Data Science Course in Delhi

Enroll in our Data Science Course in Delhi and get hands-on training in Advanced Excel, SQL, Power BI, Python, Statistics, and Machine Learning. We’ll help you land your dream job in this booming tech industry with hands-on projects and 100% placement support.

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Guaranteed Job Support 100% Assistance Till You Get Job

Flexible Learning Duration 6 Months + Life Time Access

Expert-Led Training Get Trained by Experts

Hybrid Training Format Classroom/Live Online

Next Batch: 21 Feb, 2026

Next Batch: 21 Feb, 2026

Overview
Features
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Certification
Curriculum
Projects
Reviews
FAQs

Data Science Course in Delhi Overview

Did you know Delhi NCR is set to create over 9,000 data science jobs by 2025? Get ready to seize this opportunity with Gyansetu’s Data Science Training in Delhi—designed for both beginners and professionals aiming to launch or level up their careers.

This certification program delivers hands-on learning in Python, SQL, Machine Learning, AI, Power BI, and statistical analysis through intensive, project-based training. You’ll develop the skills top employers demand, mastering the full data workflow—from data collection to cleaning, predictive modeling, and deployment.

What sets Gyansetu apart is the real-world impact: you’ll work on live industry projects, solve business-critical challenges, and build a robust portfolio aligned with market needs. Training is flexible, with weekday and weekend batches to fit your schedule.

Whether you’re a recent graduate or an experienced analyst, you’ll benefit from expert mentors, industry case studies, and capstone assignments tailored to actual business environments.

Start building your future in one of the world’s fastest-growing fields. Book your free demo session today and take the first step towards a rewarding data science career!

Were you aware that Delhi NCR is going to produce more than 9,000 data science jobs by 2025? Prepare to capture that opportunity with Gyansetu’s Data Science Training in Delhi—suitable for both novices and experienced practitioners wishing to kick start or advance their careers.

This course is project based training which provides you hands on experience in Python Programming, SQL, Machine learning, AI as well as knowledge and skills on power Bi (one of the advanced business intellegent tool to create visualisation) and statistical analysis. You will learn the skills that top companies demand, a process we designed with data engineers at top technology companies over two years. Dive in each day on Data Cleaning, Predictive Modeling, and Data Deployment skills.

What makes Gyansetu different is its real-world approach: you work on live industry projects, solve business critical problems and build a strong portfolio to match the market. Training is convenient, you can attend during weekdays or weekends.

Whether you are a recent graduate or an experienced analyst, you will receive the most effective hands-on training using real life industry case studies as well as sample capstone assignments based on actual business scenarios.

Begin constructing your future in one of the fastest-growing fields worldwide. Enroll in our free demo session now and sign-up for a rewarding career in Data Science today!

Why Choose Gyansetu’s Data Science Course In Delhi

Gyansetu provides data science training in Delhi with expert mentorship, industry partnerships, and placement support.

Learn from Veterans: Master data science under the guidance of industry experts boasting 10+ years of real-world experience.
Career-Ready Placement Support: Benefit from 100% placement assistance, including professional resume building and exclusive access to 200+ hiring partners.
Real-World Expertise: Build a high-impact portfolio with 15+ live industry projects that bridge the gap between theory and practice.
Personalized Mentorship: Our small batch sizes (5-10 students) ensure you get the individual attention and personalized feedback you deserve.
Flexibility for Professionals: Choose from weekday or weekend batches with both online and classroom options to fit your busy schedule.
Prestigious Certification: Earn a NASSCOM & MSME registered certification recognized by top MNCs and global startups.
Lifetime Resource Hub: Gain unlimited, lifetime access to all learning materials and recorded sessions for continuous upskilling.
24/7 Support Ecosystem: From dedicated doubt-clearing sessions to free software installation and technical support, we’ve got you covered.
Affordable & Accessible Learning: Start your data science journey without financial stress thanks to our accessible fees and flexible EMI options.
Comprehensive Skill Development: Go beyond technical skills with specialized training in soft skills, interview preparation, and professional etiquette to ensure you’re truly job-ready.

