Data Science/AI Masters

The most intense Data Analytics Course of 2025 with detailed teaching.

Last updated 07/2025

Best Seller

150 Hours

477 Lectures

English

Created by Dr. Satyajit Pattnaik, Become a Data Scientist/AI Engineer in just 3 months

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₹5,999.00 ₹9,999.00

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Description

The outburst of data is transforming businesses. Companies - big or small - are now expecting their business decisions to be based on data-led insight.

Data specialists have a tremendous impact on business strategies and marketing tactics.
The demand for data specialists is on the rise while the supply remains low, thus creating great job opportunities for individuals within this field.
Today, it is almost impossible to find any brand that does not have social media presence; soon, every company will need data analytics professionals.
This makes it a wise career move that has a future in business.

Job Roles after the course

This course will help you to step forward in Data Analytics and choose following roles

Data Scientist
AI Engineer
Gen AI Engineer
Data Analyst
Business Analyst
BI Analyst
BI Developer
Power BI Developer
Tableau Developer
and many more....

Syllabus: Click Here

Module 1: Introduction to the Course
Module 2: Python for Data Science
Module 3: Statistics for Data Science
Module 4: SQL
Module 5: Machine Learning
Module 6: Time Series Analysis
Module 6: Deep Learning
Module 7: NLP
Module 8: Transformers & Gen AI
Module 9: Deployment
Module 10: Power BI [Add-on]
Module 11: Tableau [Add-on]
Module 12: Data Engineering Basics [Add-on]
Projects: 20+ Industry level projects

FYI: This is a SELF PACED program that comes with 24/7 chat support, resume preparation guidance and interview prep materials

Any queries, reach out to Satyajit Pattnaik at +91 8237040802 [WhatsApp only]

Course Content

19 sections • 477 lectures

Welcome to the course!!

Course Resources

Join our Community

Let's install Python together

Google Colab, what's that?

Let's leverage chatGPT for help!!

Introduction to Python

Variables Keywords

Datatypes Operators

Loops Iteration

Control Structures

Data Vizualization

Descriptive Statistics

Inferential Statistics

Qualitative Data

Why Sampling is Important

Types of Sampling

Cluster Random Sampling

Probability Sampling

Non Probability Sampling

Population Sampling

Why n1 and not n

Measures of Central Tendency

Mode

Measures of Dispersion

Variance and Standard Deviation

Independent Events

Cumulative Probability

Conditioanal Probability

Bayes Theorem 1

Bayes Theorem 2

Uniform Distribution

Binomial Distribution

Poisson Distribution

Normal Distribution 1

Normal Distribution 2

Calculating Probability with Z Score [part 1]

Calculating Probability with Z Score [part 2]

Calculating Probability with Z Score [part 3]

Correlation vs Covariance

File Server vs Client Server

Introduction to SQL

Constraints in SQL

Table Basic DDLs

Table Basics - DQLs

Table Basics - DMLs

Data Import Export

String Functions

Date Time Functions

Regular Expressions

Stored Procedures

Windows Function

SQL Python Connectivity

INTRO - Introduction

INTRO - Types of ML

INTRO - Use Cases Part 1

INTRO - Use Cases Part 2

PRE REQUISITE - Features

PRE REQUISITE - Train-Test-Split

PRE REQUISITE - Feature Scaling

PRE REQUISITE - Standardization

PRE REQUISITE - Normalization

PRE REQUISITE - Feature Encoding

PRE REQUISITE - Feature Encoding Practicals

REGRESSION - Regression

REGRESSION - Regression Metrics

REGRESSION - Regression Metrics (PRACTICALS)

