Data Warehouse Basics OLTP vs OLAP What is a Data Warehouse? ETL vs ELT Concepts Data Lake vs Data Warehouse Data Modeling Dimensional Modeling ER Modeling vs Star Schema vs Snowflake Schema Fact Table & Dimension Table Schemas Star Schema Snowflake Schema Advantages and Disadvantages Normalization & Denormalization 1NF 2NF 3NF Slowly Changing Dimensions (SCD) Fact Table Types Additive Facts Semi Additive Facts Factless Facts Additive Facts Semi Additive Facts Factless Facts Dimension Types Conformed Dimension Junk Dimension Degenerate Dimension Role Playing Dimension ETL Concepts ETL Overview Extraction, Transformation, Loading Steps ETL Pipeline Design Principles Introduction to MySQL MySQL Architecture Client Server Model Storage Engines (InnoDB vs MyISAM) Installation & Setup Database Basics Database & Table Creation Datatypes Constraints (Primary Key, Foreign Key, Unique, Not Null, Default, Check) Basic SQL Queries SELECT Statement Filtering with WHERE ORDER BY DISTINCT LIMIT / OFFSET Functions and Operators Aggregate Functions (SUM, AVG, COUNT, MIN, MAX) String Functions Date & Time Functions Mathematical Functions CASE WHEN Logic Joins and Set Operations INNER JOIN LEFT JOIN RIGHT JOIN FULL OUTER JOIN CROSS JOIN UNION UNION ALL INTERSECT / EXCEPT Subqueries & Nested Queries Scalar Subqueries Correlated Subqueries EXISTS vs IN vs JOIN Advanced SQL Queries Common Table Expressions (CTE) Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, NTILE) Recursive CTE Derived Tables Framing Partition By Data Manipulation (DML) INSERT INSERT INTO SELECT UPDATE DELETE Data Definition (DDL) CREATE ALTER DROP TRUNCATE Transaction Management COMMIT ROLLBACK Views CREATE VIEW CREATE OR REPLACE VIEW Indexes Clustered Index (Primary Key) Non-Clustered Index (Secondary Index) Composite Indexes BTree Hash Index Stored Procedures (PL/SQL equivalent) CREATE PROCEDURE IN, OUT, INOUT Parameters Control Flow (IF, CASE, WHILE, LOOP, REPEAT) Error Handling Functions CREATE FUNCTION RETURN Statement Deterministic vs Non-Deterministic Functions Triggers BEFORE INSERT/UPDATE/DELETE AFTER INSERT/UPDATE/DELETE Use Cases and Best Practices Cursors DECLARE Cursor OPEN, FETCH, CLOSE

Data Preparation using Tableau Prep Data Visualization Business Intelligence tools Introduction to Tableau Tableau Architecture VizQL Introduction to Tableau Prep Tableau Prep Builder User Interface Data Preparation techniques using Tableau Prep Builder tool Data Connection with Tableau Desktop Features of Tableau Desktop Data Connection with Tableau Desktop Connect to data from File and Database Types of Connections Joins and Unions Data Blending Tableau Desktop User Interface Basic project: Create a workbook and publish it on Tableau Online Basic Visual Analytics Visual Analytics Basic Charts: Bar Chart, Line Chart, and Pie Chart Hierarchies Data Granularity Highlighting Sorting Filtering Grouping Sets Calculations in Tableau Types of Calculations Built-in Functions (Number, String, Date, Logical and Aggregate) Operators and Syntax Conventions Table Calculations Level Of Detail (LOD) Calculations Using Python within Tableau for Calculations Advanced Visual Analytics Tool tips Trend lines Reference lines Forecasting Clustering Level Of Detail (LOD) Expressions in Tableau Use Case I - Count Customer by Order Use Case II - Profit per Business Day Use Case III - Comparative Sales Use Case IV - Profit Vs Target Use Case V - Finding the second order date Use Case VI - Cohort Analysis Geographic Visualizations in Tableau Introduction to Geographic Visualizations Manually assigning Geographical Locations Types of Maps Spatial Files Custom Geocoding Polygon Maps Web Map Services Background Images Advanced Charts in Tableau Box and Whisker’s Plot Waterfall Chart Bump Chart Word Cloud Donut Chart Dashboards and Stories Introduction to Dashboards The Dashboard Interface Dashboard Objects Building a Dashboard Dashboard Layouts and Formatting Interactive Dashboards with actions Designing Dashboards for devices Story Points Get Industry Ready Tableau Tips and Tricks Choosing the right type of Chart Format Style Data Visualization best practices Prepare for Tableau Interview Exploring Tableau Online Publishing Workbooks to Tableau Online Interacting with Content on Tableau Online Understand Scheduling Managing Permissions on Tableau Online Data Security with Filters in Tableau Online