data-science-course-in-delhi

Key Highlights of Data Science Course

100% Placement Support

Free Course Repeat Till You Get Job

Mock Interview Sessions

1:1 Doubt Clearing Sessions

Flexible Schedules

Real-time Industry Projects

Placement Stats in Delhi

Maximum salary hike

170%

Average salary hike

80%

Our Data Science Alumni in Top Companies

Data Science Course Placement Highlights

Shefali Agarwal

80 % Hike

Lead Analyst

Evalueserve

Analytics Specialist

Accenture

Ankit

100 % Hike

MCA

Fresher

Associate Consultant

Ernst & Young

Batches Timing for Data Science Course in Delhi

Track	Weekdays (Tue-Fri)	Weekends (Sat-Sun)	Fast Track
Course Duration	5 Months	6 Months	45 Days
Hours Per Day	2-3 Hours	3-4 Hours	6 Hours
Training Mode	Classroom/Online	Classroom/Online	Classroom/Online

Data Science Certification Course in Delhi

Upon successfully completing our Data Science certification course in Delhi, you will receive an industry-recognized certificate validating your expertise in Python, Machine Learning, SQL, Power BI, and Statistics. This certificate can be shared on LinkedIn, your resume, and your portfolio to showcase your skills to recruiters.

Industry Recognition: Our certification is valued by top employers across IT, finance, healthcare, and e-commerce.
Authentic & Verifiable: Each certificate includes unique credentials, ensuring authenticity.
Career Impact: Certified professionals command higher salaries, faster promotions, and improved interview shortlisting rates.

Enquire Now

Data Science Course Curriculum

The data science course curriculum covers the major phases of the data science lifecycle with a practical approach.

SQL

Database Management & Query Optimization Master database fundamentals, SQL operators, advanced functions, complex subqueries, views, multiple table joins, and query optimization techniques for efficient data extraction and manipulation.

13 Modules

SQL Certification Course

Introduction 5 Topics

File Management System and Its Drawbacks
Overview of Database Management System (DBMS) and Data Models
Physical Data Models
Logical Data Models
Entity-Relationship (E-R) Model

Introduction to Sql Server 10 Topics

Advantages and Drawbacks of SQL Server Compared to Oracle and DB2
Connecting to Server
Authentication Modes
SQL Server Authentication Mode
Windows Authentication Mode
Login and Password
SQL Server Management Studio and Tools in Management Studio
Object Explorer
Object Explorer Details
Query Editor

Introduction to tsql 19 Topics

Types Of TSQL Commands
Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Query Language (DQL)
Data Control Language (DCL)
Transaction Control Language (TCL)
Database
Creating Database
Altering Database
Deleting Database
Constrains
Procedural Integrity Constraints
Declarative Integrity Constraints
Not Null, Unique, Default and Check constraints
Primary Key and Referential Integrity or foreign key constraints
Data Types In TSQL
Table
Creating Table
Altering/ Deleting Table

Data Manipulation Language 9 Topics

Insert
Identity
Creating A Table From Another Table
Inserting Rows From One Table To Another
Update
Computed Columns
Delete
Truncate
Difference Between Delete and Truncate

Data Query Language (DQL) 12 Topics

Select Clause
Where clause
Top clause
Order By Clause
Distinct Clause
Isnull() function
Column aliases
Predicates
Between … And
In
Like
Is Null

Built In Functions 13 Topics

Scalar Functions
Numeric Functions
Character Functions
Conversion Functions
Date Functions
Aggregate Functions
Convenient Aggregate Functions
Statistical Aggregate Functions
Group By and Having Clauses
Super Aggregates
Over (partition by …) Clause
Ranking Functions
Common Table Expressions (CTE)

Set Operators 3 Topics

Union
Intersect
Except

Joins 9 Topics

Inner Join
Equi Join
Non-Equi Join
Self-Join
Outer Join
Left Outer Join
Right Outer Join
Full Outer Join
Cross Join

