How do I enroll in the Data Science course in Pune?

Visit our website, fill out the enrollment form, or contact us at +91 97739 98596. You can also visit our Kharadi, Pune office for in-person registration.

What is the duration of the Data Science course?

The Data Science course lasts 12–16 weeks, with flexible weekend and evening batches for Pune learners.

Do I need prior programming experience?

No, our courses are beginner-friendly, but basic programming knowledge can be helpful.

What projects will I work on?

You’ll build projects like predictive models, NLP applications, and recommendation systems to create a job-ready portfolio.

Is placement assistance provided?

Yes, we offer 100% placement assistance, including resume building, mock interviews, and connections to Pune-based companies.

What is the course fee?

Contact us at info@pythoncoursetraining.com for detailed pricing. We offer EMI options and discounts for Pune students.

Can I attend classes online?

Yes, we offer live online classes with interactive sessions, ideal for learners in Pune, Mumbai, or other cities.

What tools will I learn?

You’ll master tools like Python, R, TensorFlow, Scikit-learn, Apache Spark, Tableau, and Power BI.

Are there weekend batches available?

Yes, we offer weekend and evening batches for working professionals in Pune and nearby areas.

What is the certification process?

Complete the course, pass assessments, and submit a capstone project to earn your certification.

Can freshers join the course?

Absolutely, our course is designed for freshers with no programming background, starting from basics.

What job roles can I apply for?

You can apply for roles like Data Scientist, Machine Learning Engineer, Data Engineer, or AI Specialist.

Are there any prerequisites for the course?

No prerequisites; just a laptop with Python 3.8+ and a desire to learn Data Science.

What is the class size?

Classes are limited to 15–20 students for personalized attention and effective learning.

Do you offer corporate training in Pune?

Yes, we provide customized Data Science training for corporate teams in Pune and Maharashtra.

Can I visit the institute before enrolling?

Yes, visit us at 2nd Floor, Laxmi Kunj, Kharadi, Pune, or schedule a tour via phone.

What support is provided post-course?

We offer ongoing career guidance, job placement support, and access to alumni networks.

Are there any discounts for students?

Yes, we offer special discounts for students and group enrollments. Contact us for details.

What is the class format?

Classes include lectures, hands-on labs, and project work, with both online and in-person options.

How do I contact the institute?

Reach us at +91 97739 98596 via phone or WhatsApp, email us at info@pythoncoursetraining.com, or visit our office at 2nd Floor, Laxmi Kunj, Kharadi, Pune 411014 for inquiries about our Data Science training in Pune.

Data Science Training in Pune | Top Data Science Course with Certification

Date	Mode of Class	Batch Form	Time	Status	Action
Thursday, October 30, 2025	Online/Classroom	Weekdays	6:30 PM IST	Batch Full	Enrollment Closed →
Thursday, November 6, 2025	Online/Classroom	Weekdays	6:30 PM IST	Only 1 Seat AvailableMax intake limit is 10	Enrollment Open →
Thursday, November 13, 2025	Online/Classroom	Weekdays	8:00 AM IST	Only 5 Seats AvailableMax intake limit is 10	Enrollment Open →
Saturday, November 1, 2025	Online/Classroom	Weekends	10:00 AM IST	Only 2 Seats AvailableMax intake limit is 10	Enrollment Open →

Data Science Course

Course Overview

Enroll in our Data Science course at Data Science Training Institute in Pune, located in Kharadi, the heart of Pune’s IT hub. This program is designed to transform beginners and professionals into skilled data scientists, covering Python, R, Machine Learning, Deep Learning, and Big Data tools like Apache Spark.

With hands-on projects like predictive modeling and NLP applications, and partnerships with top Pune-based companies like Infosys and TCS, our course ensures you’re job-ready in 90 days. Earn an industry-recognized certification and benefit from our 100% placement assistance.