Overview of Data Analytics Overview of Data Analytics Power BI Installation Power BI Installation Explore the Dynamic User Interface of PowerBI Explore the Dynamic User Interface of PowerBI Power Query Power Query Functions Introduction to Basic Functions Text Functions Date Functions Number Functions Conditional Column and Column From Examples Introduction to Conditional Columns and Column from Examples Conditional Column in Power BI Power Query Column from Examples in Power BI Power Query Appending or Combining Files Combining CSV Files Consolidate Files with Different Structures Replacing VLOOKUP with Merge Queries Introduction to Replacing VLOOKUP Introduction to Merge Queries Advanced Merge Queries Challenge with Merge Queries Powerful Cleaning Features in Power Query Introduction to Powerful Cleaning Features in Power BI Power Query Data Cleaning in Power BI Power Query Changing Structure of Data in Power BI Power Query (Unpivot) Working with Rows and Columns in Power Query Introduction to Working with Columns & Rows Working with Rows & Columns in Power BI Power Query Various Data Sources in Power BI Get SQL Data in Power BI Get Live Website Data in Power BI Get Email Data in Power BI Get Google Forms (Google Sheets) Data in Power BI Get Power BI Datasets Share Point Data in Power BI Get JSON Files in Power BI Working with Filters and Parameters Introduction to working with Filters, Sorting and Parameters Working with Filter, Parameters and Sorting in Power BI Power Query Custom Function Feature to Clean and Combine Files Introduction to Custom Function to Clean & Combine Files Clean & Combine CSV Files In Power BI Power Query Clean & Combine Excel Files In Power BI Power Query Finding Out Postal Codes from States and Uts Finding Out Postal Codes from States and Uts M Functions Introduction to M Language M Functions Date - IsIn M Functions Date_ Add and Subtract M Functions - Date - Basic M Functions - Text Write a Simple M Code Trick to get help for 800+ M Functions Power BI Report View Introduction to PowerBI Report View Working with Basic Visualization in PowerBI Introduction to Basic Visualization in Power BI Basic Charts in Power BI Formatting of Visuals in Power BI Visualize Spatial Data by Maps Introduction to Visualize Spatial Data by Maps Working With Maps In Power BI Geo Styling Formatting of Pages and Report Introduction to Formatting of Pages & Reports Page Perfection Working With Themes Textualize Your Data in Power BI Introduction to Textualize Your Data in Power BI Matrix Mastery Conditional Formatting In Textual Data Hierarchies In Matrix Filters and Slicers in Power BI Introduction to Filters & Slicers in Power BI Filters On Different Data Types Introduction To Filters Slicers In Power BI Formatting of Slicers Cards and KPIs Introduction to Cards & KPIs in Power BI Numeric Cards Textual Cards In Power BI Using Actions to Enhance Data Visualization in Power BI Introduction to Using Actions to Enhance Data Visualization Elements in Power BI Actions in Power BI Bonus in Power BI Artificial Intelligence in Business Intelligence Q & A in Power BI Decomposition Tree in Power BI Smart Narrative in Power BI Explain the increase & decrease feature in Power BI Get Quick Insights in Power BI Misc Features Clear all Slicers & Apply all Slicers On Object Interaction Customize Pane Switcher Option New Card in Power BI Power BI Services Introduction to PowerBI Services Introduction to Publishing The Report To Power BI Service Publishing Report To Power BI Online Sharing & Collaboration of Power BI Reports Exporting the Report Introduction to Exporting the report Creating a Dashboard in Power BI Introduction to Creating a Dashboard in Power BI Service Creating a Dashboard In Power BI Service Dashboard Options Capstone Projects FMCG Sales Report Banking Report Healthcare Report HR Report Introduction to Power Pivot Introduction to Power Pivot Power BI DAX Introduction Text Function Data Function Logical Functions Calculated Column Vs. Measures Implicit Measures Vs. Explicit Measures Calculate Function PREVIOUSDAY - Calculate Time Intelligence Functions NEXTDAY - Calculate Time Intelligence Functions Percentage Difference SAMEPERIODLASTYEAR DATEADD MTD, QTD and YTD using Quick Measures Concatenated List of Values Star Rating using Quick Measures Filter Function All Function SUMMARIZE Top N CALENDARAUTO