Sub Queries 7 Topics

Single Row Sub Queries
Multi Row Sub Queries
Any or Some
ALL
Nested Sub Queries
Co-Related Sub Queries
Exists and Not Exists

Indexes 4 Topics

Clustered Index
Non-Clustered Index
Create, Alter and Drop Indexes
Using Indexes

Security 4 Topics

Login Creation
User Creation
Granting Permissions
Revoking Permissions

Views 3 Topics

Purpose Of Views
Creating, Altering and Dropping Indexes
Simple and Complex Views

Transaction Management 4 Topics

Introduction
Begin Transaction
Commit Transaction
Rollback Transaction

Power BI

Business Intelligence & Data Visualization Learn interactive dashboard creation, best practices in visualization, DAX functions, and real-world business case studies demonstrating actionable insights.

12 Modules

Power BI Certification Course Curriculum

Introduction 7 Topics

Power BI Installation
Power BI Desktop
Data Imports into Power BI
Views in Power BI
Building a Dashboard
Publishing Reports
Creating Dashboard in Power BI

Components 8 Topics

Power Query
Power Pivot
Power View
Power Map
Power BI Service
Power BI & QA
Data Management Gateway
Data Catalog

Integration with Data Sources 8 Topics

Connect to DataSources
Power BI SQL Server Integration
Power BI MySQL Integration
Power BI Excel Integration
Python Integration with Power BI Desktop
Power BI Web Integration using APIs
Power BI Salesforce Integration
Power BI Microsoft Integration

Power Query Editor 14 Topics

Change Data type of column
Clean and Transform using Query Editor
Advanced Data sources and transformations
Cleaning irregularly formatted data
Combine Multiple tables
Clusters
Format dates
Groups
Hierarchies
Joins
Pivot table
Query Groups
Split Columns
Unpivot table

Data Models/ Relationship 14 Topics

How Data Model Looks Like
Database Normalization
Data Tables vs Lookup Tables
Primary Key vs Foreign Key
Relationships vs Merged Table
Creating Table Relationships
Creating Snowflake Schemas
Managing & Editing Relationships
Active vs Inactive Relationships
Relationship Cardinality
Many to Many
One to One
Connecting Multiple Data Tables
Filter Flow

Data Analysis Expressions (Dax) Functions 7 Topics

Introduction to DAX
DAX calculation types
Aggregate Functions
Date Functions
Logical Functions
String Functions
Trigonometric Functions

Charts 21 Topics

Area Chart
Bar chart
Card
Clustered Bar chart
Clustered Column chart
Donut chart
Funnel chart
Heat Map
Line Chart
Clustered Column and Line chart
Line and Stacked Column chart
Matrix
Multi-Row Card
Pie chart
Ribbon chart
Stacked Area char
Scatter Chart
Stacked Bar chart
Waterfall chart
Map
Filled Map

Filters 8 Topics

Slicer Basic
Filters
Advanced Filters
Top N Filters
Filters on Measures
Page Level Filters
Report Level Filters
Drill through Filters

Visualizations 12 Topics

Visuals in Power BI
Create and Customize simple visualizations
Combination charts
Slicers
Map visualizations
Matrixes and tables
Scatter charts
Waterfall and funnel charts
Gauges and single-number cards
Modify colors in charts and visuals
Z-Order
Duplicate a report page

Exploring Data 5 Topics

Power BI Service
QuickSights in Power BI
Create and configure a dashboard
Share dashboards with organizations
Install and configure personal gateway

Power BI and Excel 4 Topics

Using Excel Data in Power BI
Upload Excel Data to Power BI
Import Power View and Power Pivot to Power BI
Connect OneDrive for Business to Power BI

Power BI Publishing and Sharing 11 Topics

Introduction to content packs, security and groups
Publish Power BI Desktop reports
Print and export dashboards and reports
Manually republish and refresh your data
Introduction Power BI Mobile
Creating groups in Power BI
Build content packs
Use content packs
Update content packs
Integrate OneDrive for Business with Power BI
Publish to web

Python for Data Science

Programming & Automation Develop Python programming foundations, web scraping, NumPy for numerical computing, Pandas for data manipulation, exploratory data analysis, and automation scripts.

