Best Data Science Course in Gurgaon

Enroll now in the best Data Science Course in Gurgaon and start your practical training in Advanced Excel, SQL, Power BI, Python, Statistics, Machine Learning. This program will be guided by industry experts with real-world projects and 100% placement guidance.

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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: 8 Aug, 2026

Overview
Features
Placements
Certification
Curriculum
Projects
Reviews
FAQs

Data Science Course in Gurgaon Overview

The need of data-driven professionals has never been higher, and businesses in the finance, healthcare, e-commerce and IT sectors are actively seeking to hire talented Data Scientists to make more informed business decisions. The World Economic Forum recently put Data Science and Analytics occupations in the top 10 fastest-growing professions by 2025, which means that it is one of the most secure and rewarding careers at the moment.

At our Data Science course in Gurgaon, we have developed a curriculum that will help you attain industry-relevant knowledge on Python programming, Data Visualization, Predictive Analytics, SQL, and Big Data tools. This program will take a more practical approach than theory-based courses by focusing on practical projects, case studies, and problem-solving tasks to prepare you to take up high-demand job positions.

By the end of the course, you will have not only acquired a firm knowledge base in data analytics and data science concepts, but also a portfolio of projects demonstrating your skills to hiring managers. This course is a clear guide to success whether you are a fresher looking to get your first job in Data Science or a working professional looking to change careers.

Under the guidance of industry experts and mentors, you will be taught tools such as Python, R, Tableau, Power BI, SQL, and TensorFlow and will have a full advantage in the competitive job market. Our career-oriented process prepares you to be interview-ready with mock sessions, resume-building and placement assistance.

Why Choose Gyansetu’s Data Science Course in Gurgaon?

At Gyansetu, we believe studying data science is not just a course, but a journey to real world success. We develop solid foundations, applied skills and self-confidence in your career. Here’s why we stand out:

Placement Assistance: We have a course on data-science in Gurgaon with complete placement services. We assist with professional resumes, simulated interviews and direct recruiter networking, so you can be prepared to face the job market.
Real Projects: We teach by doing. Case studies and real datasets that students handle reflect industry issues and allow them to feel confident applying their learning.
Affordable Fee Structure: We maintain costs are straightforward and transparent. We offer competitive and completely transparent fees so you can receive true value.
Flexible Learning Modes: Select the mode that suits you- classroom lessons in Gurgaon or live online training. It suits working people and full time students equally.
Expert Trainers: We have experienced advisors in the field of data analytics, machine learning and AI. They share practical insights and career‑focused advice.
Cutting Edge Curriculum: We update our curriculum on regular basis which helps the learners to be industry-ready.
Recognized Certification: Our certification is recognized globally which increases the chance of learners employability.
Recorded Lectures: We provide access to recorded lectures and study materials to our student which helps the students to learn at the their own pace.
Small Batch Size: We keep the size of our batches small to ensure that our students receive personalized attention.
Strong Industry Network: Students join a thriving community of successful alumni working across India’s leading tech companies.

data-science-course-in-gurgaon

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

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 Certification Course in Gurgaon

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 Professional Certification in Gurgaon

Upon completing the Data Science Course in Gurgaon, you’ll receive an industry-recognized certification that validates your expertise in Advanced Excel, Power BI, SQL, Statistics Python, Machine Learning, and AI-powered solutions. This credential demonstrates your mastery of essential tools and techniques, giving you a strong edge in today’s job market through real-world project experience.

Key benefits include:

Industry-Wide Recognition: Gain credibility with employers across diverse sectors, including healthcare, IT, and finance.
Expanded Career Opportunities: Showcase proven skills in Python, SQL, Power BI, statistics, machine learning and more.
Lifelong Validation: Benefit from permanent acknowledgment of your technical proficiency for ongoing career growth.

Data Science Course Curriculum

Gyansetu’s Data Science Master Certification training, a leading data science institute in Gurgaon, will provide deep knowledge in data analytics and data science. You will gain practical experience with real-world projects, using advanced skills in analyzing data and making data-driven decisions, taught throughout the course.

SQL

SQL Developer Certification course by Gyansetu aims to give you expert database management skills. In this course you’ll deep-dive into topics like SQL Operators, SQL Functions, Subqueries, SQL Views, Functions, Stored Procedures, User-defined Functions, SQL Optimization & Performance tuning, Database Objects Design & Implementation, Database Concurrency.

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

Gyansetu’s Power BI Certification Course will make you an expert in building interactive dashboards by working on real-world case studies. Learn from experts from the industry and become a Certified Power BI Professional.

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

Gyansetu’s Python Certification will start with the basic concepts of Python Programming and end with advanced topics in Python where we will cover Web Scraping, Numpy, Pandas, Data Exploration, Automation, API Integrations, etc.

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

Gyansetu’s Statistics for Data Science Certification will make you equipped with the concepts of Statistics like probability, Descriptive Statistics, Hypothesis testing, Normal distribution, and Sampling. This will help you further understand the machine learning concepts later.

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

Gyansetu’s Machine Learning course will help you learn the skills of AI and ML and make a career in this field by gaining in-depth knowledge of the concepts such as supervised learning, unsupervised learning, dimensionality reduction, Deep learning, Artificial intelligence, reinforcement learning and much more.

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

Take your spreadsheet skills to the next level with our Advanced Excel course, crafted for professionals aiming to excel in data management and analysis. Move beyond the basics as you master complex formulas, manipulate large datasets, and boost your efficiency.

