Data Science Classes in Pune | Data Science Course in Pune

Advanced Certification in Data Science

In Association with:

Certification Aligned to:

Advanced Certification from E&ICT Academy, IIT Guwahati.
6-Months technical training program with hybrid classes.
Suitable for college student, graduate, post graduate, career gap, early professionals.
Start from scratch – no prior experience needed, all pre-requisite will be covered.
Next-Gen curriculum for the age of ChatGPT and Generative AI.

Our Presence in Pune

Next Batch

16th September, 2025

Program duration

6 Months

Learning format

Classroom/Hybrid

Ethan's x E&ICT Academy IIT Guwahati

E&ICT Academy IIT Guwahati, a leading Indian Institute of Technology, excels in education, research, and innovation. Our partnership with E&ICT Academy at IIT Guwahati aims to advance global education standards. Leveraging the Institute’s esteemed reputation, we offer world-class educational opportunities and foster a global academic community.

In Association With :

Mentors & Instructors

Our team is made up of industry experts, seasoned professionals, and passionate trainers who work together as a close-knit family. We believe in not just teaching, but mentoring, inspiring, and growing together — creating a learning environment that feels like home and performs like the best in the business.

Most Advanced Curriculum in Industry

Python Basics: Learn, Code, Create

1. 1. Introduction to Python Programming:
    - - Basic Data Types in Python
      - Variables and Naming Conventions
  2. Data Types in Python:
    - - Data Structure
      - Python String Object
      - Python List and Tuple Objects
      - Python Dictionary Object
      - Python Set Object
  3. Indexing & Slicing:
    - - Importance of indexing in Python
      - Introduction to Slicing
      - Indexing and Slicing in Strings
      - Indexing and Slicing in Lists and Tuples
  4. Operators in Python:
    - - Arithmetic Operators
      - Comparison Operators
      - Logical Operators
      - Assignment Operators
      - Membership Operators
  5. In-Built Functions & Methods:
    - - Exploring Built-in Functions
      - Use Built-in Functions with data types
      - Introducing Built-in Methods for specific data types

Python Control Flow Simplified

1. 1. Statements, Indentation and Conditionals:
    - - Statements and Indentation
      - Introduction to Conditional Statements
      - Combining Multiple Conditions
  2. Loops & Iterations:
    - - Conditional Loops (While Loop)
      - Conditional Loops (For Loop)
      - Master ‘for’ loops, Learn ‘while’ loops
      - Comprehend loop control statements
      - Explore nested loops

Building Smarter Programs

1. 1. Custom Functions in Python:
    - - Introduction to Custom Functions
      - Defining and Calling Custom Functions in Python
      - Working with Arguments and Return Values
  2. Advanced Looping Concepts:
    - - List Comprehension
      - Set Comprehension
      - Dictionary Comprehension
      - Lambda Functions
  3. OOPs in Python:
    - - Introduction to Object-Oriented Programming (OOPs)
      - Creating and Working with Classes
      - Understanding Objects and Instances
      - Working with Attributes
      - Utilizing Methods in Classes
      - Embracing Polymorphism
      - Leveraging Inheritance
  4. Exception Handling & Logging:
    - - Introduction to Exception Handling
      - Try-Except Block: Catching Exceptions
      - Handling Specific Exceptions
      - Finally Block: Cleaning Up After Exceptions
      - Raising Exceptions: Taking Control
      - Handling Multiple Exceptions: The Exceptional Mastery

Python Data Structures and Algorithms

- 1. Data Structures Fundamentals:
    1. - Introduction to Data Structures
      - Arrays
  2. Efficient String Operations:
    - - Introduction to Strings
      - Combining strings using concatenation (+)
      - Searching and Finding
      - Introduction to regex
  3. Recursion Fundamentals:
    - - Understanding Recursion: Introduction and Basics
      - Recursive Function Design
      - Implementing Recursion in Algorithms
      - Analyzing Recursive Calls
  4. Mastering Recursion Concept:
    - - Time and space complexity
  5. Algorithm Fundamentals:
    - - Introduction to Algorithms
      - Big O Notation
      - Sorting Algorithms
      - Searching Algorithms