12 Modules

Python for Data Science Certification Curriculum

Introduction 6 Topics

Interpreted vs Compiled language
Introduction to Python
Advantages & Disadvantages of Python
Python Installation (Anaconda)
Applications of Python
“Hello World!” in Python

The Basics (DataTypes) 17 Topics

Variables
Keywords
Multiple Assignment
What are Datatypes?
Numbers
Strings
Indexing and Slicing
String Functions
Lists
List Functions
Dictionary
Dictionary Functions
Tuples
Sets
Type Conversion (Str, Int, Float, etc.)
Date & Time (Date, Time and DateTime classes in Python)
Operators (Arithmetic, Comparison, Logical, Assignment, Binary, Identity, Membership Operators)

Conditional Statements & Loops 9 Topics

If-Else statements
Relational Operators
Nested If/Else
Elif Statements
Logical Operators
For Loops
While Loops
Nested Loops
Pass, Break and Continue

Functions 7 Topics

User-defined functions
Unlimited parameters
Global and Local variables
Lambda functions
Map function
Sort function
Error and Exception Handling

Python Comprehensions 4 Topics

List Comprehensions
Dictionary Comprehensions
Set Comprehensions
Tuple Comprehensions (doesn’t exist)

Oops 6 Topics

Classes and Objects
Methods in Classes
Class Attributes
Instance Attributes
Inheritance
Overriding Methods

File Handling 5 Topics

Creating a file
Reading a file
Writing a file
Appending to file
Copying file

Web Scraping 7 Topics

Intro to requests
Intro to BeautifulSoup
Parsing HTML structure into soup
Data extraction from soup/HTML
Wikipedia scraping
News website scraping
Maps scraping

Database Integration 6 Topics

What is a database?
What is SQL (Structure Query Language)?
Introduction to MySQL
Installing MySQL
Connection MySQL with Python
CRUD operations

Multi-Threading 7 Topics

What is multi-threading?
Introduction to parallel processing
Intro to threads
Threading module
Creating threads using threading module
Synchronizing threads
Threads priority queue

Exploratory Data Analysis 4 Topics

Working with NumPy
Working with Pandas
DataFrames
Analyzing a dataset using NumPy and pandas

Natural Language Processing (NLP) 12 Topics

Intro to Natural Language Processing
Installing NLTK toolkit
Working with NLTK Toolkit
Sentence Segmentation
Word Tokenization
Parts-of-Speech Tagging
Lemmatization
Stop-word removal
Named Entity Recognition (NER)
Bag of words model
TF-IDF (Term Frequency – Inverse Document Frequency)
ML modelling

Statistics for Data Science

Foundation & Application Understand probability theory, descriptive statistics, hypothesis testing, normal distribution, sampling techniques, and statistical inference for data-driven decisions.

4 Modules

Statistics for Data Science Course Curriculum

Descriptive Statistics 5 Topics

Intro to Descriptive Statistics
Measures of Center: Average, Mean, Median, Mode
Measures of Dispersion: Range, Quartiles and Percentiles, Inter Quartile Range (IQR), Variance, Standard Deviation
Covariance and Correlation
Simpson’s Paradox

Inferential Statistics 8 Topics

Normal Distribution
Standard Normal Distribution
T- Distribution
Point Estimation
Confidence Intervals
P-Value
Hypothesis testing
Levels of Hypothesis Testing

Probability 7 Topics

Uses of Probability
Need of Probability
Basics of Probability
Binomial Distribution
Conditional Probability
Bayesian Inference
Bayes Rule

Regression Modelling 7 Topics

Linear Regression Analysis
Logistic Regression Analysis
Problem of Collinearity
WOE and IV
Residual Analysis
Heteroscedasticity
Homoscedasticity

Machine Learning in Python

AI & Predictive Modeling Study artificial intelligence and machine learning, supervised (regression, classification) and unsupervised (clustering, dimensionality reduction) learning, predictive models, and deploying ML solutions.