What You'll Learn

Python and R programming for data science.
Machine Learning with Scikit-learn and TensorFlow.
Deep Learning concepts and neural networks.
Data visualization with Tableau and Power BI.
Big Data processing with Apache Spark.
SQL for database querying and management.

Requirements

A laptop with Python 3.8+ installed (free download).
Basic knowledge of programming is helpful but not mandatory.
Enthusiasm to learn data science and AI concepts.

Data Science Course Syllabus

Download Syllabus

Course Curriculum

Our Data Science course in Pune offers a comprehensive curriculum, covering data science, machine learning, deep learning, and big data. Designed for beginners and professionals, it includes hands-on projects to build AI-driven web applications, preparing you for roles at companies like Infosys, TCS, and Wipro in Pune’s tech hub.

What is Data Science? Use Cases with Business Problems (Mobile/Banking) and How ML Provides Solutions
Types of Roles: Data Analyst, Data Scientist, ML Engineer
Important Learnings and VAC Courses Offered
Jumbo Pass, Q & A

ML Project Life Cycle
Problem Definition
Data Collection
Exploratory Data Analysis (EDA)
Data Cleaning and Transformation
Data Partitioning (Train-Test Split)
Model Fitting
Cross-Validation
Metrics Evaluation
Deployment with Django (Web App and REST API)

Sample vs. Population
Data Types (Continuous, Discrete)
Measures of Central Tendency (Mean, Median, Mode) using NumPy and pandas
Spread and Shape of Data: Histogram, Skewness, Kurtosis using matplotlib and seaborn

Bar Graph, Box Plot (IQR, Whisker Lengths, Outliers), Scatter Plot (Positive, Negative, Neutral) using matplotlib and seaborn
Correlation Analysis using pandas

Introduction to Python Language
Anaconda Installation (Jupyter Notebook, Spyder)
Data Types: int, float, dict, set
Operators: Arithmetic, Comparison, Logical, Assignment

Data Structures
Lists: append, extend, insert, remove, pop, clear, index, count, sort, reverse
Tuples, Dictionaries, Sets
Control Structures: if, if-else, if-elif, Nested if

Loops: for, while
Functions: Defining and Calling Functions
NumPy: Scalars, Arrays, Vectors, 1D and 2D Arrays, Random Integers
Converting NumPy to pandas DataFrames
pandas Basics: read_csv, head, tail, describe

pandas Operations: info, Selecting Columns, Dropping Columns, groupby, concat (Rows and Columns), merge, Removing Duplicates, Filling Blanks with Mean

Exploratory Data Analysis (EDA)
Visualizations: Histogram, Box Plot, Bar Graph, Scatter Plot, Heatmap using matplotlib and seaborn
Hands-On: EDA on Example Dataset in Google Colab with Generative AI Usage

Probability Concepts
Normal Distribution Theory: Standardization, Z-Score, Z-Tables
Applications with Python Code (scipy.stats)
Confidence Intervals using NumPy and scipy.stats

Level of Significance
Hypothesis Testing: One-Sample Z-Test, Two-Sample Z-Test, t-Test using scipy.stats

Simple Linear Regression using scikit-learn
Metrics: RMSE, R²
Case Study: Age vs. Weight Example

Introduction to Regression Models
Multiple Linear Regression (MLR) using scikit-learn
Assumptions of Linear Regression
Variable Selection
Multicollinearity: Variance Inflation Factor (VIF)

Introduction to Classification Models
When to Choose Logistic Regression
Model Fitting, Confusion Matrix, Accuracy Score using scikit-learn
Case Study: Breast Cancer Classification

Metrics: Sensitivity, Specificity, Precision, F1 Score, ROC Curve, AUC Score using scikit-learn and matplotlib

Data Transformation: StandardScaler, MinMaxScaler, Label Encoding, One-Hot Encoding using scikit-learn
Data Partitioning: Training and Test Splits