REGRESSION - SLR

REGRESSION - MLR

REGRESSION LR Codes

REGRESSION - MLR Example

REGRESSION - Polynomial Regression

REGRESSION - Polynomial Regression Practicals

REGRESSION - Bias Variance Tradeoff

REGRESSION - Ridge Regression

REGRESSION - Lasso Regression

REGRESSION - Ridge and Lasso Regression Practicals

CLASSIFICATION - Classification

CLASSIFICATION - Types Of Classification

CLASSIFICATION - Log Loss

CLASSIFICATION - Confusion Matrix

CLASSIFICATION - AOC RUC

CLASSIFICATION - Classification Report

CLASSIFICATION - KNN

CLASSIFICATION - KNN Excel Example

CLASSIFICATION - Classification Practicals Part 1

CLASSIFICATION - KNN Code

CLASSIFICATION - Decision Tree

CLASSIFICATION - DT Example Entropy

CLASSIFICATION - DT Gini Index

CLASSIFICATION - DT Code

CLASSIFICATION - Visualizing DT

CLASSIFICATION - Random Forest

CLASSIFICATION - RF Code

CLASSIFICATION - Naive Bayes

CLASSIFICATION - SVMs Part 1

CLASSIFICATION - SVMs Part 2

CLASSIFICATION - Logistic Regression

CLASSIFICATION - Practicals So Far

CLASSIFICATION - Issues in Classification Part 1

CLASSIFICATION - Issues in Classification Part 2

CLASSIFICATION - Practicals

ENSEMBLE - Ensemble

ENSEMBLE - Bagging

ENSEMBLE - Bagging vs RF

ENSEMBLE - Bagging Practicals

ENSEMBLE - Bagging Reg Practicals

ENSEMBLE - Boosting

ENSEMBLE - Adaboost

ENSEMBLE - Gradient Boosting

ENSEMBLE - CF vs LF

ENSEMBLE - Cross Entropy

ENSEMBLE - XGBoost

ENSEMBLE - Practicals

CLUSTERING - Clustering

CLUSTERING - HC Practicals

CLUSTERING - Hierarchial Clustering

CLUSTERING - K Means Clustering

CLUSTERING - K means Clustering Practicals

CLUSTERING - Mean Shift Theory and Practicals

FEATURE ENGINEERING - Dimensionality Reduction

FEATURE ENGINEERING - RFE SFS

FEATURE ENGINEERING - RFE Practicals

FEATURE ENGINEERING - SFS

FEATURE ENGINEERING - CHI SQUARE TEST

FEATURE ENGINEERING - CHI SQUARE PRACTICALS

FEATURE ENGINEERING - PCA THEORY

FEATURE ENGINEERING - PCA PRACTICALS

FEATURE ENGINEERING - LDA THEORY

FEATURE ENGINEERING - LDA PRACTICALS

FEATURE ENGINEERING - kPCA & QDA

FEATURE ENGINEERING - kPCA & QDA PRACTICALS

HPO - HYPER PARAMETER OPTIMIZATION BASICS

HPO - MANUAL HPO

HPO - GRID VS RANDOM

HPO - MANUAL PRACTICALS

HPO - RSCV PRACTICALS

HPO - GSCV PRACTICALS

Introduction to TSA

Time Series vs Regression

Time Series Analysis

Anomaly Detection

Components of Time Series

Decomposition (Implementation)

Additive/Multiplicative Decomposition

Testing TS Stationarity

Introduction to Pre-Processing

Handle Missing Value

Handle Missing Value (Code)

Outlier Treatment

Sigma Technique

Feature Scaling

Standardization

Feature Scaling (Code)

Feature Encoding

Feature Encoding (Code)

Models - Algorithms

Models - ARIMA [part 1]

Models - ARIMA [part 2]

Models - AR Theory

Models - MA Theory

Models - ACF/PACF

Models - Find PDQ

Models - ARIMA (Code) [part 1]

Models - ARIMA (Code) [part 2]

Models - ARIMA (Final Code)

Models - Decomposition

Models - ACF/PACF

Models - Best Transformation

Models - Grid Search [part 1]

Models - Grid Search [part 2]

Models - Final Model

Models - FBProphet [part 1]

Models - FBProphet [part 2]

Models - FBProphet [part 3]

Models - Multi Variate TS Analysis

Models - FBProphet [Uni/Multi]