12 Modules

Machine Learning in Python Course Curriculum

Introduction to Machine Learning with Python 6 Topics

Python Revision (NumPy, Pandas, scikit learn, matplotlib)

What is Machine Learning?
Statistical Learning vs Machine Learning
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories

Model Evaluation Metrics 11 Topics

Accuracy
Precision
Recall
True Positive Rate / False Positive Rate
F1-Score
Confusion Matrix
ROC Curve
Area Under the ROC curve
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
R2 Score

Supervised Learning – Regression 5 Topics

What is Regression and its use cases?
Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression – ID3 and CART

Supervised Learning – Classification 11 Topics

What is Classification and its use cases?
Logistic Regression
Support Vector Machine (SVM)
Kernel SVM
Naïve Bayes
k-Nearest Neighbours (k-NN)
Decision Tree Classification – ID3 and CART Algorithms
Ensemble Learning
Bagging vs Boosting
AdaBoost, Gradient Boost, XGBoost
Random Forest

Hyperparameter Tuning 6 Topics

What is model tuning?
Overfitting vs Underfitting
Cross Validation
Bias and Variance
Hyperparameter Optimization
Grid Search vs Random Search

Dimensionality Reduction 6 Topics

What is Dimensionality Reduction?
Feature Selection
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Random Projection
ICA

Unsupervised Learning 9 Topics

What is Clustering & its Use Cases?
K-Means Clustering
K-Means++
How to do optimal clustering – Elbow Curve?
C-means Clustering
Hierarchical Clustering
DBSCAN Clustering
Gaussian Mixture Model
Clustering Model Evaluation Metrics – Silhouette Coefficient, Dunn’s Index, etc.

Association Rules Mining and Recommendation Systems 7 Topics

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How Recommendation Engines work?
Collaborative Filtering
Content Based Filtering

Reinforcement Learning 9 Topics

What is Reinforcement Learning?
Why Reinforcement Learning?
Elements of Reinforcement Learning
Exploration vs. Exploitation dilemma
Epsilon Greedy Algorithm
Markov Decision Process (MDP)
Q values and V values
Q – Learning
Values

Time Series Analysis 8 Topics

What is Time Series Analysis?
Importance and Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity

Natural Language Processing (NLP) 18 Topics

Intro to NLP
Setting up NLTK environment
What is NLTK Toolkit?
Text Processing
- Cleaning
- Normalization
- Tokenization
- Stop Word Removal
- Part of Speech Tagging
- Named Entity Recognition
- Stemming
- Lemmatization
Feature Extraction
- Bag of Words Model
- Count Vectorizer
- Term Frequency (TF)
Inverse Document Frequency (IDF)
- Word Embeddings

Introduction to Deep Learning 11 Topics

What is Deep Learning?
Curse of Dimensionality
Machine Learning vs. Deep Learning
Use cases of Deep Learning
Human Brain vs. Neural Network
What is Perceptron?
Learning Rate
Epoch
Batch Size
Activation Function
Single Layer Perceptron

Advanced Excel

Business Reporting & Analysis Use advanced formulas, pivot tables, data analysis tools, macros, and produce business reports.

15 Modules

Advanced Excel Course Curriculum

Workbook Protection & Security 7 Topics

Introduction to Excel
Password Protect a workbook/worksheet
Hiding & Unhiding Sheets
Protecting Workbook structure
Hiding sheets using VBA developer mode
Hiding Excel Functions
Locking/ Unlocking Cells

Data Types in Excel 8 Topics

Numbers
Text
Date Time
Currency
Percentage
Converting Numbers to Text
Converting Text to Numbers
Changing Date/Time formats

Scale up Speed using Short Keys 5 Topics

Working with Alt Key
Paste special
Insert Row/Column
Switch between worksheets
Navigate all excel features using Keyword

Conditional Formatting 6 Topics

Top Bottom Rules
Highlight Cell Rules
Data Bars
Color Scales
Exploring Rules Manager
Use formulas for cell formatting