Cross-Validation: Stratified K-Fold, K-Fold, Shuffle Split using scikit-learn

Variance-Bias Tradeoff
Underfitting: Causes (Lack of Training)
Best Fit
Overfitting: Causes (Noise, Too Many Epochs, Excessive Variables)
Visualizations: Underfitting, Best Fit, Overfitting using matplotlib
Feature Engineering
Case Study: Bangalore Housing Prices

Regularization Techniques: Lasso, Ridge, ElasticNet using scikit-learn
Case Study: Bangalore Housing Prices

Support Vector Machines (SVM): Hyperplane, Maximum Margin Classifier, Support Vectors
SVM for Linear and Non-Linear Classification (Polynomial, RBF, Sigmoid Kernels) using scikit-learn

Decision Tree Structure: Root Node, Internal Nodes, Terminal Nodes
Gini Impurity, Entropy, Information Gain
Overfitting and Underfitting, Pruning, Hyperparameters
Case Study: Sales Dataset using scikit-learn

Ensemble Methods: Bagging, Random Forests using scikit-learn
Hyperparameter Tuning to Control Overfitting

Sequential Methods: Gradient Boosting, AdaBoost using scikit-learn
Advanced Boosting: XGBoost, LightGBM
Grid Search CV for Hyperparameter Tuning

Final Project with Deployment
Build a Django Web Application for ML Model Deployment
Django REST Framework for Serving Model Predictions
Deploy on Cloud Platforms (e.g., Heroku, AWS)
Case Study: End-to-End ML Application

Dimensionality Reduction Techniques
Purpose of PCA
Eigenvectors and Eigenvalues
Applications and Advantages
Case Study using scikit-learn

Introduction to Clustering
Distance Metrics
Clustering Algorithms: K-Means, DBSCAN using scikit-learn
Choosing the Right Number of Clusters: Elbow Method, Silhouette Analysis

Introduction to Recommendation Systems
Collaborative Filtering and Content-Based Filtering
Implementation using scikit-learn or Surprise Library

Time Series Concepts and Components
Visualization using matplotlib
Data Partitioning, Lag Plot
ARIMA Models using statsmodels
Python Code for ARIMA Models

Perceptron, Single Layer Network, Activation Functions
Backpropagation, Simple ANN Code using TensorFlow
Multilayer Neural Networks, Gradient Descent, Optimizers, Learning Rate

RNN Use Cases, Vanishing/Exploding Gradient Problem
Simple RNN Code using TensorFlow
LSTM Architecture, LSTM vs. GRU
Python Code for LSTM Models

Text Data: Forms and Applications
Text Preprocessing: Tokenization, Normalization, Stopwords, Lemmatization, Stemming using NLTK and spaCy
Visualization of Preprocessed Text Data using matplotlib
Text Representation: Bag of Words (BoW), TF-IDF using scikit-learn
Sentiment Analysis and Classification Models
Named Entity Recognition (NER) using spaCy
Word Embeddings: Word2Vec (Skip-Gram, CBOW) using gensim
Language Modeling: N-Gram Models, Neural Language Models
Applications of RNNs and LSTMs on Text Data

Large Language Models (LLMs)
Transfer Learning in NLP
Pre-Trained Models with Hugging Face Transformers

Introduction to Transformers
Hugging Face Transformers Library: Use Cases and Implementation
Building NLP Applications with Python

Python Introduction: Programming Cycle
Python Installation, Python IDEs
Variables, Data Types

Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise
Data Structures: Lists, Tuples, Sets, Dictionaries

Conditional Statements: if, if-else, if-elif, Nested if
Loops: for, while
Loop Control Statements: break, continue, pass

Defining and Calling Functions
Pass by Reference vs. Value
Function Arguments, Anonymous Functions (Lambda)
Return Statements, Scope of Variables (Local, Global)
Lambda, map, filter, reduce

Importing Modules
Creating User-Defined Modules
Python Standard Library
Installing Packages using pip