Python Installation and Introduction Python Installation - Various ways Introduction To Python Features of Python Python Identifiers Reserved Words Python Data Types Slice operator Type casting Mutable and Immutable concepts Detailed discussion on Python Collection Datatypes Operators in Python Arithmatic Operators Relational or Comparision Operators Logical Operators Equality Operator Assignment Operator Special Operator Input and Output Input Streams Command Line Arguments Output Streams Flow Control Conditional Statements Iterative Statements Transfer Statements Pass Statement String Functions String Slice Operator Mathematical Operators in String String Functions Functions and Lambda Functions Functions Recursive Functions Lambda Functions Function Decorator Function Generator Modules Random Modules Packages Exception Handling Exception Handling Loggin Exceptions Assertions in Exception File Handling File Handling Pickling and Unpickling Regular Expression Regular Expression Regular Expression using web scraping Database Connectivity Database Connectivity PyMySQL with MySQL PyMySQL with MySQL Numpy Numpy Pandas Pandas Data Visualization Matplotlib Seaborn Data Preprocessing Missing Value Treatment Outlier Treatment Feature Scaling Encoding Streamlit Frontend using Streamlit

Introduction to ChatGPT for Data Analytics Why use ChatGPT for Data Analytics? Capabilities & Limitations Types of Tasks it can handle Prompt Engineering for Analysts What is a Prompt? Zero-shot, One-shot, Few-shot prompting How to write better prompts for data analysis Iterative prompting SQL Code Generation Generating SQL queries from business questions Query optimization suggestions Complex join creation Data cleaning queries Python Code Generation (Pandas/Numpy) Generating Pandas code Data cleaning using ChatGPT Feature engineering suggestions Regex pattern generation for cleaning Data Visualization Assistance Chart recommendations Matplotlib, Seaborn, Plotly code generation Insight summarization from charts Documentation & Reporting Automation Auto-generating data dictionaries Writing executive summaries Auto-generating business insights from data Business Communication Support Email drafts for reporting findings Presentation script generation Simplifying technical outputs for business users ChatGPT Integration with Tools ChatGPT with Python (OpenAI API) Auto-generation of dashboard summaries Brand Yourself Resume Building LinkedIn Posts

12 Month’s PG Certification
in Data Science With Generative AI.

Q: Module 5 : Statistics

Introduction to Statistics Why Statistics in Data Analytics? Types of Data (Qualitative vs Quantitative) Descriptive Statistics Measures of Central Tendency (Mean, Median, Mode) Measures of Dispersion (Range, Variance, Standard Deviation, IQR) Coefficient of Variation Skewness & Kurtosis Exploratory Data Analysis Univariate Analysis Bivariate Analysis Correlation (Pearson, Spearman) Covariance Outlier Detection (Z-score, IQR method) Missing Value Analysis Correlation and Regression Simple Linear Regression Multiple Linear Regression Assumptions of Regression R-squared & Adjusted R-squared Multicollinearity (VIF)

In our data-driven and technological age, businesses are looking to utilize these technological advancements in the day to day work and looking for individuals with a robust understanding of data science as they are more valuable than ever.

Program Key Strengths :

Enquiry form

Program Overview at a Glance

Delivery Type

Online Live Interactive

Learning Hours

340+ Hrs ( 12 Months )

Program Fee

1,65,000/- + GST

Starting on

06 December 2025

Batch Days

Weekends + 1 Weekday

Program description

About Program Description

Master Python, SQL, Statistics, Machine Learning, and data science frameworks through real-world applications.
Learn how to integrate tools like ChatGPT, GPT-4, and other LLMs to streamline data science workflows and automate complex tasks.
Build and deploy intelligent models, uncover hidden patterns, and present data stories with the power of AI.
Get hands-on with data wrangling, feature engineering, model building, and AI-powered visualization.
Grasp essential Machine Learning concepts and apply them using AI-enhanced methodologies for greater efficiency.
Understand core Machine Learning concepts and apply them using AI-assisted methods.
Work on industry-relevant capstone projects that combine classical data science with modern Generative AI techniques.
Designed for freshers and working professionals aspiring to lead in data science, machine learning, and AI roles.
Delivered by industry experts with a strong focus on practical tools, real-world datasets, and career readiness.
Become a next-generation Data Scientist equipped with cutting-edge skills in data science, AI, and intelligent automation.

Eligibility

Whether you're from Engineering, Commerce, Statistics, or Science, you’re eligible to join this data science journey.
Ideal for those starting out or seeking to upskill and transition into data science roles.
From Sales and HR to Finance, Healthcare, E-Commerce, Logistics, and beyond – data science adds value everywhere.
The program starts from scratch with beginner-friendly, hands-on training.
All you need is a desire to learn; we’ll equip you with the skills to succeed in data science and AI.

