How does Generative AI enhance data science?

Generative AI creates new data from patterns, making data science more powerful with automation, predictions, and smart solutions.

What career opportunities are available after this training?

You can pursue roles like Data Scientist, AI/ML Engineer, Generative AI Developer, NLP Engineer, or Research Analyst.

Is this course suitable for beginners?

Yes! It’s designed for both beginners and experienced professionals, starting from the basics and progressing to advanced topics.

What is Generative AI, and how does it relate to data science?

Generative AI creates new data by learning patterns, enhancing automation and innovation in data science.

Why take this course in 2025 for career growth?

In 2025, AI skills are in high demand. This course prepares you for top tech roles with hands-on training in data science and automation.

Data Science with Generative AI Course – 2025 (Updated)

Module 1: Python Programming And Flaskframework

Introduction
What is Python?
Why does Data Science require Python?
Installation of Anaconda
Understanding Jupyter Notebook (IDE)
Basic commands in Jupyter Notebook
Understanding Python Syntax
and Operators
Variables, Data Types, and Strings
Lists, Sets, Tuples and Dictionaries

Control Flow & Conditional Statements

Conditional Operators, Arithmetic Operators and Logical Operators
if, elif and else Statements
While Loops and control flow
For Loops and nested loops
pass, break and continue
Nested Loops and List and Dictionary Comprehensions
What is function and types of functions
Code optimization and argument functions
Scope
Lambda Functions
Map, Filter and Reduce
Importing a Module Using help() and dir() Aliasing or Renaming
Some Important Modules in Python: math module, random module, datetime and os module

File Handling

Create, Read, Write files and Operations in File Handling
Errors and Exception Handling Class and Objects
Create a class
Create a object
The __init__()
Modifying Objects
Object Methods
Self
Modify the Object Properties
Delete Object
Pass Statements

Module 2: Data Analysis In Python

Numpy - Numerical Python

Introduction to Array
Creation and Printing of array
Basic Operations in Numpy
Indexing
Mathematical Functions of Numpy
Series and DataFrames
Data Importing and Exporting through Excel, CSV Files
Data Understanding Operations
Indexing and slicing and More filtering with Conditional Slicing
Groupby, Pivot table and Cross Tab
Concatenating and Merging Joining
Descriptive Statistics
Removing Duplicates
String Manipulation
Missing Data Handling

DATA VISUALIZATION

Data Visualization Using Matplotlib And Seaborn
Introduction to Matplotlib
Basic Plotting
Properties of plotting
About Subplots
Line plots
pie chart and Bar Graph
Histograms
Box and Violin Plots
Scatterplot
Case Study On Exploratory Data Analysis (eda) & Visualizations
What is EDA?
Uni - Variate Analysis
Bi - Variate Analysis
More on Seaborn Based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots

UNSTRUCTURED DATA PROCESSING

Regular Expressions
Structured Data and Unstructured Data
Literals and Meta Characters
How to Regular Expressions using Pandas?
Inbuilt Methods
Pattern Matching

Project On Web Scraping: Data Mining And Exploratory Data Analysis

Data Mining (WEB - SCRAPING)
This project covers the main four steps of Data Science Life Cycle which involves
Data Collection
Data Mining
Data Preprocessing
Data Visualization Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Module 3: Advanced Statistics

Data Types in Statistics

Statistics in Data science
What is Statistics?
How is Statistics used in Data Science?
Population and Sample
Parameter and Statistic
Variable and its types

Data Gathering Techniques

Data types
Data Collection Techniques
Sampling Techniques
Convenience Sampling, Simple Random Sampling
Stratified Sampling ,Systematic Sampling and Cluster Sampling

Descriptive Statistics

Data types
Data Collection Techniques
Sampling Techniques
Convenience Sampling, Simple Random Sampling
Stratified Sampling ,Systematic Sampling and Cluster Sampling
What is Univariate and Bi Variate Analysis?
Measures of Central Tendencies
Measures of Dispersion
Skewness and Kurtosis
Box Plots and Outliers detection
Covariance and Correlation

Probability Distribution

Probability and Limitations
Discrete Probability Distributions
Bernoulli, Binomial Distribution, Poisson Distribution
Continuous Probability Distributions
Normal Distribution, Standard Normal Distribution

Inferential Statistics

Sampling variability and Central Limit Theorem

Confidence Intervals

Hypothesis Testing

Z -test, t-test

Chi – Square Test

F -Test and ANOVA

Module 4: Data Base (Sql) + Reporting Tool (Power BI)