Importing Data
Handling Missing Data: Filtering, Filling
Data Transformation: Removing Duplicates, Type Conversion
Detecting and Handling Outliers: Boxplot, Z-Score, Capping, Removal
Feature Engineering: Creating New Variables, Aggregations, Groupings

Hierarchical Indexing
Combining and Merging Datasets: merge(), join(), concat()
Reshaping and Pivoting with pandas

Converting to Datetime
Extracting Attributes
Creating Datetime Range
Resampling Data, Timedelta Calculations
Adding Time Offsets, Time Zone Conversion
Setting Datetime Index, Filtering by Date
Handling Missing Time Data

Exception Handling: try, except, else, finally
Built-in Exceptions, Raising Exceptions, Custom Exceptions
Hands-On Error Handling Tasks
Regular Expressions: match, search, Modifiers, Patterns using re

Class and Object, __init__ Method
Attributes and Methods
Hands-On: Creating Simple Classes

Inheritance, Polymorphism
Hands-On: Real-World OOP Examples

Encapsulation and Abstraction
Hands-On: Real-World OOP Examples

Iterators and Generators
Decorators

What is Tableau?
Data Visualization Concepts
Tableau Products and Desktop Variations
Tableau File Extensions
Data Types, Dimensions, Measures, Aggregation
Tableau Desktop Installation
Data Source Overview: Live vs. Extract

Worksheet Sections and Shelves
Bar Chart, Stacked Bar Chart
Discrete & Continuous Line Charts
Symbol Map & Filled Map
Text Table, Highlight Table
Formatting: Remove Grid Lines, Hiding Axes, Number Conversion (Thousands, Millions), Shading, Row/Column Dividers, Marks Card

Types of Filters: Extract, Data Source, Context, Dimension, Measure, Quick Filters
Order of Operation of Filters
Cascading Filters
Apply to Worksheets

Need for Calculations
Types: Basic, LODs, Table
Examples: Aggregate Functions, Logical Functions, String Functions, Tableau Calculation Functions, Numerical Functions, Date Functions

Level of Detail (LOD) Calculations: Examples
Table Calculations: Examples

Data Combining Techniques
Types: Joins, Relationships, Blending, Union

Dual Axis, Combined Axis
Donut Chart, Lollipop Chart
KPI Cards: Simple and With Shape

Groups: Purpose and Examples
Bins: Purpose and Examples
Hierarchies: Purpose and Examples
Sets: Purpose and Examples
Parameters: Purpose and Examples

Reference Lines, Trend Lines
Dashboard Overview: Tiled vs. Floating
Objects and Layout Overview
Dashboard Creation with Formatting

Actions: Filter, Highlight, URL, Sheet, Parameter, Set
Saving Workbooks to Tableau Public

Introduction to Databases and RDBMS
RDBMS through Normalization
Types of RDBMS
Software Installation: MySQL Workbench
Integration with Python using mysql-connector-python

Types of SQL Commands: DDL, DML, DQL, DCL, TCL and Applications
Data Types: Numeric, Char, Datetime
Executing SQL Queries in Python using mysql-connector-python

SELECT: LIMIT, DISTINCT, WHERE, AND, OR, IN, NOT IN, BETWEEN, EXISTS, IS NULL, IS NOT NULL, Wildcards, ORDER BY
Python Integration: Querying MySQL with pandas and mysql-connector-python

Usage of CASE WHEN THEN for Logical Problems
Handling NULL Values: IFNULL, COALESCE
Implementing in Python with pandas and SQL

GROUP BY, HAVING Clause
Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
String Functions, Date & Time Functions
Python Integration: Aggregations with pandas and SQL

Constraints: NOT NULL, UNIQUE, CHECK, DEFAULT, ENUM, Primary Key, Foreign Key (Column and Table Level)

Joins: Inner, Left, Right, Cross, Self, Full Outer
Python Integration: Joining Tables with pandas and SQL

DDL: CREATE, DROP, ALTER, RENAME, TRUNCATE, MODIFY, COMMENT
Python Integration: Executing DDL Commands via mysql-connector-python

