Data Science Course in Pathankot

Enroll now in Gyansetu’s Data Science Course in Pathankot and master industry-leading skills of Python, AI, Machine Learning. Unlock high-paying opportunities with our dedicated 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: 21 Mar, 2026

Next Batch: 21 Mar, 2026

Overview
Features
Placements
Certification
Curriculum
Projects
Reviews
FAQs

Data Science Course in Pathankot Overview

We at Gyansetu have designed our Data Science Course in Rewari to bridge the gap between theoretical knowledge and industry demands. Our cutting-edge, well-rounded curriculum includes essential tools like Python, SQL, Machine Learning, and Tableau. With the global data science market expected to grow at a 27.7% CAGR through 2030 (Grand View Research, 2024), entering this field has never been more compelling.

At our Data Science Training in Rewari, we do not just offer classroom lectures on Data Science. We offer an ecosystem of hands-on learning with live projects that mirror real-world business scenarios. Our students have a strong portfolio and the confidence to tackle complex data challenges. Whether you aim for a career switch or professional growth, our dedicated placement helps you grab the highest-paying job. Our pride is to provide the best Data Science Course Rewari for practical skill development.

Wondering why so many students trust their careers to us?

Why Choose Gyansetu’s Data Science Course In Pathankot?

We distinguish ourselves through a career-first approach, blending expert mentorship with practical exposure to ensure you don’t just learn Data Science in Rewari, but master it completely. Here is why we are a top choice:

Industry-Expert Trainers: Learn directly from mentors with 10+ years of experience working at top Fortune 500 tech companies.
Live Capstone Projects: Build your portfolio by working on real-world datasets across finance, healthcare, and e-commerce domains effectively.
100% Placement Support: We provide dedicated assistance, from resume building to unlimited interview opportunities until you get hired.
Affordable Fee Structure: Access the Best Data Science Course Rewari offers at a price point designed for student accessibility.
Small Batch Size: Experience personalized attention in small groups, ensuring every doubt is resolved instantly by instructors.
Lifetime LMS Access: Enjoy unlimited access to recorded sessions, study materials, and updated course content forever after completion.
Flexible Learning Options: Choose between weekend and weekday batches, in both online and offline modes, to suit your schedule perfectly.
Mock Interview Preparation: Gain confidence through rigorous technical and HR mock interviews tailored to specific data science job roles.
Globally Recognized Certification: Earn a certification that validates your skills and enhances your credibility with employers worldwide.
Cutting-Edge Curriculum: Stay ahead with a syllabus updated quarterly to reflect the latest Data Science Training in Rewari trends.

data-science-course-in-pathankot

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 Course in Pathankot

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 Pathankot

At Gyansetu, we award both digital and physical certificates to all successful Data Science graduates. Your digital certificate offers instant validation of your achievement and is perfect for showcasing your expertise on platforms like LinkedIn, your resume, portfolio, and social media. The physical certificate provides a formal, tangible record you can present during interviews or keep for your professional documentation.

Industry Recognition: Both certificate formats are widely accepted by tech employers and companies throughout the region, helping your profile stand out in a competitive job market for technical and analytical roles.
Verifiable Authenticity: Each certificate includes a unique identification number, so potential employers can easily verify your credentials and confirm you have met the rigorous standards of our training.
Career Impact: By having both digital and physical certificates, you maximize your career opportunities—whether applying for remote roles online or presenting hard copies for in-person interviews, ensuring you are prepared for every step in your career advancement.

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Data Science Course Curriculum

Our dynamic curriculum is aligned with current market trends, covering everything from foundational concepts to advanced deployment strategies. We blend theoretical knowledge with practical application, ensuring you gain proficiency in in-demand technologies through interactive modules and real-world case studies.

SQL Mastery

Gain comprehensive expertise in database management by mastering the foundational and advanced aspects of SQL. You will learn to utilize SQL operators, complex functions, subqueries, and views to manipulate data. Additionally, we dive deep into various types of joins and query optimization techniques, ensuring you can manage, retrieve, and process massive datasets with speed and precision in any enterprise environment.

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 for Business Intelligence

Transform raw data into compelling visual stories with Power BI. This module teaches you how to design interactive, high-impact dashboards and visualize complex data relationships. You will achieve proficiency in DAX (Data Analysis Expressions) functions for custom calculations and work through extensive real-world case studies to derive actionable insights that drive strategic business decisions.

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 & Automation

Develop a powerful programming foundation using Python, the leading language for data professionals. You will explore core programming concepts, web scraping techniques, and the essential library stack, including NumPy for numerical computing and Pandas for sophisticated data manipulation. Learn to perform exploratory data analysis and build automated pipelines that streamline the entire data science workflow.

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 & Probability for Data Analysis

Build a solid mathematical foundation to ensure the integrity of your data findings. This course covers vital concepts such as probability theory, descriptive and inferential statistics, hypothesis testing, and normal distribution. You will also learn sampling techniques and error margin analysis, which are crucial for making accurate predictions and avoiding common pitfalls 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 with Python

Take a deep dive into the world of Artificial Intelligence and Machine Learning. You will move from conceptual understanding to practical implementation, covering supervised and unsupervised learning algorithms. The curriculum includes regression, classification, and clustering, as well as model evaluation metrics and deployment strategies to bring your predictive models to life in a production setting.

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

Unlock the full potential of Microsoft Excel as a robust data analysis tool. Beyond the basics, you will master advanced formulas, dynamic pivot tables, and sophisticated data visualization features. We also cover Power Query, macros for automation, and professional business reporting techniques, enabling you to perform rapid data manipulation and present findings clearly to stakeholders.

