Python Programming & Data Structures: Variables

datatypes

data structures

conditional and looping statements

and functions

NumPy & Pandas for Data Science: Creating

accessing

modifying

saving

and loading NumPy arrays and Pandas Series/DataFrames; using Pandas functions for efficient data manipulation

Exploratory Data Analysis (EDA): Data overview

univariate and multivariate analysis (histograms

boxplots

scatterplots

heatmaps

etc.)

customizing plots

handling missing values

and outlier detection and treatment.

Inferential Statistics Foundations: Experiments

events

probability definitions

probability distributions (random variables

discrete and continuous distributions)

binomial distribution

and normal distribution

Estimation and Hypothesis Testing: Sampling

central limit theorem

estimation

hypothesis formulation

one-tailed and two-tailed tests

confidence intervals

and performing hypothesis tests

Common Statistical Tests: Test for one mean

test for equality of means

chi-square test of independence

and one-way ANOVA.

Supervised Learning & Linear Regression: Learning from data

simple and multiple linear regression

model evaluation

pros and cons of linear regression

linear regression assumptions

statistical inferences

and differences between statisticians and ML practitioners.

Classification Models & Decision Trees: Logistic regression interpretation

threshold adjustments

classification model evaluation

decision tree fundamentals

impurity measures

splitting criteria

pruning methods

regression trees

and their pros and cons.

Ensemble Learning & Model Tuning: Bagging

Random Forest

Boosting (Adaboost

Gradient Boost

XGBoost)

stacking

feature engineering

cross-validation

oversampling/undersampling

hyperparameter tuning

grid search

random search

and regularization.

Clustering & Dimensionality Reduction: K-means clustering

hierarchical clustering

silhouette score

cophenetic correlation

scaling importance

visual analysis

and principal component analysis (PCA).

Generative AI & Prompt Engineering: Generative vs discriminative AI

generative models

business applications

supervised vs unsupervised learning

prompt engineering techniques

types of prompts

and limitations.

SQL & Database Management: Database fundamentals

querying and filtering data

aggregation

in-built functions

joins

window functions

subqueries

and query execution order.

Data Visualization & Storytelling with Tableau: Dimensions and measures

data types

calculations

filtering

various visualizations

interactive dashboards

parameters

level of detail calculations

sets

blends

and storyboarding.

Artificial Intelligence & Deep Learning: Neural networks

computer vision

image processing

convolutional neural networks (CNNs)

NLP with generative AI

text embedding

transformer architectures

retrieval-augmented generation (RAG)

and prompt engineering with open-source LLMs.

Business Analytics: Marketing and retail analytics (RFM analysis

clustering

market basket analysis

CLV modeling)

financial and risk analytics (credit risk modeling

PD models

market risk optimization)

web and social media analytics (Google Ads

Google Analytics

social media analytics

sentiment analysis)

and supply chain and logistics analytics (forecasting models

Monte Carlo simulation

supply chain optimization

strategy).

Capstone: Identify and define a real-world problem

design and develop a data science solution

explore and process data

apply and evaluate data science techniques

and communicate insights to stakeholders.

Additional Modules: Learn at Your Own Pace – Introduction to Data Science

Python Pre-Work

Time Series Forecasting

and Model Deployment.