Python Data Wrangling

- 1. Getting Started with Pandas & Numpy:
    - - Introduction to Pandas & Numpy
  2. Mastering Data Wrangling:
    - - Series and dataframes
      - Indexing and slicing
      - Manipulating data
  3. Advanced Data Wrangling Concepts:
    - - Introduction to Data Wrangling
      - Handling Missing Data
      - Merging and Joining Data
      - Grouping and Aggregation
      - Reshaping Data
      - Filtering Data
  4. Data Wrangling – Code Optimisation:
    - - Techniques for Code Optimization
      - Strategies for Parallel and Distributed Data Wrangling
      - Best Practices for Efficient Data Wrangling
  5. Data Wrangling on Different Data Formats:
    - - Data Wrangling on Different Data Formats
  6. Data Management Libraries:
    - - File Management Libraries
      - Data Manipulation Libraries
      - Data Visualization Libraries
      - Web Scraping
      - Regular Expression Libraries
      - Date and Time Libraries
  7. Web Scraping using Python:
    - - Introduction to Web Scraping

Python Visualization Libraries in Action

- 1. Data Visualization with Matplotlib & Seaborn:

1. 1. 1. - Introduction to Matplotlib and Seaborn Plots

Introduction to SQL: Learn the Lang. of Data

- 1. SQL Basics for Data Analysis:
    1. - Introduction to SQL
      - Setting up the SQL environment
      - Basic SQL Commands
      - Creating and Deleting Databases and Tables
      - Importing and Exporting Data from CSV Files
  2. Fundamentals of SQL Query:
    1. - Anatomy of SQL Query
      - SQL Data Types and Operators
      - Filtering and Sorting Data in SQL
      - Aggregate functions in SQL
  3. Dealing With Multiple Tables:
    1. - Grouping Data – GROUP BY
      - HAVING
      - Subqueries
      - Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN
      - Alias in SQL queries
      - Working with Multiple Tables Using Subqueries
      - Using Set Operators
      - Aggregating Data from Multiple Tables using GROUP BY and HAVING.
  4. Advanced SQL Joins:
    1. - Advanced Join Techniques
      - Joining multiple tables
      - Handling duplicate records and eliminating duplicates
      - Using UNION and UNION ALL to combine data from multiple tables

SQL In-Built Functions

- 1. Type Casting & Math Functions:
    1. - Mathematical Functions
      - Type Conversion Functions
      - Using CASE Statements to Perform Conditional Operations
  2. DateTime & String Functions:
    1. - Working with date/time data in SQL
      - Date/time functions
      - Formatting date/time data
      - String manipulation functions (e.g. UPPER, LOWER, LEFT, RIGHT, etc.)
      - Regular expressions in SQL for string operations
      - Using CONCAT_WS to concatenate strings with a separator
  3. Window Functions:
    1. - Syntax of Windows Function
      - Ranking functions (e.g. ROW_NUMBER, RANK, DENSE_RANK, etc.)
      - Aggregate functions using windows (e.g. SUM, AVG, MAX, MIN, etc.)
      - Partitioning data for window functions
      - Understanding the difference between row-based and aggregate-based window functions

SQL for Data Preparation

- 1. Complex queries using CTE & Pivoting:
    1. - Common Table Expressions(CTE)
      - Recursive CTEs for Hierarchical Data
      - Combining CTEs with Window Functions and Subqueries
      - Understanding the Performance Implications of CTEs
  2. Database Management & Schema Design:
    - - Understanding The Relational Model and Database Schema Design
      - Normalization and Denormalization of Database Tables
      - Database Administration Tasks
      - Implementing Indexes and Constraints for Data Integrity
      - Designing Efficient Database Queries for Performance Optimization