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 (Telecom / Banking)

Business Problem: High customer churn rates lead to significant revenue loss and increased marketing costs to acquire new customers. Businesses struggle to identify at-risk customers early enough to intervene effectively.
Objective: Develop a predictive model to identify customers with a high likelihood of churning. This will enable proactive retention campaigns and personalized interventions to improve customer loyalty.
Tech Stack: Python, SQL, pandas, scikit-learn, XGBoost

Recommendation Engine (E-commerce / OTT)

Business Problem: Users often struggle to find relevant products or content in vast catalogs, leading to low engagement and missed sales opportunities. Generic marketing fails to capture individual user preferences.
Objective: Build a recommendation system that provides personalized product or content suggestions to each user. The goal is to increase user engagement, session duration, and conversion rates.
Tech Stack: Python, pandas, scikit-learn, Surprise

Credit Risk & Loan Default Prediction (Banking)

Business Problem: Incorrectly assessing a borrower’s creditworthiness leads to financial losses from loan defaults. Manual and outdated risk assessment processes can be slow and inconsistent.
Objective: Create a machine learning model to accurately predict the probability of a loan default based on applicant data. This will support more informed, data-driven lending decisions and minimize credit risk.
Tech Stack: Python, pandas, scikit-learn, Logistic Regression

Business Problem: Manually sorting and routing high volumes of customer support tickets is time-consuming and inefficient. Delays in ticket resolution lead to poor customer satisfaction and increased operational costs.
Objective: Automate the categorization of incoming support tickets based on their content. The goal is to ensure tickets are routed to the correct department instantly, speeding up resolution times.
Tech Stack: Python, NLTK, scikit-learn, TF-IDF

Business Problem: Financial institutions face constant threats from fraudulent transactions, which result in direct financial losses and erode customer trust. Traditional rule-based systems often fail to detect novel fraud patterns.
Objective: Build a real-time system to detect and flag potentially fraudulent transactions with high accuracy. The model aims to minimize financial losses while reducing the number of legitimate transactions flagged as fraudulent (false positives).
Tech Stack: Python, pandas, scikit-learn, Anomaly Detection

Business Problem: Generic, one-size-fits-all marketing campaigns are ineffective and result in wasted ad spend. Businesses need a better understanding of their customer base to create relevant offers.
Objective: Group customers into distinct segments based on their purchasing behavior and demographics. This will enable the creation of targeted marketing campaigns and personalized experiences to increase engagement and ROI.
Tech Stack: Python, pandas, scikit-learn, K-Means Clustering

Business Problem: Patient no-shows result in significant revenue loss for healthcare providers and inefficient use of medical staff and resources. Overbooking to compensate for no-shows can lead to long wait times and poor patient experiences.
Objective: Predict the likelihood of a patient missing their scheduled appointment. This allows administrative staff to implement targeted reminders or strategically overbook to optimize scheduling and minimize revenue loss.
Tech Stack: Python, pandas, scikit-learn, classification models

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 Pathankot

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

Delivering Training To Wedapt

Manav Rachna University

GLA University

DPGITM

GD Goenka University

FOR QUERIES, FEEDBACK OR ASSISTANCE

Have Questions? Speak to Our Experts!

+91 9999 201 478 +919999201478

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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.

FAQ: Data Science Course in Pathankot

Q1. What is the duration of the Data Science Course in Pathankot?

Our comprehensive Data Science course typically spans 4 to 6 months, depending on whether you choose weekday or weekend batches. We ensure enough time is dedicated to both learning modules and completing practical projects.

Q2. Do I need a coding background to join this course?

No, you do not need prior coding experience. We start from the absolute basics of Python and SQL, making it suitable for beginners from non-technical backgrounds who want to learn Data Science.

Q3. What is the fee structure for Data Science training in Pathankot?

We offer competitive and affordable fees for our training programs. For the most current fee structure and available EMI options, please contact our counseling team directly.

Yes, we offer 100% placement support. While we are based in major hubs, we assist students in Pathankot with resume building, mock interviews, and connecting with hiring partners for remote and on-site roles.

Absolutely. Our curriculum includes 7+ real-world projects across industries like Banking, Healthcare, and E-commerce to ensure you gain practical experience that employers value.

Yes, we offer live, interactive online classes that allow you to attend sessions from Pathankot. You can interact with instructors in real-time and access recordings later.

You will master industry-standard tools including Python, SQL, Power BI, Advanced Excel, NumPy, Pandas, and various Machine Learning libraries like Scikit-learn.

We focus on job-ready skills. By working on capstone projects and receiving interview preparation, professionals from other fields can smoothly transition into high-paying Data Science roles.

Yes, our instructors are working professionals with 10+ years of experience in Data Science and Analytics. They bring real-world industry insights directly into the classroom.

Yes, upon finishing the course and projects, you will receive a Gyansetu Data Science Certificate. This certification is recognized by industry partners and boosts your professional credibility.

We provide recordings of every live session. If you miss a class, you can easily catch up by watching the recording and clarifying doubts in the next session.

While major tech hubs have higher demand, businesses in Pathankot are increasingly adopting data-driven decisions. Furthermore, remote work opportunities allow you to work for global companies from Pathankot.

Freshers in Data Science can expect starting packages ranging from ₹4 LPA to ₹8 LPA, depending on their skill level and the company, with significant growth potential.

Yes, we encourage you to attend a free demo session. This allows you to experience our teaching methodology and interact with our mentors before making a decision.

To enroll in this course you can visit our website or contact our admission team on +91 9999201478. We will guide you through the registration process and help you choose the right batch.

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