Data Science Course

AT A GLANCE

Data Science Course snapshot

Practical Learning Learn data science concepts through hands-on exercises and real-world datasets

Industry-Relevant Skills Build skills in Python, SQL, statistics, data analysis, visualization and machine learning

Real-World Projects Work on guided projects that take you from data preparation to analysis and model evaluation.

Complete Learning Path Progress from data science fundamentals to machine learning through a structured curriculum.

Portfolio Development Build practical projects that can demonstrate your data science skills to employers.

Career Preparation Develop technical, analytical and interview skills for data-focused career opportunities.

AT A GLANCE

Course highlights

LevelBeginner

Duration6 Months

Learning hours300 hours

Total classes40

Learning formatClassroom

Course languageEnglish + Hindi

THE OVERVIEW

About this course

Learn how to turn raw data into meaningful insights, predictive models and practical business solutions with a structured data science course designed around hands-on learning.

The course covers the complete data science workflow, from collecting and cleaning data to exploratory data analysis, statistics, visualization, machine learning and project development. You will work with Python and commonly used data science tools while learning how to approach real-world datasets systematically.

Rather than focusing only on theory, the training connects concepts with practical exercises and guided projects. You will learn how to understand a business problem, prepare relevant data, explore patterns, select suitable techniques, evaluate results and communicate your findings clearly.

BUILD YOUR SKILLS

What you will learn

Python fundamentals for data science
NumPy and Pandas for data manipulation
Exploratory Data Analysis (EDA)

LEARN BY DOING

Practical projects

Sales Data Analysis Project

Analyze a sales dataset using Python and Pandas. Clean the data, explore sales trends, identify high-performing products and categories, create visualizations and present actionable findings

Customer Churn Prediction

Prepare a customer dataset, explore factors associated with churn, engineer relevant features and build a classification model. Evaluate the model and interpret the results.

Customer Segmentation Project

Use exploratory analysis and clustering techniques to identify groups of customers with similar characteristics. Visualize the segments and explain how the findings could support business decisions.

End-to-End Data Science Project

Follow a complete data science workflow from problem definition and data preparation to exploratory analysis, feature engineering, machine learning, model evaluation and final presentation

YOUR TOOLKIT

Tools & technologies

Python
pandas
NumPy
SQL
matplotip
seaborn
Jupyter Notebook
Git
github
Excel

BEFORE YOU BEGIN

Who should join?

Students who want to learn data science
Beginners starting a career in data
Graduates looking to develop practical technical skills
Professionals planning a career transition into data-related roles
Python learners interested in applying programming to data
Analysts who want to expand into machine learning
Entrepreneurs and professionals interested in data-driven decision making

Prerequisites

Basic computer skills and school-level mathematics are recommended. No prior professional data science experience is required. Beginners can start with the foundational modules before moving into advanced topics.

CAREER GUIDE

Career roadmap

What Does a Data Scientist Do?

A data scientist works with data to uncover patterns, generate insights and develop solutions for business and technical problems. Their work can include collecting and cleaning data, exploratory analysis, visualization, statistical analysis, machine learning, model evaluation and communicating findings to stakeholders.

Skills Needed for a Data Science Career

A strong data science foundation combines Python programming, statistics, SQL, data analysis, visualization and machine learning. Problem-solving, analytical thinking, communication and the ability to explain technical findings are also important professional skills.

Where Can Data Science Skills Lead?

Data science skills can support career paths across data analysis, business analytics, machine learning and other data-focused roles. With experience, professionals can specialize in areas such as predictive analytics, artificial intelligence, natural language processing or advanced machine learning.

Data Science Career Roadmap

Start with Python and data handling, build a foundation in statistics and SQL, develop exploratory analysis and visualization skills, then progress into machine learning and model evaluation. Practical projects and portfolio development can help turn these skills into career-ready experience

LEARNING PATH

Step-by-step roadmap

Phase 1

Data Science Foundations

Understand what data science is, how the data science lifecycle works, different types of data, common problem-solving approaches and the role of data in business and technology.