Data Analysis using Excel

- 1. Fundamentals of Excel:
    1. - Introduction to Excel
      - Reading Data into Excel
      - Basic Data Manipulation in Excel
      - Arithmetic Manipulation in Excel
      - Basic Functions in Excel
      - Function Using Absolute and Relative Reference in Excel
      - Additional Useful Function in Excel
      - Conditional Statements in Excel
  2. Data Exploration with In-Built Functions:
    1. - Data Filtering in Excel
      - Data Validation in Excel
      - Use of Pivot Tables in Excel
      - Pivot Table Operations & Applications
      - Introduction to Charts in Excel
      - Excel Shortcuts
  3. Storytelling with Excel:
    1. - Introduction to storytelling with data
      - The importance of data visualization in storytelling
      - Choosing the right chart type for different data scenarios
      - Understanding the principles of good data visualization
      - How to use Excel to create compelling data visualizations
  4. Advanced Dashboarding Concepts:
    1. - Introduction to dashboards
      - The purpose and benefits of dashboards
      - Choosing the right chart types for different dashboard scenarios
      - Techniques for creating interactive dashboards in Excel
      - Creating dashboard templates for reuse
      - Sharing dashboards with stakeholders

Tableau for Professionals

- 1. Getting Started with Tableau Ecosystem:
    1. - Introduction to Tableau
      - Use of tableau for the company’s KPI
      - What are business KPIs?
  2. Choosing the Right Chart:
    1. - What Is a Chart and Types of Charts?
      - Line Chart
      - Bar Chart and Column Chart
      - Pie Chart
      - Area Chart
  3. Dashboarding & Storytelling with Tableau:
    - - Best Practices for Dashboard Design
      - Define your goal: Start by identifying the key insights or metrics that you want to communicate through your dashboard
      - Use appropriate visualizations: Choose the right charts and graphs to best represent your data
      - Make it interactive: Use filters and other interactive elements to allow users to explore the data
      - Keep it simple: Avoid using too many elements that may confuse or distract the viewer

Data-Driven Business Analysis with Power BI

- 1. Dashboarding with Power Bl:
    - - Introduction to creating and formatting table visualization
      - Formatting Our First visualization
      - Creating Different Visualizations: Matrices and Charts
      - Adding More Control to Your Visualizations
      - Introduction to Mapping
      - Measure Performance by Using Kpis, Gauges, and Cards
  2. Advanced Dashboarding Concepts with PowerBl:
    - - Introduction to Get and Transform Data
      - Introduction for Get Data – Home
      - Getting multiple files
      - Transform menu
  3. Customer & Web Analytics:
    1. 1. Introduction
      2. Understanding customer behavior and preferences through website data
      3. Key performance indicators (KPIs) for website analytics
      4. Analyzing website traffic and user engagement using tools like Google Analytics
      5. A/B testing and experimental design for website optimization
      6. Creating user personas and segments for targeted marketing -Cro techniques
      7. Identifying opportunities for upselling and cross-selling through website data
  4. Advanced Charts:
    - - Introduction
      - Visualization best practices and chart selection for different data types and analysis goals
      - Advanced chart types and techniques, such as heatmaps, sankey diagrams, and sparklines
      - Tools for creating advanced charts, such as D3.js, Plotly, and Tableau
      - Dashboard design principles and user experience considerations
      - Creating interactive charts and dashboards for exploratory data analysis
  5. Dashboarding with Business KPIs – Ecommerce:
    - - Key Performance Indicators (KPIs) for Ecommerce Businesses
      - Defining Business Goals and Objectives for E-commerce Dashboards
      - Selecting and Designing Appropriate Charts and Visualizations for E-commerce KPIs
      - Identifying Trends and Patterns in E-commerce Data Using Exploratory Analysis Techniques
      - Creating Data-Driven Recommendations for Improving Ecommerce Performance
      - Measuring the Impact of Marketing Campaigns and Other Initiatives on Ecommerce Metrics
      - Integrating Data from Multiple Sources into E-commerce Dashboards

Maths Essentials

- 1. Foundational Maths for DS:
    - - Introduction to Linear Algebra
      - Theory of Matrices
      - Determinant of a Matrix
      - Eigenvalues and Eigenvectors
  2. Advanced Maths for DS:
    - - Calculus
      - Differentiation
      - Integration
      - Maxima and Minima using derivatives
      - Partial Derivatives