DML: INSERT, UPDATE, DELETE
TCL: COMMIT, ROLLBACK, SAVEPOINT
Data Partitioning
Python Integration: DML and TCL with mysql-connector-python

Indexes: Types and Usage
Views in SQL
Python Integration: Managing Indexes and Views with mysql-connector-python

Stored Procedures: IN, OUT, INOUT Parameters
Python Integration: Calling Stored Procedures with mysql-connector-python

User-Defined Functions
Window Functions: RANK, DENSE_RANK, LEAD, LAG, ROW_NUMBER
Python Integration: Executing Window Functions with SQL and pandas

UNION, UNION ALL, INTERSECT
Sub-Queries, Multiple Queries
Python Integration: Handling Complex Queries with pandas and SQL

Handling Exceptions in SQL: CONTINUE Handler, EXIT Handler
Loops: Simple, Repeat, While
Cursors in SQL
Python Integration: Managing Exceptions and Cursors with mysql-connector-python

Triggers: BEFORE and AFTER DML Statements
Python Integration: Managing Triggers with mysql-connector-python

What is Django? Django Architecture (MVC vs. MTV)
Setting Up Django Environment
Creating a Django Project and App
Django Project Structure
Integration with Python Data Science Libraries

Django ORM for MySQL Integration
Defining Models for Data Science Datasets
Database Migrations
CRUD Operations with Django ORM
QuerySets: Filtering, Ordering, Aggregation
Django Admin Interface for Data Management

Django Views: Function-Based Views (FBV), Class-Based Views (CBV)
URL Routing and Mapping
Templates: Rendering Data Visualizations (e.g., matplotlib Charts)
Static Files: Serving CSS, JS, Images
Django Template Language: Filters, Tags

Introduction to RESTful APIs
Installing Django REST Framework
Creating APIs for ML Model Predictions
Serializers for Data Science Models
GET, POST, PUT, DELETE Endpoints
Authentication and Permissions
Testing APIs with Postman
Deploying Models: Integrating scikit-learn, TensorFlow with Django

Building a Web App for Data Visualization
Embedding matplotlib/seaborn Plots in Django Templates
Interactive Dashboards with Django and Plotly
User Authentication: Login/Signup with Django
Case Study: Deploying a Predictive Model (e.g., Housing Prices) as a Web App

What is MLOps?
Stages in MLOps
ML Project Lifecycle
Job Roles in MLOps
Python Tools for MLOps (MLflow, DVC)

Development Stage of ML Workflow
Pipelines and Steps
Artifacts, Materializers, Parameters, Settings
Python Implementation with MLflow

Stacks and Components
Orchestrators (e.g., Airflow with Python)
Artifact Stores
Flavors in MLflow

ML Server Infrastructure
Server Deployment with Django
Metadata Tracking with MLflow
Collaboration Tools and Dashboards

History and Development of ChatGPT
Examples of ChatGPT Use in Various Industries
Basics of Transformers
Key Concepts of Generative AI
Examples of Generative AI Models (ChatGPT, Open-Source LLMs)
Prompting Basics
Overview of Different ChatGPT Models
Python Integration: Using OpenAI API with Python

Prompt Techniques: Few-Shot, Zero-Shot, One-Shot, Chain-of-Thought
ChatGPT Applications: Writing, Translation, Creativity
Use Cases in Education, Work, and Business
Python Implementation: Prompt Engineering with OpenAI API

Code Generation, Code Explanation
Machine Translation
Structured and Unstructured Outputs
Canvas, Deep Research, Image/Video Generation
Codex, Plugins, Browsing
Python Integration: Building Applications with OpenAI API

Utilizing ChatGPT for Excel, Word, PowerPoint, Web Development, Data Analysis, Programming
Building Dashboards with Python and ChatGPT Outputs
ChatGPT Projects with Python and Django Integration