Working on Data Tools 9 Topics

Text Filters
Number Filters
Sorting
Multi-level sorting
Text to Columns – Delimiters
Text to Columns – Fixed Width
Remove Duplicates
Data Validations
Grouping

Cell Referencing 3 Topics

Relative Referencing (A1)
Absolute Referencing ($A$1)
Partial Referencing ($A1) / (A$1)

Mathematical Functions 5 Topics

Sum/ Sumif/ Sumifs
Count/ Countif/ Countifs
SumProduct
Round/ Roundup/ Rounddown
Min/ Max/ Average

Logical Functions 5 Topics

If
And
Or
IsText
IsNumber

Lookup Functions 9 Topics

Vlookup
Vlookup with Match Function
Vlookup using (*) problems
Vlookup using Running Countif
Nested Vlookup
Hlookup
Index Match
Indirect
Offset

Text Functions 9 Topics

Char
Concatenate
Exact
Find/ Search
Left
Right
Proper
Mid
Upper

Date-Time Functions 5 Topics

Weekday
Weeknum
Day/ Month/ Year
Date / Time
Hour/ Minute/ Second

Array Functions 2 Topics

Learning functions with {}
Minif, Maxif, etc.

Charts 8 Topics

Line
Column
Pie
Bar
Combo Charts
Waterfall Charts
Doughnut Charts
Dynamic Charts using Pivot Slicers

Pivot Table & Charts 9 Topics

Pivot Table Fields
Filters/ Rows/ Columns/ Values
Insert Slicers
Insert Timelines
Group Selection
Changing Data Source
Report Layouts
Group Selection
Working with Multiple Tables using Relationship

Introduction to VBA programming 2 Topics

Record a Macro
Save macro sheet in .xlsm format

Industry Ready Data Science Course Projects

Get Real-World Experience

Customer Churn Prediction (Telecom / Banking)

Business Problem: High customer churn rates lead to significant revenue loss and increased acquisition costs. Companies struggle to proactively identify at-risk customers and implement timely retention strategies, resulting in missed opportunities to preserve their customer base.
Objective: To build a predictive model that accurately forecasts customer churn likelihood. This will enable the business to target at-risk customers with personalized retention offers, thereby reducing churn and maximizing customer lifetime value.
Tech Stack: Python, SQL, pandas, scikit-learn, XGBoost.

Recommendation Engine (E-commerce / OTT)

Business Problem: Generic content and product suggestions lead to low user engagement and missed cross-selling opportunities. Without personalized recommendations, users often struggle to discover relevant items, resulting in a poor experience and reduced platform loyalty.
Objective: To develop a recommendation system that provides personalized content or product suggestions. This system will enhance user engagement, increase session duration, and drive higher conversion rates.
Tech Stack: Python, pandas, scikit-learn, Collaborative Filtering, Content-Based Filtering.

Credit Risk & Loan Default Prediction (Banking)

Business Problem: Inaccurate assessment of loan applications exposes financial institutions to significant credit risk and potential losses from defaults. Manual or outdated risk models can lead to inconsistent lending decisions and financial instability.
Objective: To build a robust machine learning model that predicts the probability of loan default. This will empower the institution to make data-driven lending decisions, minimize financial risk, and maintain a healthy loan portfolio.
Tech Stack: Python, pandas, scikit-learn, Logistic Regression, XGBoost.

Business Problem: Manually sorting and routing a high volume of customer support tickets is inefficient and time-consuming. This delay leads to slower response times, decreased customer satisfaction, and increased operational costs.
Objective: To automate the categorization and routing of support tickets to the appropriate departments. This will accelerate issue resolution, improve team efficiency, and enhance the overall customer service experience.
Tech Stack: Python, NLTK, scikit-learn, TF-IDF, Bag-of-Words

Business Problem: Financial institutions face constant threats from fraudulent transactions, which result in direct financial losses and damage to customer trust. Traditional rule-based systems often fail to detect sophisticated and evolving fraud patterns in real time.
Objective: To develop a machine learning model capable of identifying potentially fraudulent transactions in real time. This will enable immediate intervention, reduce financial losses, and protect both the institution and its customers.
Tech Stack: Python, pandas, scikit-learn, Anomaly Detection Algorithms.

