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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Job Guarantee Program 100% Support Until You’re Hired

Course Duration 6 Months + Life Time Access

Expert Mentorship Learn from Industry Experts

Flexible Learning Available in Classroom/Online formats

Next Batch: 3 Oct, 2026

Overview
Features
Placements
Certification
Curriculum
Projects
Reviews
FAQs

Data Science Course in Delhi Overview

Gyansetu’s Data science course in Delhi is a NASSCOM and MMSE registered certification program that teaches you SQL, Python, Power BI, Machine learning, Statistics, Advanced Excel. Each course module includes real-world projects that prepare learners for data analyst and data scientist roles for tech and analytics sector in Delhi NCR.

In India, the demand for data professionals is growing quickly. As per the NASSCOM’s report State of Data Science and AI skills report puts the gap between open roles and qualified candidates for Machine Learning Engineers, data scientists and Data Architects at 60-73%. Gyansetu’s headquarter is in Sector 38, Gurgaon. The alumni of Gyansetu are working in top hiring companies like Accenture, TCS, Amazon, Wipro and more.

The curriculum covers six core areas. SQL is used for querying, structuring and managing relational data. Advanced Excel is used for data cleaning, analysis, formulas, pivot tables, and reporting. Python includes libraries like Pandas, Numpy and scikit-learn and is used for data analysis. Machine learning helps in model development. Power BI is used to build dashboards from processed data. Statistics provides the methods used to test and validate model outputs.

The batch size is small, usually of 5-7 students, across weekdays, weekends and fast-track in the form of online, offline and blended format. The course duration is usually 6 months for weekdays, 9 months for weekends and 60 days for fasttrack batches. We have expert-trainers with industry experience and learners get to work on real-world projects.

On completion of the course learners receive a certificate which is recognized by NASSCOM and can be added to a Linkedin profile or resume.

data-science-course-in-delhi

Key Highlights of Data Science Course

100% Placement Support

Unlimited Course Repeats

Mock Interviews

1:1 Doubt Sessions

Flexible Timing

Real Industry Projects

Placement Stats in Delhi

Maximum salary hike

135%

Average salary hike

75%

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	7 Months	9 Months	60 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

Gyansetu’s Data Science Certification Course in Delhi is a NASSCOM-accredited, MSME – registered credential which is awarded after completion of the course, projects and final assessment. This certificate proves the skills of learners in Python, SQL, Power BI, Machine Learning, Advanced Excel, Statistics.

Gyansetu’s certification is accredited by NASSCOM and registered under MSME. It is also approved by the Government of India for skill – training delivery.

Learners can add this certificate to their linkedin profile, resume or portfolio. Each certificate has a unique verification that can be checked to confirm the authenticity of the certificate.

The certification is issued after a learner completes all assigned projects, a capstone project based on a live dataset, and the course’s final assessment. Certification requirements are the same across the weekday, weekend, and fast-track batch formats: all three lead to the same NASSCOM- and MSME-registered credential.

The certificate is issued after the completion of the course, projects which are based on live dataset. Requirements for certification are the same across all the batches whether it is weekdays, weekends or fast-track.

Industry-Recognized Credential: The certificate is accredited by NASSCOM and registered with MSME and contains a unique ID.
Skill-Specific Validation: The certificate proves six core skills – Machine Learning, Python, Power BI, SQL, Statistics, Advanced Excel.
Consistent Across Batch Formats: The requirements for the certificate are same across all the batches whether its weekend, weekday or fast-track.

Data Science Course Curriculum

Gyansetu’s Data Science Course Curriculum in Delhi covers six core tracks, which includes SQL, Advanced Excel, Power BI, Python, Statistics, and Machine Learning. The course curriculum also includes real-world projects. Each track moves from foundational concepts to advanced concepts and tools used in real data workflows, not just a generalized syllabus.

SQL

Master the backbone of data work — from core database fundamentals and SQL operators to advanced functions, complex subqueries, multi-table joins, and views. You'll also learn query optimization techniques to extract and manipulate data faster, cleaner, and smarter than ever.

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

Statistics is the secret weapon of every top data scientist — sharpen yours with probability theory, hypothesis testing, normal distributions, sampling techniques, and statistical inference that turns raw data into real 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

Machine Learning in Python go beyond the basics — dive deep into AI and predictive modeling, covering supervised learning (regression & classification), unsupervised techniques (clustering & dimensionality reduction), and take your models all the way from training to real-world deployment.

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

Advanced Excel turns raw numbers into business decisions — master advanced formulas, pivot tables, macros, and powerful data analysis tools to produce professional reports that actually drive results.

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.