Introduction to Metrics

Forecasting Evaluation Metrics

Mean Squared Error

Root Mean Squared Error

Mean Absolute Percentage Error

Project 1 - Energy Forecasting #1

Project 1 - Energy Forecasting #2

Project 1 - Energy Forecasting #3

Project 2 - Stock Market Prediction #1

Project 2 - Stock Market Prediction #2

Project 2 - Stock Market Prediction #3

Project 2 - Demand Forecasting #1

Project 2 - Demand Forecasting #2

Project 2 - Demand Forecasting #3

Project 2 - Demand Forecasting #4

Project 2 - Demand Forecasting #5

Project 2 - Demand Forecasting #6

Introduction to DL

Understanding Deep Learning

What is a Neuron

Activation Funations

Linear Function

Sigmoid Function

Backpropagation and Forward Pass

Gradient Descent

CNN Steps in CNN

CNN What is CNN

CNN CNN Architecture Explained

CNN Image Augmentation

CNN Batch Size vs Iterations vs Epochs

CNN Code Implementation of CNN

CNN Model Summary & Parameters

CNN Hands on XRAY

Before we move on

Pre trained RAW

Pre trained Code

VGG16

MobileNet

Transfer Learning

Final Project + Streamlit

Intro to NLP - NLP Intro

Intro to NLP - Intro Part 2

Intro to NLP - NLP Key Challenges

Intro to NLP - Linguistics

NLP Basics - Case Folding

NLP Basics - SCR

NLP Basics - Handling Contractions

NLP Basics - Tokenization

NLP Basics - Stop Words Removal

NLP Basics - nGrams

NLP Basics - Vectorization

NLP Basics - Word Embeddings

NLP Basics - BoW

NLP Basics - BoW Practicals

NLP Basics - TFIDF

NLP Basics - TFIDF Practicals

NLP Basics PoS & NER

NLP Basics - NER Practicals

Word Embeddings - Word2Vec Intro

Word Embeddings - Word2Vec Part 2

Word Embeddings - Pretrained Word2Vec

Word Embeddings - Word2Vec INTUITION

Word Embeddings - Word2Vec 50Features

Word Embeddings - Word2Vec CBOW

Word Embeddings - Word2Vec SkipGrams

Word Embeddings - GloVe

Word Embeddings - fastText

Word Embeddings - Cosine Similarity

Neural Networks - LSTM Part 1

Neural Networks - LSTM Part 2 Architecture

Neural Networks - LSTM Part 3 Deep Dive Architecture

Neural Networks - LSTM Part 4 Pointwise Operations

Neural Networks - LSTM Part 5 forgetGate

Neural Networks - LSTM Part 6 inputGate

Neural Networks - LSTM Part 7 outputGate

Neural Networks - LSTM Part 8 Practicals

Neural Networks - LSTM Part 9 Practicals

Neural Networks - LSTM Part 10 Practicals

Neural Networks - GRU Part 1

Neural Networks - GRU Part 2

Neural Networks - GRU Part 3 resetGate

Neural Networks - GRU Part 4 updateGate

Neural Networks - GRU Part 5 Practicals

Neural Networks - Bi Directional RNN

Encoder Architecture

Contextual Embeddings

Decoder Architecture

Introduction to BERT

Configurations of BERT

BERT - Fine Tuning

BERT - Pre Tuning (Masked LM)

BERT - Input Embeddings

Introduction to GPT (Decoder-Only Architecture)

GPT Architecture

GPT Masked Multi Head Attention

LLM Basics - Tokens

LLM Basics - Context Window

LLM Basics - Prompt

LLM Basics - Prompt Engineering

LLM Basics - Prompt Tuning

LLM Basics - Prompt Structures

RAGs - Introduction

RAGs - What and Why?

RAGs - Use Cases

RAGs - Paper Explanation

RAGs - Architecture Explanation

RAGs - Detailed Architecture Walkthrough

RAGs - Practical Use Case

Prompt Engineering (Intro)

Types of Prompting

Few Shot Limitations

Chain of Thoughts Prompting

Vector Databases

Vector database vs Vector Index

How Vector databases work

Vector database Practicals

Model Overview: Ollama

Getting Started with Ollama

Testing Models with Ollama

Python Implementation: Ollama

RAG Systems with Ollama

RAG Systems with Ollama (Practicals)

Model Overview: LLM APIs

RAG Systems with xAI

RAG Systems with xAI (Practicals)

Introduction to Fine Tuning

RAGs vs Fine Tuning

When to use RAGs

PEFT and Quantization

LoRA (Phase I - Low Rank)

LoRA (Phase II - Adapters)

LoRA (Phase III - Low Rank + Adapters)

qLoRA (Quantized LoRA)

Introduction to RAG Evaluation

RAG Evaluation (BLEU)

RAG Evaluation (ROUGE)

RAG Evaluation (METEOR)

RAG Evaluation (PPL Score)

RAG Evaluation (PPL Score) Practicals

RAG Evaluation (BERTScore, BARTScore)

RAGAS Framework

RAG Evaluation (Practicals #1)

RAG Evaluation (Practicals #2)

RAG Evaluation (Practicals #3)

Flask Basic App

Model Building Breast Cancer

Flask App Breast Cancer

What is a Data Warehouse

Table Basics - DDLs

Data Warehouse Structure

Why do we need staging

What are Data Marts

Data Lake vs Data Warehouse

Elements of Datalake

House Price Prediction (Advanced)

Appliance Energy Prediction

Autralia Weather Prediction

Banking Term Deposit

Big Mart Sales Prediction

Bike Sharing Demand Prediction

Car Price Prediction

Cirrhosis Prediction

Credit Card Default Prediction

Heart Failure Prediction

Hospital Mortality

Income Data Classification

Loan Prediction

Migraine Classification

Policy Recommendation Systems

Prediction of Used Car Prices

Real Estate Analysis

Spanish Wine Quality

Stroke Prediction

Supply Chain Management

Water Quality Classification

ChatScholar (EdTech Project)

Research RAG Chat

Automated AI Claims Processing using Gen AI

Multi PDF Rag Chatbot built on Web Scraping

AI Career Coach (Part 1)

AI Career Coach (Part 2)

AI Career Coach (Deployment)

Sustainability Chatbot (GROK API)

Sustainability Chatbot (Ollama)

ML Interview #1

ML Interview #2

ML Interview #3

ML Interview #4

ML Interview #5

ML Interview #6

ML Interview #7

ML Interview #8

ML Interview #9

ML Interview #10

DL Interview #1

DL Interview #2

DL Interview #3

DL Interview #4

DL Interview #5

DL Interview #6

DL Interview #7

DL Interview #8

DL Interview #9

DL Interview #10

Gen AI Interview #1

Gen AI Interview #2

Gen AI Interview #3

Gen AI Interview #4

Gen AI Interview #5

Gen AI Interview #6

Gen AI Interview #7

Gen AI Interview #8

Gen AI Interview #9

Gen AI Interview #10

Interview Preparation

Assignment Details

Terms

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