Your Road to Mastery Starts Now

Program Roadmap – Built Just for You

Module 1 : MySql

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
CROSS JOIN
UNION
UNION ALL
INTERSECT / EXCEPT

Module 2 : Tableau

Data Visualization
Business Intelligence tools
Introduction to Tableau
Tableau Architecture
VizQL
Introduction to Tableau Prep
Tableau Prep Builder User Interface
Data Preparation techniques using Tableau Prep Builder tool

Features of Tableau Desktop
Data Connection with Tableau Desktop
Connect to data from File and Database
Types of Connections
Joins and Unions
Data Blending
Tableau Desktop User Interface
Basic project: Create a workbook and publish it on Tableau Online

Visual Analytics
Basic Charts: Bar Chart, Line Chart, and Pie Chart
Hierarchies
Data Granularity
Highlighting
Sorting
Filtering
Grouping
Sets

Types of Calculations
Built-in Functions (Number, String, Date, Logical and Aggregate)
Operators and Syntax Conventions
Table Calculations
Level Of Detail (LOD) Calculations
Using Python within Tableau for Calculations

Use Case I - Count Customer by Order
Use Case II - Profit per Business Day
Use Case III - Comparative Sales
Use Case IV - Profit Vs Target
Use Case V - Finding the second order date
Use Case VI - Cohort Analysis

Introduction to Geographic Visualizations
Manually assigning Geographical Locations
Types of Maps
Spatial Files
Custom Geocoding
Polygon Maps
Web Map Services
Background Images

Introduction to Dashboards
The Dashboard Interface
Dashboard Objects
Building a Dashboard
Dashboard Layouts and Formatting
Interactive Dashboards with actions
Designing Dashboards for devices
Story Points

Module 3 : Power BI

Get SQL Data in Power BI
Get Live Website Data in Power BI
Get Email Data in Power BI
Get Google Forms (Google Sheets) Data in Power BI
Get Power BI Datasets
Share Point Data in Power BI
Get JSON Files in Power BI

Introduction to M Language
M Functions Date - IsIn
M Functions Date_ Add and Subtract
M Functions - Date - Basic
M Functions - Text
Write a Simple M Code
Trick to get help for 800+ M Functions

Introduction
Text Function
Data Function
Logical Functions
Calculated Column Vs. Measures
Implicit Measures Vs. Explicit Measures
Calculate Function
PREVIOUSDAY - Calculate Time Intelligence Functions
NEXTDAY - Calculate Time Intelligence Functions
Percentage Difference
SAMEPERIODLASTYEAR
DATEADD
MTD, QTD and YTD using Quick Measures
Concatenated List of Values
Star Rating using Quick Measures
Filter Function
All Function
SUMMARIZE
Top N
CALENDARAUTO

Module 4 : Python

Python Installation - Various ways
Introduction To Python
Features of Python
Python Identifiers
Reserved Words
Python Data Types
Slice operator
Type casting
Mutable and Immutable concepts
Detailed discussion on Python Collection Datatypes

Functions
Recursive Functions
Lambda Functions
Function Decorator
Function Generator
Modules
Random Modules
Packages

Module 5 : Statistics

Module 6 : Machine Learning

1. Why Logistic Regression is for Classification
2. Sigmoid Function and Decision Boundary
3. Cost Function (Log Loss)
4. Regularization in Logistic Regression
5. Model Evaluation (Confusion Matrix, ROC, AUC)
6. Case Study: Customer Churn Prediction

Module 7 : ChatGPT

Module 8 : Advance Excel

Module 9 : Deep Learning & AI

History of Neural networks and Deep Learning.
How Biological Neurons work?
Growth of biological neural networks.
Diagrammatic representation: Logistic Regression and Perceptron.
Multi-Layered Perceptron (MLP).
Notation.
Training a single-neuron model.
Backpropagation.
Activation functions.
Vanishing Gradient problem.
Bias-Variance tradeoff.

Deep Multi-layer perceptrons:1980s to 2010s
Dropout layers & Regularization.
Rectified Linear Units (ReLU).
Weight initialization.
Batch Normalization.
Optimizers:Hill-descent analogy in 2D
Optimizers:Hill descent in 3D and contours.
SGD Recap
Batch SGD with momentum.
Nesterov Accelerated Gradient (NAG)
Optimizers:AdaGrad
Optimizers : Adadelta andRMSProp
Adam
Which algorithm to choose when?
Gradient Checking and clipping
Softmax and Cross-entropy for multi-class classification.
How to train a Deep MLP?

