – Where and how to use?

Session 17:

Course Content : Sampling theory details – SRSWR, SRSWOR, Stratified Sampling, Intuition behind Multistage Design

Session 18:

Course Content : Simple regression overview, MLE estimates and their distributions; Determine Regression coefficients – Interpret importance and validate Assumptions

Session 19:

Course Content : Feature Extraction and Feature Engineering related to most of the Statistical Algo

Session 20:

Course Content : Multiple regression – All details including Regression Assumptions

Session 21:

Course Content : Logistic Regression – All details including Log Likelihood Loss Function

Session 22:

Course Content : Decision Tree – Where and where to use? Example – CART, CHAID

Session 23:

Course Content : General Overview of Overfitting; Example – Pruning, Ridge, Lasso

Session 24:

Course Content : Correctness of any Regression/Classification – Intuitive and formulative

Session 25:

Course Content : Time Series data – How and where is it used? Why is it different with Regression?

Session 26:

Course Content : Time Series – Autocorrelation, Stationarity – How to derive and importance

ChatGPT Usage : ChatGPT to show examples

Session 27:

Course Content : Time Series – Autoregression, Moving Average, ARMA – Intuition

ChatGPT Usage : ChatGPT to show examples

Session 28:

Course Content : Clustering – Intuition and Importance – K-means, Hierarchical – Difference

ChatGPT Usage : ChatGPT to show Graphical representation

Session 29:

Course Content : Clustering – Density based Clustering – Why and Where is it used?

ChatGPT Usage : ChatGPT to show Graphical representation

Session 30:

Course Content : Basic Neural Network – Intuition and Loss function (Backpropagation)

ChatGPT Usage : ChatGPT to show Graphical representation