Business Problem: “One-size-fits-all” marketing campaigns are often ineffective, leading to low engagement, wasted marketing spend, and poor ROI. Businesses struggle to understand the diverse needs and behaviors within their customer base.
Objective: To segment customers into distinct groups based on their purchasing behavior and demographics. This will allow for the creation of targeted marketing campaigns that increase personalization, improve conversion rates, and optimize marketing spend.
Tech Stack: Python, pandas, scikit-learn, K-Means Clustering.

Business Problem: Patient no-shows result in wasted appointment slots, significant revenue loss for healthcare providers, and disruptions in clinic scheduling. This inefficiency prevents other patients from receiving timely care and reduces operational effectiveness.
Objective: To build a model that predicts the likelihood of a patient missing their scheduled appointment. This will enable clinics to implement proactive measures like overbooking or targeted reminders, thereby optimizing schedules and minimizing revenue loss.
Tech Stack: Python, pandas, scikit-learn, Logistic Regression.

480+

Hours of content

120+

Live sessions

Tools and software

Data Science Skills you can add in your CV

Data Science Tools Covered

What Sets This Program Apart?

GyanSetu

Other Courses

all-in-oneAll-in-One Toolkit

Complete roadmap: Gen AI, Python, Machine Learning, Deep Learning, Statistics, Power BI, SQL

Limited topics, outdated tools, no Generative AI or ML coverage.

progress-iconBeginner-to-Pro

Starts from scratch, no prior knowledge required, progressive skill-building.

Assume prior knowledge, leaving beginners confused and los

empoweredAI-Integrated Learning

Modern Data Science powered with Gen AI tools like ChatGPT, Gemini, Copilot, Midjourney & LLMs in every module.

No exposure to modern AI tools, LLMs, or data-driven automation.

focusedCareer-Focused Tracks

Choose your specialization –
✔ Data Scientist
✔ Machine Learning Engineer
✔ AI Engineer
✔ Data Analyst (bridge track included)

One-size-fits-all curriculum with zero career customization.

Real Industry Exposure

25+ hands-on industry projects + 2 live capstone projects using real company datasets.

Only 1–2 academic or theoretical projects with no real-world application.

expertiseLegacy & Expertise

Founded by IIIT-H alumni with 15+ years of tech, AI & training expertise.

Trainers with limited real-world data science or AI background.

practicePractice Over Theory

70% hands-on coding with ML models, datasets, AI pipelines & real problem-solving.

Primarily theory-based learning with limited practical implementation.

mentorshipExpert Mentorship

Learn directly from working Data Scientists & ML Engineers from Amazon, Deloitte, Accenture & top AI teams.

Generic trainers with no active industry involvement.

Who is this course for?

Aspiring Data Scientists/ AI Professionals
Software Engineers
Data Analysts
Career Changers
Statisticians
Academic Researchers and Educators

Career Assistance for Data Science Course in Delhi

Job Opportunities Guaranteed

Get a 100% Guaranteed Interview Opportunities Post Completion of the training.

Access to Job Application & Alumni Network

Get chance to connect with Hiring partners from top startups and product-based companies.

Mock Interview Session

Get One-On-One Mock Interview Session with our Experts. They will provide continuous feedback and improvement plan until you get a job in industry.

Live Interactive Sessions

Live interactive sessions with industry experts to gain knowledge on the skills expected by companies. Solve practice sheets on interview questions to help crack interviews.

Career Oriented Sessions

Personalized career focused sessions to guide on current interview trends, personality development, soft skill and HR related questions.

Resume & Naukri Profile Building

Get help in creating resume & Naukri Profile from our placement team and learn how to grab attention of HR’s for shortlisting your profile.