420+

Hours of content

105+

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

Complete Data Science Stack

✔ Python + Statistics
✔ Machine Learning
✔ AI & Deep Learning
✔ Generative AI tools

✘ Limited syllabus
✘ No AI integration

Beginner to Advanced Roadmap

✔ Starts from basics
✔ Step-by-step learning
✔ No coding required

✘ Assume prior knowledge
✘ No clear path

Hands-On Learning

✔ 70% practical training
✔ Real datasets
✔ Case studies & projects

✘ Theory-heavy
✘ Minimal practice

Real Industry Projects

✔ Live projects
✔ Capstone projects
✔ Business problem solving

✘ Academic projects
✘ No real exposure

Machine Learning & AI Focus

✔ ML models
✔ Predictive analytics
✔ AI-based solutions

✘ Basic ML theory
✘ No real implementation

Tools & Technologies

✔ Python, Pandas, NumPy
✔ Scikit-learn
✔ Power BI / Visualization
✔ AI tools

✘ Limited tools
✘ Outdated tech

Industry Mentors

✔ Data Scientists as trainers
✔ Real-world experience
✔ Doubt support

✘ Theoretical trainers
✘ No industry exposure

Placement Support

✔ 100% assistance
✔ Resume & interview prep
✔ Hiring support

✘ No placement support

Flexible Learning

✔ Weekday / Weekend
✔ Online + Offline
✔ Fast-track option

✘ Fixed schedule

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

Denso International

Luminous

Bharat Petroleum

Toshiba Midea

BryAir

GD Goenka University

Livpure

Denso International

ReverseLogix

BML Munjal University

Wedapt

Manav Rachna University

GLA University

DPGITM

GD Goenka University

FOR QUERIES, FEEDBACK OR ASSISTANCE

Contact Gyansetu Learner Support

+91 9999 201 478 +919999201478

4.8

4.7

5.0

Our Learners Testimonials

Vikalp

Data Science Intern

Honestly, I expected another theory-heavy course. Instead, we spent most of our time building projects and solving real datasets. That's what I enjoyed the most.

Vivek

Business Intelligence Analyst

The environment is very supportive. No matter how many questions I asked, I always received clear explanations. That made learning much less stressful.

Mohit Sharma

Machine Learning Engineer

I watched countless YouTube videos before joining, but nothing matched the structured learning at Gyansetu. Shalki Ma'am explains even complex Machine Learning algorithms with real-world examples. Instead of memorizing theory, we actually learned how to solve business problems using data.

Anjali Mehra

Junior Data Analyst

Coming from a commerce background, I honestly thought Data Analytics wasn't for people like me. But Sahil Sir, the founder and course mentor, explained every concept so patiently that learning SQL and Power BI never felt overwhelming. Whenever I doubted myself, the entire Gyansetu team encouraged me to keep going. Today, I confidently work with data and build dashboards. I'm really grateful for this journey.

Riya Kapoor

Data Scientist

Data Science always felt like a huge mountain to climb, especially because I came from a non-technical background. Shalki Ma'am broke every concept into simple, practical lessons that made learning enjoyable. The projects gave me confidence, and the constant motivation kept me going. Looking back, joining Gyansetu was the turning point in my career.

Sneha

Data Science Associate

The interview preparation and project guidance gave me the confidence I needed. Every assignment felt like something I'd actually do in a real job, which made learning much more meaningful.

Karan

Data Scientist

I had tried learning Data Science on my own, but everything felt confusing. After joining Gyansetu, the concepts finally started making sense. The structured curriculum and practical sessions helped me stay consistent, and I genuinely enjoyed learning throughout the course.

Aarav Malhotra

Junior Data Scientist

The course was practical from day one. Every project helped me understand how Data Science is used in real companies. I feel much more confident applying for jobs now.

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.

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Frequently Asked Questions

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

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

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

Q4. What are the placement opportunities after completing Data Science Training in Delhi?

Q5. Which tools and technologies are covered in Gyansetu's Data Science Course?

Q6. What is the average salary for Data Science professionals in Delhi?

Q7. Are weekend batches available for the Data Science Training in Delhi?

Q8. What kind of projects will I work on during the Data Science Course?

Q9. Is the Data Science Certification recognized by companies in Delhi?

Q10. What are the eligibility criteria for joining this Data Science Course in Delhi?

Q11. How does Gyansetu support job placement after the Data Science Course?

Q12. Can I switch to Data Science from a non-technical background?

Q13. What is the difference between Data Science and Data Analytics courses?

Q14. Are there any hidden charges beyond the Data Science Course fees?

Q15. What is the scope of Data Science careers in Delhi in 2026?

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