Job Search Strategies using ChatGPT
Resume Building and LinkedIn Profile Optimization
Python Scripts for Automating Job Search Tasks

Introduction to OpenAI API: Usage Limits
Authentication, Endpoint Usage
Integrating GPT with Python, Google Sheets, Excel, Power BI, Zapier, Make, LangChain
Django REST Framework for GPT Integration

Linear Algebra: Vectors, Matrices, Dot Product, Matrix Multiplication using NumPy
Calculus: Derivatives, Partial Derivatives, Chain Rule for Backpropagation
Review of Artificial Neural Networks (ANN)

Gradient Descent: Weight and Bias Updates
Loss Functions: MSE, Binary Cross-Entropy (Binary and Multi-Class)
Overfitting Solutions: Dropout, Early Stopping
Optimizers and Activation Functions: Applications
Case Study with TensorFlow

CNN Concepts: Deep Convolution Models, Detection Algorithms, Face Recognition
Case Study: MNIST Dataset with TensorFlow

Introduction to Web Scraping and Web Basics
Python Libraries: requests, BeautifulSoup
HTML and Web Page Structure Basics

Selecting a Website and Extracting Data
Extracting Images, Reviews, Ratings, Price Tags
Storing in Structured Format (e.g., pandas DataFrame, MySQL via Django)
ML Applications:
Image Classification with TensorFlow
Sentiment Analysis from Reviews using NLTK/spaCy
Regression Model for Prices using scikit-learn
Django Integration: Building a Web App to Display Scraped Data and ML Results

What is Big Data? Characteristics and Technologies

Spark Environment: Documentation, Installation
Spark Concepts
Python Integration: PySpark Setup

PySpark Environment: Basics and Functions
Working with PySpark for Data Processing

PySpark RDD Structures, DataFrame Modules, SQL Modules
Examples and Exercise Problems
Working on Datasets with PySpark

PySpark ML Libraries
Regression Models: Linear and Logistic Regression
Clustering Basics
Tree-Based Models and Ensemble Concepts

PySpark ML Applications with Exercises and Visualizations
Integration with Django for Web-Based Visualizations

What is Databricks?
Account and Cluster Creation
Working on PySpark Applications in Databricks with Python
Django Integration: Serving Databricks Outputs via Web App

What is Cloud Computing? Importance, Services, Applications, Benefits, Architectures

What is Azure? Why Azure?
Azure Services and Core Architecture
Core Azure Service Domains
Creation of Azure Account

Introduction to AI/ML Services
Azure ML Designer Studio
Developing ML Models with Python
Django Integration: Deploying Azure ML Models via Django REST API

Resource Groups, Virtual Machines, Storage Services
Web Apps, Databricks Environment, Azure SQL Databases
Billing Concepts
Django Integration: Connecting Azure Services with Django

What is Azure Open AI?
Open AI Documentation
Using Azure Open AI Studio
Creating Applications with Python
Different Models in Azure Open AI
Django Integration: Building Web Apps with Azure Open AI

Advanced Data Structures: Nested Lists, Dictionaries, Sets
Functional Programming: map, filter, reduce
Data Analysis with pandas: Advanced GroupBy, Pivot Tables
Visualization with matplotlib and seaborn: Advanced Plots (e.g., Pair Plots, Violin Plots)
Case Study: Analyzing a Dataset (e.g., Iris Dataset) with pandas and seaborn

Django Project Setup for Data Science
Creating Models for Datasets (e.g., Iris Dataset)
Views and Templates for Data Visualization
Django REST API for Serving Data Analysis Results
Case Study: Web App for Dataset Analysis and Visualization

Integrating scikit-learn Models with Django
Building a Predictive Web App (e.g., Classification or Regression Model)
Serving ML Predictions via Django REST API
Interactive Visualizations with Plotly in Django
Case Study: Deploying a Machine Learning Model as a Web App

Our Trainers

Expert Data Science Trainers

Our trainers are industry experts with over 12 years of experience in Data Science, Machine Learning, and AI, having guided over 5,000 students to success in Pune’s data-driven industry.

