Data Science Course

Enroll in the world's best data science course that changes your career! Acquire the demanded skills in AI, machine learning and big data through practical projects and through course consultation. Enroll now and get high-paid opportunities in any corner of the world as well as future-proof your competencies!

4.8 (8598 Reviews)

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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: 24 Jan, 2026

Next Batch: 24 Jan, 2026

Overview
Features
Placements
Certification
Curriculum
Projects
Reviews
FAQs

Data Science Course Overview

The data science course at Gyansetu is designed to suit those who are willing to achieve success in the sphere of data analytics, machine learning, and artificial intelligence. This course provides essential competencies in Python, R, SQL, and the most recent technologies, including TensorFlow and Power BI, since it is projected that the global job market will increase by more than 30 percent by 2030.

The active participants will be able to participate in real-time case studies and will be provided with the services of an expert mentor, which will put them in the job market with all the necessary skills to solve complicated data problems. The certification offered by the course is accepted by the major tech employers worldwide, which gives it an upper hand in career promotion.

Joining such a program is an opportunity to unlock high demand and to be a member of a successful global community in data science. The training also focuses on critical thinking and decision-making skills based on data, which is necessary in driving strategic initiatives in different industries globally. It is high time to become a data science professional with an international career path: apply now to change your career trajectory and get hired in the field.

Why choose Gyansetu’s Data Science Course?

The data science course offered by Gyansetu equips you with all the industry-demanding tools. This makes you ready for real-world issues in data science and analytics.

Comprehensive Curriculum: Get acquainted with all data science core technologies such as Python, R, SQL, machine learning, AI, and advanced analytics tools in a series of extensive hands-on projects and case studies.
Expert Mentorship: Gain expert advice and mentorship via personalized assistance with experienced data scientists and industry leaders in the course of your studies.
Career-Nurturing Services: Get access to powerful industry networks, allowing job placements, industry internships, and career-specific services such as resume building and data-specific interview preparation.
International Standing: Get recognized through certifications accepted by the top employers globally, and be linked to an extensive data community and network.
Flexible Learning: Study at your own speed with a combination of self-paced courses and live classes, which is ideal in a busy work schedule and those students who aspire to become data scientists but have to balance their other responsibilities.

Gyansetu is a choice to enroll in a program that teaches and enables you to engage directly with a vibrant, worldwide data science community. Apply now to launch your career with confidence.

data-science-course

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 Data Science Course

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

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

The Data Science Course of Gyansetu provides a world-recognized certification to verify that you have mastered the essentials of data science and the most popular tools: Python, R, SQL, machine learning, and AI. The certificate has great importance to the industry and it is supported by the major technology firms and training partners.

The certification also increases your professional reliability, as it makes you a choice for data science employment opportunities across the globe. It facilitates career development through providing access to desirable job placements and career development. Also, the course will prepare you to certify based on the professions of leading vendors such as Microsoft and Google, which will further enrich your resume.

Gaining certification in data science at Gyansetu implies that you are not just armed with some practical skills but also have a reliable piece of paper that helps you to become a successful data scientist in the world marke

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Data Science Course Curriculum

The course of Data Science provides us with the most fundamental tools and concepts underlying the skillset of any data scientist, such as SQL, Power BI, Python, Statistics, and Machine Learning.

SQL

It forms the basis of manipulating and querying relational databases thus allowing you to access and manipulate data effectively to perform analysis. Power BI supplies you with the potent visualization tools to design interactive reports and dashboards to enable the data insights to be available and influential to the decision-makers.

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

Power BI is a Microsoft business analytics software, which converts raw data into interactive visual reports and dashboards. It has many tools that can be customized with charts, maps, and insights powered by AI, allowing the exploration of data in real-time. Power BI is user-friendly due to its ability to enable users to make decisions that are data-driven via clear and compelling visual storytelling.

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

It is a universal programming language that can be extensively used in data science to manipulate and analyze data and create machine learning models. It provides large libraries such as Pandas, NumPy and Scikit-learn in order to support these.

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

This gives the mathematical background that it requires to interpret results correctly, to test hypotheses, and to make decisions based on data, which are essential in data interpretation.

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

It presents algorithms that enable systems to learn patterns based on data and can be used to predict analytics and automate complex tasks: essential innovations in data science today.

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

Industry Ready Data Science Projects

Get Real-World Experience

Health Monitoring and Disease Prediction

You will be working on decision trees, neural networks, support vector machines to predict diseases and monitor health. The input data like vital signs, medical history, and diagnostic results will be gathered in a pre-processed manner to predict disease probability, personalized health insights, and early warnings.

Credit Risk Assessment

You will be working on Machine learning algorithms like Random Forest, Gradient Boosting, and Logistic Regression to predict the default applications. You will be developing a prediction model to check credit risks for loan applications by examining credit scores, personal data and repayment patterns of the customer.

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 lost.

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

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

GD Goenka University

Livpure

Denso International

ReverseLogix

BML Munjal University

Manav Rachna University

GLA University

DPGITM

GD Goenka University

FOR QUERIES, FEEDBACK OR ASSISTANCE

Have Questions? Speak to Our Lucknow Experts!

+91 9999 201 478 +919999201478

4.8

4.7

5.0

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

What is the duration of the Data Science Course?

The course span depends on your preference of batch. At Gyansetu, we offer weekday and weekend batches. These batches balance theoretical and practical learning. We have flexible schedules which makes it accessible to both students and working professionals.

Do I need prior experience in programming?

Basic programming knowledge helps but is not mandatory. Our course starts with basic fundamentals of Python and R which makes this suitable for beginners or those who wish to change their career in the field of data science.

What tools and technologies will I learn?

During the course you’ll be learning tools which are required to be a data scientist. These tools include Python, R, SQL, Machine Learning Algorithms, Power BI, TensorFlow.

Yes, on completion of course Gyansetu provides a certificate which is globally recognized. This certificate validates your skills and enhances your job prospects in the market.

At Gyansetu, we have a curriculum which includes hands-on projects and real-world case studies which provide practical learning. These practical projects are based on real cases from industries which makes you job ready from day one.

Gyansetu offers 100% placement assistance in the form of resume building, interview preparation and access to industry networks which helps to get learners their dream job roles.

Yes, at Gyansetu we offer flexible learning options which includes online, offline and hybrid options. This is designed to fit schedules of learners which allows them to learn while working.

Data science professionals earn competitive salaries globally. This salary often ranges from $70,000 to $120,000 annually, depending on experience, location. The salary grows as you gain more experience and skills.

The process of enrollment is simple with the help of Gyansetu’s website. You can also visit the center to have details about the course. To know more about the course you can attend a free demo session.