Descriptive Statistics

- 1. Probability Theory:
    - - Introduction to probability theory
      - Conditional probability and Bayes theorem
      - Random variables and properties
  2. Data Summarization:
    - - Introduction to data summarization
      - Descriptive statistics and its uses
      - Measures of central tendency
      - Percentile and quartiles
      - Skewness and kurtosis
      - Outlier detection and treatment
  3. Discrete Probability Distributions:
    - - Probability mass function
      - Cumulative distribution function
      - Distribution
  4. Continuous Probability Distributions:
    - - Probability density function
      - Cumulative distribution function
      - Distribution
  5. Joint Distribution Concept:
    - - Joint probability mass function
      - Joint probability density function
      - Covariance and correlation
      - Multivariate normal distribution

Mastering Inferential Statistics

- 1. Sampling & Statistical Inference:
    - - Introduction to Sampling
      - Probability Sampling
      - Non-Probability Sampling
      - Sampling Bias and Estimation
      - Sample Size Determination and Sum of Independent Random Variables
  2. Concept of Confidence:
    - - Introduction to Confidence Levels
      - Interpreting Confidence Intervals
      - Choosing Appropriate Confidence Levels
  3. Hypothesis Testing:
    - - Hypothesis
      - Null and Alternative Hypotheses
      - Type I and Type II Errors
      - One-Tailed and Two-Tailed Tests
      - P-Values
  4. Experimental Design:
    - - Experimental Design
      - Types of Experimental Design
      - Hypothesis Testing
      - Power Analysis
      - Ethical Considerations

Unlocking Machine Learning

1. 1. Learning Objective:
    - - Introduction to ML
      - Supervised and Unsupervised Algorithms
      - Understand the Mechanisms Behind Machine Learning
  2. Mechanisms Behind Machine Learning:
    - - Introduction to mechanism of Supervised
      - Unsupervised and Deep Learning
  3. Supervised Learning – Regression:
    - - What are supervised models?
      - What is regression analysis?
      - Types of Regression Models
  4. Supervised Learning – Classification:
    - - Introduction to Supervised Models – Classification
      - Logistic Regression
      - What is logistic regression?
      - Implementing logistic regression
      - Evaluation Metrics used for Classification
      - Defining the cost function
  5. Decision Trees:
    - - What is a Decision Tree?
      - How to split?
      - Math Behind it
      - Simple Implementation
      - Advantages & Disadvantages
  6. Unsupervised Learning:
    - - What is Unsupervised Learning?
      - Introduction to Clustering
      - K-Means Clustering
      - Simple Example
      - Expectation-Maximization
      - Silhouette Analysis
      - Elbow Method
      - Implementation of K-Means
      - Limitations

Optimizing Models for Accuracy

1. 1. Data Preparation for ML Models:
    - - Introduction
      - Understanding the data: Data types, data quality, and data distribution
      - Handling missing values: Imputation techniques
      - Handling outliers: Detection and removal techniques
      - Dealing with categorical data: Techniques for encoding categorical variables
      - Data normalization and scaling: Techniques and their applicability
  2. Optimizing Model Performance With Validation:
    - - Introduction
      - Why Cross-Validation?
      - K-Fold Cross-Validation
      - Stratified K-Fold Cross-Validation
      - Trade-off between Train-Test Split and Cross-Validation
      - Optimizing the number of folds: Bias-variance tradeoff
      - Hyperparameter Tuning
  3. Feature Engineering for Interpretable Models:
    - - What is Feature Engineering?
      - Why do we need it?
      - What is feature understanding?
      - Basic EDA
      - Other methods for Feature Engineering
  4. Customer Segmentation – Case Study:
    - - What is customer segmentation?
      - Why segment customers?
      - What are the benefits of customer segmentation
      - Analysis of the customer segments