Biological inspiration: Visual Cortex
Convolution:Edge Detection on images.
Convolution:Padding and strides
Convolution over RGB images.
Convolutional layer.
Max-pooling.
CNN Training: Optimization
Receptive Fields and Effective Receptive Fields
ImageNet dataset.
Data Augmentation.
Convolution Layers in Keras
AlexNet
VGGNet

Why RNNs?
Recurrent Neural Network
Training RNNs: Backprop
Types of RNNs
Need for LSTM/GRU
LSTM
GRUs

Your Future in Data Starts Here – Click to Transform!

Note* : Certain Modules will be delivered in Self-Paced Video mode and Confirmation for the same will be communicated in your admission letter .

Tools You Will Learn

Essential Tools You Will Gain Proficiency In

Learn What Employers Want – Data Science with Gen AI = Your New Edge!

Note* : Few tools will be in self-paced video mode

Capstone Projects

Industry Collaboration Projects

Amazon Fashion Discovery Engine Learn to build a recommendation engine which suggests similar apparel products to a given apparel product on any e-commerce websites such as Amazon.

Ecommerce Domain : Ecommerce

Tag predictions for questions Learn to develop a high-performance automated tagging system to aid topic discovery in Stack Overflow, a popular online community to learn and share knowledge.

Web analytics Domain : Web analytics

Cancer Prediction Learn to model the progression and treatment of cancerous conditions using patients medical records using supervised Machine learning Algorithms

Life Sciences and Health care Domain : Life Sciences and Health care

HR Analytics Employee Attrition & Performance. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’

HR Analytics Domain : HR Analytics

Machine Learning for Dynamic Pricing Machine Learning enhances dynamic pricing by continuously learning from data to identify patterns, detect changing demands, and adapt to market trendsultimately helping improve your KPIs.

Ecommerce Domain : Ecommerce

Sales conversion optimization. Social Media Ad Campaign marketing is a leading source of Sales Conversion and in this project learners will have to work on the optimization of the sales conversions

Campaign management Domain : Campaign management

Claim Prediction . The insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. The insurers use rather complex methodologies for this purpose

Banking and Financials Domain : Banking and Financials

Course Certificate

Recognized Certificate of Achievement

Upon successful completion of the program, participants will receive a Recognized Certificate of Achievement, serving as official validation of their dedication, knowledge, and skills acquired throughout the course. This certificate can be proudly showcased to employers, institutions, or peers as a testament to their accomplishment.

Fuel Your Career with Data Power!

Learning Path

Growth Through Learning

Potential Recruiters

Industries Potential Recruiter’s

Selection Process

Candidate Evaluation Process For Admission

Personal Interview with Academic Expert

Aptitude Test

Admission Letter & Enrollment

Success stories

Stories of Excellence

Testimonials

Experience Of Our Alumni

Geetika Puri Associate Consultant (Senior Data Analysts) at Capgemini

â€œI rarely come across real talents who stand out like Rahul. I had the pleasure of working with Rahul on Machine Learning algorithm specifically.

Don Nachtwey Data Warehouse Analyst at GDIT ( Washington)

Rahul was my course instructor for Tableau. I am pleased to gain knowledge and skills in such a powerful and flexible tool.

Mayur Korde

It was a very nice and quality experience to learn data science with The Scholar . The course is well designed and our trainer was experienced and good at presenting the concept clearly by presenting the real time examples.

Sanjay Yadav IT Analyst at TCS

I personally recommend data scientists or BI enthusiast to for this course. Rahul is very good at explaining each and every topic with so ease that learning becomes fun

Dinkar Ji Consultant

Rahul was my instructor for data science learning using python. His command over the subject is outstanding and he makes sure he is able to impart his knowledge.

SB Subhaprakash Entrepreneur In Residence - Wakefit Gift Cards | IIM Rohtak

I have attended Rahul's Tableau and Python sessions. I have to say, those sessions were real gem. Rahul's sessions were full of interactions and insightful.

Mahima K.

Rahul has played a major role in my journey of learning data science. His approach towards teaching makes it very conducive for the students to quickly understand the complex concepts.

Jagjit Hanspal Sr BI/ETL Consultant at Intact

Rahul was my Mentor for Tableau Certification Training. He has in depth knowledge in Tableau and is a brilliant teacher. I appreciate his help.

Rakshit Pophali

I started my data science journey with the guidance of The Scholar. The mentors from The Scholar taught me tableau, python and data science.

Shimelis Kitancho

The Scholar is a Data Science Experts : They are an excellent teacher, guide, expert, or master in the field.