Unlock the Power of Data with Our Data Science Internship

Dive into real-world projects and master data analytics, machine learning, and visualization.

Build In-Demand Skills in Machine Learning, Python, and Data Analytics

Launch Your Career with a Data Science Internship

our Data Science Internship in Coimbatore offers the perfect launchpad to enter one of the most in-demand fields today. This internship is designed to provide hands-on training in core areas like Python programming, data analysis, machine learning, data visualization, and real-time project handling.

Why Choose a Data Science Internship?

Kickstart your tech career with a hands-on Data Science Internship in Coimbatore. Learn to turn data into insights using real-world tools, guided by industry experts.

Work on Real Projects & Case Studies

Build your portfolio with real-time data science projects. From data preprocessing to machine learning models, gain skills employers look for in fresh graduates.

Beginner-Friendly & Career-Switch Ready

Perfect for students, freshers, or professionals shifting careers. Learn data science from scratch, no prior coding required—just curiosity and dedication.

Learn Python, ML & Industry Tools

Master essential tools like Python, Pandas, SQL, Scikit-Learn, and more. This internship ensures you gain job-ready data science skills for today’s tech roles.

What You Will Learn?

Gain end-to-end data science skills—from Python to predictive analytics

Python- Basics & Advanced

  • Syntax, Variables, and Data Types
  • Conditional Statements & Loops
  • Functions and Basic Data Structures
  • Input/Output, Assignments & Assessments
  • Comprehensions (List, Dict, Set)
  • File Handling & Regular Expressions
  • Object-Oriented Programming
  • Pickling, Lambda, Map/Filter/Reduce
Front-End Development

Algorithmic Thinking with Python

  • Importance of Algorithmic Thinking in Problem Solving
  • Writing Clean and Efficient Python Code
  • Time and Space Complexity Basics
  • Introduction to Data Structures (Arrays, Lists, Stacks, Queues)
  • Searching and Sorting Algorithms (Linear, Binary Search, Bubble, Merge, etc
  • Recursion and Iterative Problem Solving
  • Memory Management & Optimization Techniques
  • Problem-Solving Strategies (Greedy, Divide & Conquer, Brute Force)
  • Problem-Solving Strategies (Greedy, Divide & Conquer, Brute Force)
Front-End Development

Data handling in Python – Pandas & MongoDB

  • Reading and Writing Data (CSV, Excel, JSON)
  • DataFrames and Series – Creation & Manipulation
  • Data Cleaning: Handling Missing Values, Duplicates
  • Data Cleaning: Handling Missing Values, Duplicates, Grouping, Aggregation, and Pivot Tables
  • Applying Functions and Lambda Operations, Merging, Joining, and Concatenation
  • Introduction to NoSQL & MongoDB
  • Key Components: Collections, Documents, BSON and Connecting MongoDB with Python (PyMongo)
  • Performing CRUD Operations (Create, Read, Update, Delete)
  • Performing CRUD Operations (Create, Read, Update, Delete)
Front-End Development

Probability and Statistics with NumPy

  • Basics of Probability Theory & Bayes’ Theorem
  • Types of Probability & Real-life Applications
  • Common Distributions (Normal, Binomial, Poisson, etc.)
  • Descriptive Statistics (Mean, Median, Mode, Variance, Std. Deviation
  • Inferential Statistics & Sampling Techniques
  • Hands-on Implementation with NumPy
  • Data Interpretation Using Statistical Methods

Data Visualisation in Python (Matplotlib, Seaborn)

  • Introduction to Data Visualization & Storytelling
  • Understanding Visual Patterns, Trends, and Correlations
  • Plotting with Matplotlib (Line, Bar, Pie, Histogram, etc.)
  • Advanced Visuals with Seaborn (Heatmaps, Pairplots, Boxplots)
  • Customizing Charts: Titles, Labels, Legends, and Styles
  • Multivariate & Comparative Data Visualization
  • Choosing the Right Plot for the Right Data
  • Building Effective Dashboards & Visual Narratives.
Python Basics and Advanced

Python – Basics & Advanced

  • Syntax, Variables, and Data Types
  • Conditional Statements & Loops
  • Functions and Basic Data Structures
  • Input/Output, Assignments & Assessments
  • Comprehensions (List, Dict, Set)
  • File Handling & Regular Expressions
  • Object-Oriented Programming
  • Pickling, Lambda, Map/Filter/Reduce
Algorithmic Thinking with Python