Beyond Simplicity – Artificial Intelligence

- 1. Bagging & Boosting:
    - - What are ensembles?
      - Bagging
      - Boosting
      - Stacking
      - Bagging vs Boosting
  2. Neural Networks:
    - - Understanding Neural Networks
      - Decoding how neural networks work?
      - Artificial Neural Networks
      - Implementing Neural Network
  3. Introduction to NLP:
    - - Introduction to NLP
      - Basic Concepts of NLP
      - Sentiment Analysis
      - Tokenization
      - Lemmatization
      - Stemming vs Lemmatization
      - Vectorization
  4. Image Processing (Self Paced):
    - - Introduction
      - Understanding digital images: Pixels, color spaces, and image representation
      - Image filtering: Techniques for enhancing image quality and removing noise
      - Object detection: Techniques for identifying and localizing objects in an image
      - Image segmentation: Techniques for separating the foreground and background regions in an image
      - Deep learning for image processing
  5. Time Series Analysis:
    - - Introduction
      - What is a time series data?
      - Stationarity and its importance
      - Smoothing Technique: Moving Average
      - Forecasting Techniques: ARIMA & SARIMA
  6. Recommender Systems:
    - - Introduction
      - Understanding recommendation systems: Collaborative filtering, Content-based filtering, and hybrid models
      - Understanding Recommendation Systems: Implementation of Collaborative Filtering, Content Based Filtering, Implementation of Content Based Filtering
      - Evaluation metrics for recommendation systems: Precision, recall, F1-score, and others
      - Real-world applications of recommendation systems: E-commerce, social media, and others.

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Python Beyond Basics

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Industry Projects

Project 1

Retail Analytics for Milan (Italian) Store

This study aims to investigate whether there is a statistically significant reduction in sales over a specific period for a given retail store. By applying hypothesis testing methods

Project 2

Pricing Strategy Modeling for a New US Automobile Brand

This project aims to develop a machine learning-based price prediction model for a new US automobile company planning to enter the Indian market.

Project 3

A Deep Learning approach to identify Patterns in Healthcare

This project leverages deep learning techniques to analyze healthcare data and identify hidden patterns that can aid in early detection and risk assessment of medical conditions.

Project 4 – 8

Using Tableau, PowerBI, ML and DL

The program includes a total of 8 academic projects carefully integrated within the core modules to reinforce learning through practical application.

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Data Science - Program Highlights

Unlock the power of data with Ethans Tech’s Data Science Program, designed for aspirants looking to master the most in-demand skills in the tech world. This program takes you from the fundamentals of data handling to advanced machine learning and AI applications, equipping you with the ability to extract meaningful insights and drive strategic decisions.

Up-Skill with 3-in-1 Certifications

Gain a competitive edge in the job market with our exclusive 3-in-1 certification bundle designed to validate your skills and elevate your resume:

🎓 E&ICT Academy IIT Guwahati Certification 💼 NexGen Internship Letter✅ Ethans Certificate

Gain a competitive edge in the job market with our exclusive 3-in-1 certification bundle designed to validate your skills and elevate your resume:

🎓 E&ICT Academy IIT Guwahati Certification

💼 NexGen Internship Letter

✅ Ethans Certificate

Ethan’s x E&ICT Academy IIT Guwahati Certificate

Ethans Certificate

NexGen Internship Certificate

Master 12+ In-demand Skills

Why to Join this Program:

Next Batch Starts on 16th September, 2025

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Join 1% Elite Cohort

Join the 1% Elite Club.

Step into the IIT learning environment and embrace the pride of excellence.

Take part in a comprehensive job orientation program conducted by E&ICT Academy IIT Guwahati.
Learn through a cutting-edge curriculum, thoughtfully designed in collaboration with IIT faculty.
Gain exclusive exposure to guest lectures delivered by experienced professors from E&ICT Academy IIT Guwahati.
Receive your official admission letter, student ID card, branded T-shirt, completion certificate symbolizing your association with the institution.

What Other Learners are Saying:

IT Experience -1+ Years

Enrolling in the DevOps course at Ethan's Tech was one of the best decisions I made for my career. First of all, a big thank you to Team Ethans and Kumar Sir for their incredible support. IVe successfully completed the Azure training and am extremely satisfied with the experience. Kumar Sir's teaching approach made it easy to understand Azure concepts, and the course content was well-structured and insightful. I highly recommend this training to anyone looking to learn Azure effectively!

IT Professional - 4+ Years

Kumar Sir at Ethans Tech is an outstanding instructor for Azure training in Pune. The course was well-structured and covered all essential topics thoroughly. Sir's deep expertise and engaging teaching style made the sessions highly effective. The hands-on exercises and interactive discussions added immense value to the learning experience. After completing the training, I feel well-equipped and confident in my Azure skills. Kudos to Ethans Tech for delivering such a high-quality program!