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 Projects

Get Real-World Experience

Customer Churn Prediction

Business Problem: High churn impacts recurring revenue and customer lifetime value. Limited visibility into customer behavior makes it difficult to proactively prevent churn.
Objective: Predict customers likely to churn and identify key churn drivers. Enable data-driven retention actions by providing early churn risk signals to business teams.
Tech Stack: Python, SQL, pandas, scikit-learn, XGBoost

Recommendation Engine

Business Problem: Low user engagement and poor cross-sell opportunities limit revenue growth and customer retention.
Objective: Build a recommendation system that suggests relevant products or content based on user behavior, preferences, and interaction history to increase engagement and conversions.
Tech Stack: Python, collaborative filtering, content-based recommendation models

Credit Risk & Loan Default Prediction

Business Problem: Poor credit decisions increase NPAs and losses. Lack of data-driven risk assessment leads to inconsistent loan approvals and higher default rates.
Objective: Predict loan default probability and segment customers by risk. Support better lending strategies by providing explainable risk scores for credit and underwriting teams.
Tech Stack: Logistic regression, tree-based models, SQL

Business Problem: Manual review and routing of thousands of daily support tickets is slow, inefficient, and error-prone.
Objective: Build a machine learning model that automatically classifies incoming ticket text into predefined categories. Enable faster ticket routing, reduced operational effort, and improved customer resolution times.
Tech Stack: Python, NLP (TF-IDF / Bag-of-Words), Naive Bayes or Logistic Regression

Business Problem: Fraudulent transactions lead to significant financial losses and reputational damage. Detecting them in real time is challenging, as high false positives can disrupt genuine customer transactions and operational efficiency.
Objective: Develop a machine learning classification model to accurately flag suspicious transactions in real time, minimizing false positives and enabling proactive fraud prevention.
Tech Stack: Python, machine learning models, anomaly detection techniques, SQL

Business Problem: Organizations often treat all customers similarly, missing opportunities for targeted marketing, personalized offers, and optimized engagement. Understanding customer segments is challenging due to diverse behaviors and purchase patterns.
Objective: Apply clustering techniques to segment customers based on demographics, transaction history, and behavior. Use these segments to tailor marketing campaigns, improve cross-sell/up-sell strategies, and enhance customer retention.
Tech Stack: Python, SQL, pandas, scikit-learn (K-Means, Hierarchical Clustering), data visualization libraries

Business Problem: Missed patient appointments result in revenue loss, underutilized resources, and inefficient scheduling. Clinics struggle to manage staff and optimize appointment slots due to unpredictable patient behavior.
Objective: Build a predictive model to identify patients likely to miss appointments, enabling proactive reminders and better scheduling decisions.
Tech Stack: Python, SQL, machine learning models (classification), Ensemble methods, pandas, scikit-learn

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 Gurgaon

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 Role

Honours & Awards Recognition

Denso International

Luminous

Bharat Petroleum

Toshiba Midea

BryAir

GD Goenka University

Livpure

Denso International

ReverseLogix

BML Munjal University

Delivering Training To 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

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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

What are the prerequisites for taking up this Data Science Certification training in Gurgaon?

There is no prerequisite to join this course. However, basic knowledge of programming concepts is an additional benefit.

Why should you do Data Science Certification in Gurgaon from Gyansetu?

Though there are many online courses available online but we at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is the most important task. This is the reason we have our course curriculum designed and delivered by industry experts along with capstone industry ready projects which will drive your learning through real-time IT industry scenarios and help in clearing interviews.

How long is the course duration?

The total duration of the Data Science Certification course is 480 hours (240 Hours of live Instructor-Led training and 240 hours of self-paced learning).

We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

Interview selection depends on your knowledge and learning. As per the past trend, the initial 5 interviews are a learning experience of :-

What type of technical questions are asked in interviews
What are their expectations?
How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!!

Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x.

Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of –

What type of technical questions are asked in interviews
What are their expectations?
How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get a job after appearing in 6-7 interviews.

We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students, so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x.

Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of :-

What type of technical questions are asked in interviews
What are their expectations?
How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

Yes, a 1:1 faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you resolve all your queries before you start class

We understand the importance of every session. Session’s recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.

Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc., so that a student can get a good handle of these topics.

We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.

We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

Yes, we have batches available on weekends. We understand many students are in jobs and it’s difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors on +91-9999201478.

Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more about this on +91-9999201478.

You don’t need to pay anyone for software installation, our faculties will provide you with all the required software’s and will assist you in the complete installation process.

Our faculties will help you in resolving your queries during and after the course.

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Frequently Asked Questions

What are the prerequisites for taking up this Data Science Certification training in Gurgaon?

Why should you do Data Science Certification in Gurgaon from Gyansetu?

How long is the course duration?

I am currently working as an IT professional; will you provide me with Job Assistance?

I am currently working in a non-IT field; will you provide me with Job Assistance?

I’m not working or have a gap in profile or currently pursuing studies, will you provide me Job Assistance?

Will you provide a demo session before enrollment?

What if I miss a class?

Do you provide learning content?

What if I am taking more time to learn or I want to repeat the course?

What is your average batch size?

Do you provide classes on weekends?

Do you provide classes on weekdays?

How will I get the software installed?

What if I have a query after I complete the course?

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