Top Companies Hiring for Data Science Course

Honours & Awards Recognition

GD Goenka University

Livpure

Denso International

ReverseLogix

BML Munjal University

Manav Rachna University

GLA University

DPGITM

GD Goenka University

FOR QUERIES, FEEDBACK OR ASSISTANCE

Contact Gyansetu Learner Support

+91 9999 201 478 +919999201478

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Our Learners Testimonials

Pawan Kumar

I joined Gyansetu institute for Data Science, faculty is brilliant here. Training got completed in around 6 months.

Shruti Aggarwal

Gyansetu's practical learning methodologies have been invaluable. Institute focuses on hands-on projects and industry-relevant curriculum equipped me with the skills needed to analyze complex datasets and derive actionable insights for my organization.

Divya Singh

I was passionate about data analysis it was an amazing experience learning Data Analytics from Gyansetu. Here the faculties helped me learn from the basics to advanced concepts and with the practice on real projects I was able to crack the job.

Self Assessment Test

Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals.

Frequently Asked Questions

Q1. What is the duration of the Data Science Course in Delhi at Gyansetu?

The duration of the course depends on which batch you choose. For weekdays it is 7 months, for weekends 9 months and for fast-track it is 60 days. This course includes projects, assignments and capstone work. After enrollment learners receive lifetime access to study material and recorded classes.

Q2. How much does the Data Science Certification Course cost in Delhi?

For the latest course pricing and current offers, please call us directly. Fees vary based on program duration and learning mode, and EMI options are available. The fee includes all study materials, software, project resources, placement support, and certification.

Q3. Do I need programming experience to join this Data Science Course?

No programming experience is required. The course starts with Python fundamentals and covers advanced topics gradually. Support is provided for both technical and non-technical backgrounds.

There are over 9,000 data science job openings in Delhi NCR. Placement assistance includes resume preparation, interview guidance, and introductions to hiring partners. Alumni work at TCS, Wipro, Accenture, Amazon, and startups. Entry-level data scientists earn ₹6-10 LPA.

Tools taught include Python (Pandas, NumPy, Scikit-learn, TensorFlow), SQL, Power BI, Tableau, Excel, Git, Jupyter Notebooks, and cloud platforms. The course covers statistical analysis, machine learning, deep learning, and deployment technologies.

Freshers start with salaries between ₹6-10 LPA. Professionals with 3-5 years earn ₹12-18 LPA, and those with over 5 years receive ₹20-30 LPA or more. Delhi NCR salaries are above the national average for this field.

Weekend batches are available for working professionals, spanning 9 months with classes on Saturdays and Sundays. Flexible timings and full curriculum coverage are provided. Recorded classes are available for missed sessions.

The course features 15+ projects in various domains. Examples include churn prediction, recommendation engines, fraud detection, and no-show prediction. Projects use real datasets and industry-standard workflows to help build a portfolio.

The certification is registered under NASSCOM and MSME and accepted by MNCs, startups, and consulting firms. It demonstrates ability in Python, Machine Learning, AI, and analytics. Certificates can be shared on professional profiles and resumes.

Applicants should have basic math knowledge and interest in data. The course welcomes graduates, working professionals, and those from commerce, arts, or science backgrounds. Programming is taught from basics so no technical prerequisite is needed.

Placement support includes resume and LinkedIn review, mock interviews, soft skills training, and introductions to over 200 hiring partners. The institute organizes hiring drives and shares job openings from network companies.

Yes. Non-technical participants are supported with extra help and foundational programming modules. The course is designed for learners from a variety of backgrounds, with emphasis on analytical thinking and data problem-solving.

Data Science involves machine learning, predictive modeling, and advanced statistics, while Data Analytics focuses on historical analysis and reporting. The course includes both analytics (Excel, Power BI, SQL) and advanced machine learning topics.

There are no hidden charges. The fee includes study materials, licenses, project resources, certification exams, and placement support. Software installation is included. Additional support is available at no extra cost.

The demand of data professionals is increasing rapidly in roles across various industries including IT, finance, e-commerce, health and consulting. Industry projections suggest 11 million data science positions in India by 2026, with Delhi as a primary hub.