SQL for Data Science

Introduction to Databases
Basics of SQL
DML, DDL, DCLand Data Types
Common SQLcommands using SELECT, FROM and WHERE
Logical Operators in SQL
Filtering and Sorting
Advanced filtering using IN, OR and NOT
Sorting with GROUPBYand ORDER BY
SQLJoins
INNER and OUTER joins to combine data from multiple tables
RIGHT, LEFTjoins to combine data from multiple tables
SQLAggregations
Common Aggregations including COUNT, SUM, MIN and MAX
CASE and DATE functions as well as work with NULLvalues
Subqueries and Temp Tables
Subqueries to run multiple queries together
Temp tables to access a table with more than one query
Window Functions
ROW_NUMBERS(), RANK(), DENSE_RANK(), LAG, LEAD, SUM, COUNT, AVG

Introduction To Power Bi

What is Business Intelligence?
Power BI Introduction
Quadrant report
Comparison with other BI tools
Power BI Desktop overview
Power BI workflow
Installation query addressal

Data Import And Visualizations

Data import options in Power BI
Import from Web (hands on)
Why Visualization?
Visualization types
Categorical data visualization
Trend Data viz
Visuals for Filtering
Slicer details and use
Formatting visuals
KPI visuals
Tables and Matix

Power Queries

Power Query Introduction
Data Transformation - its benefits
Introducing ribbons
Queries panel
M Language briefing
Power BI Datatypes
Changing Datatypes of columns
Filtering
Inbuilt column Transformations
Inbuilt row Transformations
Combine Queries
Merge Queries

Power Pivot And Introduction To Dax

Power Pivot
Intro to Data Modelling
Relationship and Cardinality
Relationship view
Calculated Columns vs Measures
DAX Introduction and Syntax

Data Analysis Expressions

DAX recap
DAX logical functions
DAX text functions
DAX math and statistical Functions
DAX aggregation function
DAX filter function
DAX time intelligent function
Creating a Date Dimension table
Related aspects with tables

Login, Publish To Web And Rls

Power BI services
Dashboard creation
Web Content, Image, Text Box, Video
Dashboard formatting
vSharing your dashboard
RLS introduction

Miscellaneous Topics

Visual Interactions
Drill Through
Drilldown
Conditional Formatting
Creating buttons in Power BI reports
Creating Python Script Visuals
This module will end up with a project

Module 5: Machine Learning - Supervised Learning

Introduction

What Is Machine Learning?
Supervised Versus Unsupervised Learning
Regression Versus Classification Problems Assessing Model Accuracy

Introduction And Linear Algebra

Supervised Versus Unsupervised Learning
Introduction to Matrices
Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms
Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors

REGRESSION TECHNIQUES

Linear Regression
Simple Linear Regression:
Estimating the Coefficients
Assessing the Coefficient Estimates
R Squared and Adjusted R Squared
M SE and RMSE
Estimating the Regression Coefficients
OLS Assumptions
Multicollinearity
Feature Selection
Gradient Discent

Evaluating the Metrics of Regression Techniques

Homoscedasticity and Heteroscedasticity of error terms
Residual Analysis
Q-Q Plot
Cook's distance and Shapiro-Wilk Test
Identifying the line of best fit
Other Considerations in the Regression Model
Qualitative Predictors
Interaction Terms
Non-linear Transformations of the Predictors

Polynomial Regression

Why Polynomial Regression
Creating polynomial linear regression
evaluating the metrics

Regularization Techniques

Lasso Regularization
Ridge Regularization
ElasticNet Regularization

Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python

PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing and above Regression Techniques

CLASSIFICATION TECHNIQUES

Logistic regression
An Overview of Classification
Difference Between Regression and classification Models
Why Not Linear Regression?
Logistic Regression:
The Logistic Model
Estimating the Regression Coefficients and Making Pr edictions
Logit and Sigmoid functions
Setting the threshold and understanding decision boundary
Logistic Regression for >2 Response Classes
Evaluation Metrics for Classification Models:
Confusion Matrix
Accuracy and Error rate
TPR and FPR
Precision and Recall, F1 Score
AUC – ROC
Kappa Score
Principle of Naive Bayes Classifier
Bayes Theorem
Terminology in Naive Bayes
Posterior probability
Prior probability of class
Likelihood
Types of Naive Bayes Classifier
Multinomial Naive Bayes
Bernoulli Naive Bayes and Gaussian Naive Bayes

TREE BASED MODULES

Decision Trees
Decision Trees (Rule Based Learning)
Basic Terminology in Decision Tree
Root Node and Terminal Node
Regression Trees and Classification Trees
Trees Versus Linear Models
Advantages and Disadvantages of Trees
Gini Index
Overfitting and Pruning
Stopping Criteria
Accuracy Estimation using Decision Trees

Case Study: A Case Study on Decision Tree using Python

Resampling Methods
Cross-Validation
The Validation Set Approach Leave-One-Out Cross-Validation
k -Fold Cross-Validation
Bias-Variance Trade-Offfor k-Fold Cross-Validation