IT Professional -12+ Years

I had an excellent experience with Kumar Sir during the Azure training at Ethans. He covered everything from basics to advanced topics, ensuring every aspect of the course content was thoroughly explained. Each topic was taught in detail, making learning effective and engaging. A big thanks to the Ethans administration team as well for their seamless support and well-organized arrangements. every request I had was handled promptly during the learning journey. I strongly recommend to anyone aiming to build a career.

IT Experience -10+ Years

I was searching for Azure training in Pune and came across several options, but I chose Ethans Tech because of their experienced trainers and supportive team. The trainer's deep expertise made a big difference, and the Ethans team went above and beyond by conducting mock interviews and helping me build a strong portfolio—crucial for my job placement. Their assessment model was well- structured, and all my requests were addressed promptly throughout the learning process.

Ethan's Offices Tour

Ethans Tech is a leading professional training institute founded with the mission to bridge the gap between academic learning and real-world skills. With a strong presence in Pune and expanding across India, Ethans Tech has trained thousands of students and working professionals, helping them upgrade their careers in the most in-demand technologies.

The name “Ethans” represents a commitment to “education with excellence”. It’s not just a name; it’s a culture — built by passionate industry experts who believe in practical, hands-on learning rather than rote education. Every trainer at Ethan’s is a seasoned professional with real industry exposure, making the learning experience highly relevant, practical, and impactful.

At Ethans, it’s not just about completing a course — it’s about building a career.

Learners Profile

Our diverse and dynamic batch brings together individuals from various academic and professional backgrounds, creating a rich learning environment driven by collaboration and growth.

📘 11% – College Graduates (Non-technical backgrounds)
🛠️ 23% – B.Tech & M.Tech Graduates (CS, IT, ME, CIVIL)
💻 27% – BCA, B.Sc (IT/CS/Maths/Stats) Graduates
💼 23% – Early Career Professionals (1–6 years of experience)
🔄 16% – Career Comeback Learners (with a gap in education or employment)

This blend of learners adds immense value to the learning experience — offering unique perspectives, fresh ideas, and real-world context to every session

Learner Profiles & Trusted Companies

Batch Profile

Our diverse and dynamic batch brings together individuals from various academic and professional backgrounds, creating a rich learning environment driven by collaboration and growth.

📘 11% – College Graduates (Non-technical backgrounds)
🛠️ 23% – B.Tech & M.Tech Graduates (CS, IT, ME, CIVIL)
💻 27% – BCA, B.Sc (IT/CS/Maths/Stats) Graduates
💼 23% – Early Career Professionals (1–6 years of experience)
🔄 16% – Career Comeback Learners (with a gap in education or employment)

This blend of learners adds immense value to the learning experience — offering unique perspectives, fresh ideas, and real-world context to every session

Learner Profiles & Trusted Companies

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Data Science Certification Training FAQs

Who is this program meant for?

This program is ideal for college students, graduates (BCA, B.Sc, B.Tech, M.Tech), early professionals, and career-switchers who want to build a strong foundation in data science and launch a career in this high-demand field.

How are the classes scheduled?

Classes are held from Tuesday to Friday, combining live instructor-led sessions with hands-on, lab-based practice. Learners are expected to dedicate 4–5 hours per day to fully engage with the program content. Flexible scheduling options may be provided depending on the batch structure and availability.

Do I need any prior coding knowledge?

No prior coding or technical background is required to join this program. It is specifically designed to be beginner-friendly, starting with the fundamentals of Python programming, SQL for data handling, and essential statistical concepts. Whether you’re from a non-technical background or completely new to data science, the structured learning path ensures you build a strong foundation before moving into advanced topics like machine learning, data visualization, and AI.

Are there real-world projects included?

Absolutely. In addition to the 8 core academic modules, the program also includes multiple industry-relevant projects and a capstone project. These real-world assignments are designed to provide hands-on experience, reinforce your learning, and help you build a strong, job-ready portfolio that showcases your skills to potential employers.

What are the IIT Guest Lectures?

What is the NexGen Internship and when does it start?

Can I repeat the classes if I miss or need a revision?

Will I receive any certification?

Is there a placement support system?