12+ years of expertise in Data Science and AI.
Trained 5,000+ students across Pune and Maharashtra.
Certified professionals with hands-on project experience.
Connected with HR teams at Infosys, TCS, and Wipro.
Updated with the latest Data Science tools and trends.

Career Benefits of Data Science Training

Why Learn Data Science?

Data Science is a high-demand field driving innovation in industries like IT, finance, and healthcare. Our course at Data Science Training Institute in Pune equips you with skills to excel in Pune’s competitive data market, from startups to MNCs like TCS and Infosys.

With hands-on projects and placement support, you’ll build a portfolio that stands out, ensuring a rewarding career in just 90 days.

Key Benefits

High Demand: Data scientists are sought by IT, finance, and AI-driven sectors.
Lucrative Salaries: Earn ₹6L–₹25L annually in roles like Data Scientist or ML Engineer.
Versatile Roles: Work as a Data Scientist, Machine Learning Engineer, or AI Specialist.
Global Opportunities: Data Science skills open doors to MNCs and startups worldwide.
Future-Proof: Stay relevant with growing applications in AI and automation.

Data Science Job Roles and Salaries in Pune

Career Opportunities

Our Data Science course prepares you for top data roles in Pune’s IT hub. Below are key job profiles, responsibilities, and salary ranges for Data Science professionals in Pune.

Job Profile	Role Description	Average Salary (INR)
Data Scientist	Build predictive models and analyze data using Python, R, and ML tools.	Entry-Level: ₹6L–₹10L Experienced: ₹12L–₹25L
Machine Learning Engineer	Design and deploy ML models for applications like NLP and computer vision.	Entry-Level: ₹6L–₹9L Experienced: ₹12L–₹22L
Data Engineer	Develop data pipelines and manage large datasets with Spark and SQL.	Entry-Level: ₹5L–₹9L Experienced: ₹10L–₹18L
AI Specialist	Work on advanced AI projects, including deep learning and neural networks.	Entry-Level: ₹7L–₹12L Experienced: ₹15L–₹30L
Data Analyst	Analyze data and create visualizations to support business decisions.	Entry-Level: ₹4L–₹7L Experienced: ₹8L–₹14L

Key Notes

Salaries depend on experience, company, and project complexity.
Pune offers competitive pay due to its status as an IT hub.
Certifications enhance earning potential and job prospects.

Why Choose Our Data Science Training in Pune?

Your Path to Success

Data Science Training Institute in Pune offers a transformative learning experience, equipping you with skills to excel in Data Science, Machine Learning, and AI. Our course is tailored for Pune’s data-driven market, ensuring you stand out to employers like TCS, Wipro, and Infosys.

With a focus on practical training and career support, we help you achieve your data science goals in just 90 days, backed by our Kharadi-based facility and expert instructors.

Why Us?

Expert Instructors: Learn from professionals with 12+ years of Data Science expertise.
Hands-On Projects: Build real-world ML models and AI applications to showcase your skills.
Placement Support: 100% assistance with resume building and interviews.
Flexible Learning: Online and classroom options for Pune and Maharashtra learners.
Industry Certification: Earn a credential recognized by Pune’s top employers.

Data Science Training Modes in Pune

Flexible Learning Paths

Our Data Science course offers multiple training modes to suit your needs, whether you’re a student in Kharadi or a professional in Mumbai. Learn at our state-of-the-art facility in Pune or join live online sessions from anywhere in Maharashtra.

Each mode includes hands-on projects and placement support, ensuring you’re ready for Pune’s competitive data science job market.

Training Modes

Classroom Training: In-person classes at our Kharadi, Pune center for hands-on learning.
Online Training: Live, interactive sessions for learners in Pune, Mumbai, and beyond.
Corporate Training: Tailored programs for teams in Pune’s data-driven companies.