Data Science Program

Data Science is a live, instructor-led program designed to build strong skills in data analysis, machine learning, and predictive modeling using modern data science tools and frameworks.

  • This 24-week structured journey includes real data science projects, 10+ hands-on workshops, 30+ real-world data use cases, and masterclasses with continuous mentorship.
  • You will work with industry tools such as Python, Pandas, NumPy, Scikit-learn, SQL, Jupyter Notebook, data visualization libraries, and machine learning frameworks.
  • The program also provides an option to pursue industry-recognized certifications including IBM Data Science Professional Certificate, Google Professional Data Engineer, Microsoft Azure Data Scientist Associate, and AWS Machine Learning Specialty.
Format Live Instructor-Led
Duration 24 Weeks | 180 Hours
Admission Deadline 30 April 2026
Case Studies & Projects 30+
Enroll Now

Key Program Takeaways

Build real data science capability through guided data labs, machine learning workflows, and project-based learning designed to solve complex real-world problems.

Data Analysis

Python, Pandas, Data Cleaning, Exploratory Data Analysis

Statistical Foundations

Probability, Hypothesis Testing, Statistical Modeling

Machine Learning

Supervised & Unsupervised Learning, Model Building

Data Visualization

Matplotlib, Seaborn, Insightful Data Storytelling

Data Engineering Basics

SQL, Data Pipelines, Data Processing

Capstone Portfolio

End-to-End Data Science Project with Real Datasets

List of Modules in this Program

01

Data Science Foundations

  • Introduction to data science concepts and the data science lifecycle.
  • Data collection, cleaning, and exploratory data analysis.
  • Working with datasets using Python and Jupyter Notebook.
02

Python for Data Science

  • Learn Python programming for data analysis.
  • Work with libraries such as Pandas, NumPy, and Matplotlib.
  • Perform data manipulation, transformation, and analysis.
03

Statistical Analysis

  • Understand probability, statistical distributions, and hypothesis testing.
  • Apply statistical methods to analyze patterns and trends.
  • Use statistics to support data-driven decision making.
04

Machine Learning Fundamentals

  • Introduction to supervised and unsupervised learning.
  • Build predictive models using Scikit-learn.
  • Model training, evaluation, and performance optimization.
05

Data Visualization & Storytelling

  • Create visualizations using Matplotlib, Seaborn, and dashboards.
  • Communicate insights effectively through data storytelling.
  • Build visual reports to explain analytical findings.
06

Capstone Project

  • Work with real-world datasets to build predictive models.
  • Apply the full data science workflow from data preparation to model deployment.
  • Present a portfolio-ready data science project with insights and predictions.

Hands-On Roadmap

Weeks 1–4

Data Science Foundations & Environment Setup

  • Install Python, Jupyter Notebook, and data science tools
  • Learn data cleaning and exploratory data analysis techniques
  • Work with structured datasets using Pandas and NumPy
  • Hands-on: Exploratory Data Analysis on Sales Data, Dataset Cleaning Project
Weeks 5–8

Data Analysis with Python

  • Use Pandas and NumPy for data manipulation and transformation
  • Perform statistical analysis and feature exploration
  • Create basic data visualizations using Matplotlib and Seaborn
  • Hands-on: Customer Behavior Analysis, Financial Dataset Insights
Weeks 9–12

Statistics for Data Science

  • Learn probability, distributions, and hypothesis testing
  • Apply statistical techniques to analyze trends and correlations
  • Perform feature engineering for predictive models
  • Hands-on: A/B Testing Analysis, Sales Trend Forecasting
Weeks 13–16

Machine Learning Fundamentals

  • Learn supervised and unsupervised learning methods
  • Train models using Scikit-learn
  • Perform model evaluation and optimization
  • Hands-on: Customer Churn Prediction, Prediction Model
Weeks 17–20

Advanced Data Science & Model Optimization

  • Feature selection and model tuning techniques
  • Introduction to deep learning and neural networks
  • Work with larger datasets and model pipelines
  • Hands-on: Recommendation System, Fraud Detection Model
Weeks 21–24

Capstone Data Science Project

Deliver a real-world data science solution using the complete workflow.
  • Data collection, preprocessing, modeling, and evaluation
  • Build predictive models and interpret results
  • Present insights and deploy models for practical use
  • Capstone project options:
  • Customer Churn Prediction Platform
  • Retail Demand Forecasting System
  • Personalised Recommendation Engine

Top Companies Hiring Data Scientists

Leading technology companies, AI labs, consulting firms, financial institutions, and digital platforms actively hire data scientists and machine learning engineers.

Amazon Google Microsoft Meta Apple IBM NVIDIA OpenAI McKinsey & Company BCG Bain & Company Deloitte Accenture Goldman Sachs JPMorgan Chase Morgan Stanley Flipkart Paytm Razorpay Swiggy Zomato PhonePe Infosys TCS Wipro Cognizant

Some of our exceptional outcomes with top companies.

Sai Arya

Sai Arya

₹65 LPA CTC

Nitish

Nitish

₹65 LPA CTC

Manurbhav A

Manurbhav A

₹21 LPA CTC

Aditya T

Aditya T

₹21 LPA CTC

Om Nakrani

Om Nakrani

₹65 LPA CTC

Yuvana T

Yuvana T

₹22 LPA CTC

Pardhiv A

Pardhiv A

₹21 LPA CTC

Phanindra B

Phanindra B

₹19 LPA CTC

Sai Gokul

Sai Gokul

₹65 LPA CTC

Abhigyan N

Abhigyan N

₹65 LPA CTC

Aryan T

Aryan T

₹21 LPA CTC

Devendra A

Devendra A

₹65 LPA CTC

Mahesh Reddy

Mahesh Reddy

₹65 LPA CTC

Prakul K

Prakul K

₹21 LPA CTC

Prashanth Reddy K

Prashanth K

₹65 LPA CTC

Rajeev T

Rajeev T

₹21 LPA CTC

Master Technologies

Core data science, machine learning, and data engineering technologies used throughout the program.

Python logoPython
Pandas logoPandas
NumPy logoNumPy
Jupyter logoJupyter Notebook
Scikit-learn logoScikit-learn
TensorFlow logoTensorFlow
PyTorch logoPyTorch
Matplotlib
Seaborn
SQL logoSQL
Apache Spark logoApache Spark
Hadoop logoHadoop
AWS Machine Learning
Azure Machine Learning
Google Cloud AI logoGoogle Cloud AI
R logoR Programming

Eligibility & Admission

A fully online, straightforward admissions process with advisor support throughout enrollment.

Who Can Apply Eligibility requirements for enrollment
  • Graduates in computer science, IT, mathematics, statistics, engineering, or related disciplines.
  • Final-year undergraduate students completing their degree before the program concludes.
  • Working professionals in analytics, software, consulting, and operations roles looking to build applied data science skills.
Admission Process Simple, structured steps from application to enrollment
  1. Application Submission: Complete a short online application with academic/professional details.
  2. Profile Review: Selected applicants receive official admission confirmation.
  3. Seat Confirmation: Reserve your seat with INR 10,000.
  4. Fee Completion: Pay the remaining fee within 7 days of confirmation or before program start, whichever is earlier.
Learner Assistance Advisor support throughout your admission journey

Program advisors are available 7 days a week, 10:00 AM to 7:00 PM.

Email: hello@42learn.com

Phone: 080 4736 3406

Disclaimer: Outcome, career progression, and salary information is indicative only; individual results vary by background, experience, and market conditions. Certificates/credits are governed by the issuing institution's policies where external partners are involved.