Algorithmic Thinking with Python

  • Importance of Algorithmic Thinking in Problem Solving
  • Writing Clean and Efficient Python Code
  • Time and Space Complexity Basics
  • Introduction to Data Structures (Arrays, Lists, Stacks, Queues)
  • Searching and Sorting Algorithms (Linear, Binary Search, Bubble, Merge, etc.)
  • Recursion and Iterative Problem Solving
  • Memory Management & Optimization Techniques
  • Problem-Solving Strategies (Greedy, Divide & Conquer, Brute Force)
Data Handling in Python - Pandas & MongoDB

Data Handling in Python – Pandas & MongoDB

  • Reading and Writing Data (CSV, Excel, JSON)
  • DataFrames and Series – Creation & Manipulation
  • Data Cleaning: Missing Values, Duplicates, Grouping, Aggregation
  • Pivot Tables and Lambda Functions
  • Merging, Joining, and Concatenation
  • Introduction to NoSQL & MongoDB
  • MongoDB Collections, Documents, BSON, PyMongo
  • CRUD Operations (Create, Read, Update, Delete)
Probability and Statistics with NumPy

Probability and Statistics with NumPy

  • Basics of Probability Theory & Bayes’ Theorem
  • Types of Probability & Real-life Applications
  • Common Distributions (Normal, Binomial, Poisson, etc.)
  • Descriptive Statistics (Mean, Median, Mode, Variance, Std. Deviation)
  • Inferential Statistics & Sampling Techniques
  • Hands-on Implementation with NumPy
  • Data Interpretation Using Statistical Methods
Data Visualisation in Python (Matplotlib, Seaborn)

Data Visualisation in Python (Matplotlib, Seaborn)

  • Introduction to Data Visualization & Storytelling
  • Understanding Visual Patterns, Trends, and Correlations
  • Plotting with Matplotlib (Line, Bar, Pie, Histogram, etc.)
  • Advanced Visuals with Seaborn (Heatmaps, Pairplots, Boxplots)
  • Customizing Charts: Titles, Labels, Legends, and Styles
  • Multivariate & Comparative Data Visualization
  • Choosing the Right Plot for the Right Data
  • Building Effective Dashboards & Visual Narratives

Explore Learning from Data Science Internship

Discover how data drives decisions in today’s tech-driven world through our immersive Data Science Internship. Learn to collect, clean, and analyze real-world datasets using tools like Python, Pandas, and Power BI. Dive into core concepts such as data visualization, statistical modeling, and basic machine learning—all through practical, hands-on projects.

Why Choose SkillRadar’s Internship?

Gain hands-on experience in data analysis, visualization, and machine learning with expert mentorship.

  • Start with the basics and progress to advanced data science tools and techniques.
  • Work on real-world datasets and build industry-relevant projects.
  • Master Python, Pandas, NumPy, Scikit-Learn, MongoDB, Matplotlib, Seaborn & more.

More Than Just Learning

At SkillRadar, our Data Science Internship isn’t just about grasping concepts — it’s about transforming knowledge into real-world impact. You’ll explore Python, statistics, and machine learning while understanding how data science powers industries like finance, education, HR, and manufacturing.

Don't miss this opportunity to advance your career in Data Science

Don't Miss This
Opportunity

Real-World Application

Work with actual datasets and solve practical problems inspired by industry use-cases.

Hands-on Project Experience

Build multiple data science projects from scratch — from data cleaning to model deployment.

Get Certified & Job-Ready

Earn an industry-recognized certification while receiving mentorship, and resume building.

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Choose Your Path to Success

Your Questions, Our Answers

Frequently Asked Questions

Data Science is a multidisciplinary field that helps organizations make sense of complex data. It involves techniques from statistics, computer science, and machine learning to gather, process, and analyze data for actionable insights.

What does a Data Scientist do?

Data Scientists analyze large datasets using Python, statistics, and machine learning to uncover insights, predict trends, and support smarter business decisions.

Python is the top choice for data science due to its simplicity and vast libraries, making it ideal for beginners. R is powerful for statistical analysis but more complex. SQL is essential for managing data in relational databases. While languages like Java, C++, and Scala are also used, knowing Python or R along with SQL is key for most data science roles.

Data Science is a highly rewarding career with strong demand across industries like healthcare, finance, and tech. It offers high-paying roles, job security, and growth opportunities in areas like AI, machine learning, and analytics.

Step into the world of Data Science with SkillRadar’s immersive internship program.

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