Introduction - Data-Driven Leadership: The New Industry Standard

Introduction - Why Top Firms Choose Python over Excel

Introduction - Overview of Python Ecosystem: pandas, NumPy, matplotlib, Jupyter

Introduction - Setting Up Your Environment (Anaconda, IDEs, Jupyter Notebook)

Essentials - Python Basics: Variables, Data Types, Loops, Functions

Essentials - Working with Python Scripts and Jupyter Notebooks

Essentials - Common Python Libraries for Data Science

Data Structures - Lists, Tuples, Dictionaries, Sets

Data Structures - List and Dictionary Comprehensions

Data Structures - Functions and Lambda Expressions

Data Structures - Reading from and Writing to Files

NumPy - Arrays and Operations

NumPy - Indexing, Slicing, and Reshaping Arrays

NumPy - Mathematical and Statistical Operations

NumPy - Random Number Generation and Simulations

Pandas - Introduction to pandas: Series and DataFrames

Pandas - Indexing, Filtering, and Selection

Pandas - Data Alignment, Sorting, and Ranking

Pandas - Applying Functions and Handling Missing Data

Import-Export: Reading Data from CSV, Excel, JSON

Import-Export: Saving Cleaned Data

Import-Export: Web Data: APIs, HTML, and XML

Import-Export: Working with Databases (Intro to SQL with pandas)

Data Wrangling - Handling Missing Values and Duplicates

Data Wrangling - Data Transformation and Mapping

Data Wrangling - Binning, Outlier Detection, and Dummy Variables

Data Wrangling - Working with Text and Regular Expressions

Data Wrangling - Combining Datasets: Merge, Join, and Concatenate

Data Wrangling - Pivot Tables and Reshaping DataFrames

Data Wrangling - Hierarchical Indexing and Multi-Level Aggregation

Data Visualizaton - Plotting with matplotlib: Line, Bar, Scatter, Histogram

Data Visualizaton - Advanced Plotting with seaborn: Heatmaps, Pairplots, and Categorical Plots

Data Visualizaton - Customizing Charts: Legends, Labels, Styles

Data Visualizaton - Saving and Exporting Visualizations

Data Operations - Using group by for Data Aggregation

Data Operations - Applying Multiple Functions

Data Operations - Pivot Tables and Cross-Tabulations

Data Operations - Real-World Examples: Weighted Averages, Correlations, and More

Relationship in Data : Covariance - Part 1

Relationship in Data : Covariance - Part 2

Relationship in Data : Pearson’s Correlation - Part 1

Relationship in Data : Pearson’s Correlation - Part 2

Relationship in Data : R-squared

Linear Regression : Least Square Method

Linear Regression : Simple Linear Regression

Linear Regression : Multiple Regression

Linear Regression : General Linear Models (GLM) Unlocked (t-test and ANOVA)

Linear Regression Case Study : Simple Linear Regression

Linear Regression Case Study : t tests

Linear Regression Model : Assumptions

Linear Regression Model : Model Selection using Mallow’ Cp

Linear Regression Model : Backward and Forward Selection

Linear Regression Model : Residual Analysis

Linear Regression Model : Influential Observations Analysis

Linear Regression Model : Collinearity Diagnostics

Linear Regression Case Study 2 : Multiple_Linear_Regression.ipynb

Interview & CV - Guidance

Logistics Regression : Expected Values - Discrete

Logistics Regression : Expected Values - Continuous

Logistics Regression : Test of Association

Logistics Regression : Fisher's Exact Test

Logistics Regression : The Mantel-Haenszel Test (Controlling for Confounders)

Logistics Regression : Logs

Logistics Regression : Odds and Log(Odds)

Logistics Regression : Odds Ratios and Log(Odds Ratios)

Logistics Regression : Probability vs Likelihood

Logistics Regression : Maximum Likelihood

Logistics Regression : Maximum Likelihood - Binomial Distribution

Logistics Regression : Concept

Logistics Regression : Coefficients - Continuous

Logistics Regression : Coefficients - Discrete

Logistics Regression : Reference Cell and Effect Cell Coding

Logistics Regression : R Squared and p-value

Logistics Regression : Concordant and Discordant Pairs

Logistics Regression : Null Deviance and Residual deviance

Logistics Regression : FAIQs

Case Study 3 - Logistics Regression : Real World Case Study

Model Validation : Cross Validation

Model Performance : Confusion Matrix

Model Metrics : Sensitivity & Specificity

Model Diagnostics : Bias and Variance

Model Evaluation : ROC and AUC

Information Logic : Entropy

Information Logic : Mutual Information

Regularization : Ridge Regression

Regularization : Lasso Regression

Regularization : Elastic-Net Regression

Regularization : Case Study 4

Dimension Reduction : PCA (Principal Component Analysis)

Dimension Reduction : MDS (Multidimensional Scaling) and PCoA (Principal Coordinate Analysis)

Dimension Reduction : t-SNE

Dimension Reduction : UMAP Dimension Reduction

Dimension Reduction : Heatmap

Case Study 5

Clustering : K-Means

Clustering : DBSCAN

Clustering : Hierarchical

Gradient Descent

Stochastic Gradient Descent

K - Nearest Neighbors

Naive Bayes

Gaussian Naive Bayes

Decision Tree : One-Hot, Label, Target and K-Fold Target Encoding

Decision Tree : Classification Tree

Decision Tree : Regression Trees

Decision Tree : Pruning

Case Study 6

Random Forest : Build, use and validate

Random Forest : Missing Data and Clustering

Case Study 7

Boosting : AdaBoost

Boosting : Gradient Boost

Boosting : XGBoost

Boosting : Cosine Similarity

Boosting : CatBoost

Case Study 8

Support Vector Machine : Understanding

Support Vector Machine : The Polynomial Kernel

Support Vector Machine : The Radial Kernel (RBF)

Neural Networks: Demystifying Complexity

Neural Networks: Building Blocks of Neural Architectures

Backpropagation : Foundations

Backpropagation : Advanced Backpropagation Techniques

Activation Functions : ReLU and Its Applications

Activation Functions : SoftMax and ArgMax for Classification

Feedforward Networks : Handling Multiple Inputs and Outputs

Feedforward Networks : Cross-Entropy Loss and Optimization

Feedforward Networks : Derivative Calculations for Loss Functions

Convolutional Neural Networks: ConImage Classification with CNNs

Recurrent Neural Networks (RNNs) : Fundamentals

Recurrent Neural Networks (RNNs) : Long Short-Term Memory (LSTM) Networks

Advanced Architectures : Encoder-Decoder Frameworks

Advanced Architectures : Attention Mechanisms

Transformer Networks : Architecture

Transformer Networks : Decoder-Only Models (e.g., GPT-style

Transformer Networks : Decoder-Only Models (e.g., GPT-style

Linear Algebra Essentials : Tensor Operations in Neural Networks

Linear Algebra Essentials : Matrix Algebra for Deep Learning

Linear Algebra Essentials : Matrix-Based Transformer Implementation

Neural Networks in Reinforcement Learning

Start with PyTorch – Learn the basics with hands-on, beginner-friendly notebooks.

Build Your First Neural Net – Code real models using PyTorch + Lightning.

Handle Complex Data – Work with networks that take multiple inputs and outputs.

Master CNNs – Create image-based models from scratch.

Implement LSTMs – Tackle sequential data like time series and text.

Create Word Embeddings – Learn how machines understand language.

Explore Autoencoders – Compress and reconstruct data with unsupervised learning.

Code a Transformer – Build a simplified ChatGPT-style model step by step.