Ensemble Methods in Tree Based Models

What is Ensemble Learning?
What is Bootstrap Aggregation Classifiers and how does it work?
Random Forest
What is it and how does it work?
Variable selection using Random Forest
What is it and how does it work?
Hyper parameter and Pro's and Con's
Case Study: Ensemble Methods - Random Forest Techniques using Python

DISTANCE BASED MODULES

K Nearest Neighbors
K-Nearest Neighbor Algorithm
Eager Vs Lazy learners
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Curse of Dimensionality
Pros and Cons of KNN
How to improve KNN performance
Case Study: A Case Study on k-NN using Python
Support Vector Machines
The Maximal Margin Classifier
HyperPlane
Support Vector Classifiers and Support Vector Machines
Hard and Soft Margin Classification
Classification with Non-linear Decision Boundaries
Kernel Trick
Polynomial and Radial
Tuning Hyper parameters for SVM
Gamma, Cost and Epsilon
SVMs with More than Two Classes

Case Study: A Case Study on SVM using Python

PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing and above Classification Techniques

UN-SUPERVISED LEARNING

Why Unsupervised Learning
How it Different from Supervised Learning
The Challenges of Unsupervised Learning

Principal Components Analysis

Introduction to Dimensionality Reduction and it's necessity
What Are Principal Components?
Demonstration of 2D PCAand 3D PCA
EigenValues, EigenVectors and Orthogonality
Transforming Eigen values into a new data set
Proportion of variance explained in PCA
Case Study: A Case Study on PCA using Python

K-Means Clustering

Centroids and Medoids
Deciding optimal value of 'k' using Elbow Method
Linkage Methods

Hierarchical Clustering

Divisive and Agglomerative Clustering
Dendrograms and their interpretation
Applications of Clustering
Practical Issues in Clustering
Case Study: A Case Study on clusterings using Python

Recommendation Systems

What are recommendation engines?
How does a recommendation engine work?
Data collection
Data storage
Filtering the data
Content based filtering
Collaborative filtering
Cold start problem
Matrix factorization
Building a recommendation engine using matrix factorization
Case Study

Module 6: Deep Learning

Introduction to Neural Networks

Introduction to Perceptron & History of Neural networks
Activation functions a)Sigmoid b) Relu c)Softmax d)Leaky Relu e)Tanh
Gradient Descent
Learning Rate and tuning
Optimization functions
Introduction to Tensorflow
Introduction to keras
Back propagation and chain rule
Fully connected layer
Cross entropy
Weight Initialization
Regularization

TensorFlow 2.0

Introducing Google Colab
Tensorflow basic syntax
Tensorflow Graphs
Tensorboard

Artificial Neural Network with Tensorflow

Neural Network for Regression
Neural Network for Classification
Evaluating the ANN
Improving and tuning the ANN
Saving and Restoring Graphs

Module 7: Cnn & Computer Vision

UNIT 1: Working with images & CNN Building Blocks

Working with Images_Introduction
Working with Images - Reshaping understanding, size of image understanding pixels Digitization, Sampling, and Quantization
Working with images - Filtering
Hands-on Python Demo: Working with images
Introduction to Convolutions
2D convolutions for Images
Convolution - Backward
Transposed Convolution and Fully Connected Layer as a Convolution
Pooling: Max Pooling and Other pooling options

UNIT 2: CNN Architectures and Transfer Learning

CNN Architectures and LeNet Case Study
Case Study: AlexNet
Case Study: ZFNet and VGGNet
Case Study: GoogleNet
Case Study: ResNet
GPU vs CPU
Transfer Learning Principles and Practice
Hands-on Keras Demo: SVHN Transfer learning from MNISTdataset
Transfer learning Visualization (run package, occlusion experiment)
Hands-on demo -T-SNE

UNIT 3: Object Detection

CNN's at Work - Object Detection with region proposals
CNN's at Work - Object Detection with Yolo and SSD
Hands-on demo- Bounding box regressor
Need to do a semantic segmentation project

UNIT 4: CNN's at Work - Semantic Segmentation

CNNs at Work - Semantic Segmentation
Semantic Segmentation process
U-Net Architecture for Semantic Segmentation
Hands-on demo - Semantic Segmentation using U-Ne
Other variants of Convolutions
Inception and Mobile Net models

UNIT 5: CNN's at work- Siamese Network for Metric Learning

Metric Learning
Siamese Network as metric learning
How to train a Neural Network in Siamese way
Hands-on demo - Siamese Network

Module 8: Natural Language Processing

Unit 1: Introduction to Statistical NLP Techniques

Introduction to NLP
Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
Preprocessing in NLPBag of words, TF-IDF as features
Language model probabilistic models, n-gram model and channel model
Hands on NLTK

Unit 2 : Word embedding

Word2vec
Golve
POS Tagger
Named Entity Recognition(NER)
POS with NLTK
TF-IDF with NLTK

Unit 3: Sequential Models

Introdcution to sequential models
Introduction to RNN
Intro to LSTM
LSTM forward pass
LSTM backprop through time
Hands on keras LSTM

Unit 4 : Applications

Sentiment Analysis
Sentence generation
Machine translation
Advanced LSTM structures
Keras- machine translation
ChatBot

Gen AI

Module 1: Generative AI and its Industry Applications Topics

Generative AI Principles Types of Generative Models
Applications of Generative Models Machine Learning Algorithms with GenAI Applications of Generative AI
Generative AI: Advantages and Disadvantages Ethical Considerations

Module 2: NLP and Deep Learning Topics

Natural Language Processing (NLP) Essentials Text Classification
Text Preprocessing
Basic NLP Tasks
Deep Learning for NLP Neural Networks
Backpropagation RNN
LSTM
Deep Learning Applications in NLP

Module 3: Autoencoders and GANs Topics

Basic Autoencoders
Variational Autoencoders (VAEs)
Applications in Data Compression and Generation Basic GAN Architecture
Training GANs Variants of GANs DCGAN
Cycle GAN

Module 4: Language Models and Transformer-based Generative Models Topics

Exploring Language Models Types of Language Models
Applications of Language Models
The Transformer Architecture: Attention Mechanism Advanced Transformer Models
GPT BERT
Applications of Transformer-based Models

Module 5: Prompt Engineering

Prompt Engineering Principles What is Prompt Engineering? Importance and Applications Prompt Design Strategies Types of Prompting
Crafting Effective Prompts Parameter Tuning

Module 6: Generative AI with LLMs Topics

LLMs and Generative AI Project Lifecycle LLM Pre-Training and Scaling
Fine-Tuning LLMs with Specific Instructions Efficient Fine-Tuning of Parameters Reinforcement Learning from Human Response

Module 7: LLMs for Search, Prediction, and Generation Topics

Search Query Completion Next Word Prediction Word Embeddings Transformers
Generating Text
Stacking Attention Layers

Module 8: LangChain for LLM Application Development Topics

Understanding Retrieval-Augmented Generation (RAG) Document Loading and Splitting
Vector Stores and Embeddings Retrieval
Question Answering with Chatbots Building RAG Models using LangChain

Module 9: Interacting with Data Using LangChain and RAG Topics

Understanding Retrieval-Augmented Generation (RAG) Document Loading and Splitting
Vector Stores and Embeddings Retrieval
Question Answering with Chatbots Building RAG Models using LangChain

Module 10: Generative AI on Cloud Topics

Cloud Computing Foundations AWS S3
Amazon EC2 Trn1n Amazon EC2 Inf2
Amazon Sagemaker
Amazon CodeWhisperer Amazon Bedrock
Azure OpenAI

Module 11: Working with ChatGPT Topics

Introduction to ChatGPT
Leveraging ChatGPT for Productivity Mastering Excel through ChatGPT
Becoming a Data Scientist using ChatGPT Data Analysis in PowerBI with ChatGPT
Creating a Content Marketing Plan
Social Media Marketing using ChatGPT Keyword Search and SEO using ChatGPT Generating Content using ChatGPT
Implementing ChatGPT for Customer Service ChatGPT for Developers
ChatGPT for Creating Programs ChatGPT for Debugging
ChatGPT for Integrating New Features ChatGPT for Testing
Documenting the Code with ChatGPT

Module 12: Python with Generative AI Topics

Python Code Generation with Generative AI Gen AI Tools for Coding
Advanced Code Optimization with ChatGPT Gen AI Tool Coding with ChatGPT
Building an Application in Python with ChatGPT

Module 13: Evaluating LLM Performance Topics

LLM Performance Comparison Perplexity
BLEU Score Human Evaluation
Choosing the Right Metrics Interpreting the Results

Module 14: Industry Case Studies and In-class Project Topics

In-class Project: AI-Powered Text and Image Generator Case Study: Generative AI for Personalized Learning
Case Study: Generative AI for Creative Content Generation Case Study: Generative AI for Business

Module 15: Bonus Module - Machine Learning with Generative AI

Artificial Intelligence Essentials Disciplines of AI
Types of AI
Machine Learning Fundamentals Predictive ML Models
ML Algorithms: Deep Dive Supervised Learning Unsupervised Learning
Semi-Supervised Learning Reinforcement Learning

Module 16: Bonus Module - Generative AI Tools Topics:

Hugging Face Transformers OpenAI GPT3 API
Google Cloud AI Platform Midjourney
DALL E- 2
Google Bard

Data Science with Generative AI Online Training– 2025 Updated

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