Phase 2

Python for Data Science

Build Python fundamentals including variables, data types, conditions, loops, functions and data structures, then apply Python concepts to basic data-focused tasks

Phase 3

Data Handling & Analysis

Learn to work with datasets using NumPy and Pandas. Practice importing, cleaning, transforming, filtering, grouping and analyzing structured data

Phase 4

Data Handling & Analysis

Learn to work with datasets using NumPy and Pandas. Practice importing, cleaning, transforming, filtering, grouping and analyzing structured data.

Phase 4

Statistics & Data Understanding

Develop statistical reasoning through descriptive statistics, probability, distributions, sampling, correlation and other concepts used to understand and analyze data.

Phase 5

Exploratory Data Analysis & Visualization

Explore datasets to identify trends, relationships and anomalies. Create meaningful visualizations and communicate analytical findings using Python visualization tools.

Phase 6

SQL for Data Work

Learn how to retrieve and analyze structured data using SQL, including filtering, aggregation, grouping, joins and subqueries.

Phase 7

Machine Learning Fundamentals

Understand the machine learning workflow, supervised and unsupervised learning, feature preparation and how algorithms can be applied to practical data problems.

Phase 8

Model Building & Evaluation

Build and evaluate machine learning models using appropriate metrics. Learn about training and testing data, overfitting, underfitting and model comparison

Phase 9

Real-World Data Science Projects

Apply the complete workflow to practical projects, from defining a problem and preparing data to analysis, modeling, evaluation and presenting results

Phase 10

Portfolio & Career Preparation

Organize project work into a professional portfolio, strengthen project explanations, practice data science interview topics and prepare for data-focused career opportunities.

SKILLS YOU BUILD

What you will learn

Python fundamentals for data science
NumPy and Pandas for data manipulation
Exploratory Data Analysis (EDA)

JOB OUTLOOK

Career opportunities

Data Scientist 4 LPA – 12 LPA+

Junior Data Scientist 3 LPA – 7 LPA

Data Analyst 3 LPA – 8 LPA

Business Analyst 3 LPA – 8 LPA

Machine Learning Engineer 4 LPA – 12 LPA+

Machine Learning Associate 3 LPA – 8 LPA

Data Science Intern 10,000 – 35,000/month

Analytics Associate 3 LPA – 7 LPA

Data Scientist 4 LPA – 12 LPA+

Junior Data Scientist 3 LPA – 7 LPA

Data Analyst 3 LPA – 8 LPA

Business Analyst 3 LPA – 8 LPA

Machine Learning Engineer 4 LPA – 12 LPA+

Machine Learning Associate 3 LPA – 8 LPA

Data Science Intern 10,000 – 35,000/month

Analytics Associate 3 LPA – 7 LPA

GROWTH

Career progression

Build Core Data Skills

Develop a strong foundation in Python, SQL, statistics, data handling and visualization to work confidently with real-world datasets.

Gain Practical Experience

Apply your skills through hands-on exercises and projects involving data cleaning, exploratory analysis, visualization and problem solving.

Develop Machine Learning Skills

Progress from data analysis into machine learning by learning model development, feature preparation, evaluation and predictive techniques.

Build a Strong Project Portfolio

Create and document practical data science projects that demonstrate your ability to take a problem from raw data to meaningful results.

Prepare for Data-Focused Roles

Strengthen your resume, project presentation and interview skills while preparing for entry-level opportunities in data and analytics

Specialize and Advance

With professional experience, develop deeper expertise in areas such as machine learning, artificial intelligence, advanced analytics or specialized data science.

INDUSTRY FIT

Where this skill fits

Information Technology Banking & Financial Services Healthcare E-commerce & Retail Telecommunications Manufacturing Consulting Logistics & Supply Chain Education & EdTech Marketing & Advertising

Related roles

Data Analyst Business Analyst Machine Learning Engineer Data Engineer Business Intelligence Analyst AI Engineer Analytics Consultant

Why classroom training works

Instructor-led learning
Structured curriculum
Hands-on practice
Guided project development
Doubt-solving support
Practical exercises
Career-oriented learning
Classroom interaction

FAQ

Common questions

A data science course teaches you how to work with data using programming, statistics, data analysis, visualization and machine learning. The learning process typically covers the complete journey from data preparation and exploration to model development and evaluation.

Yes. The course is structured to introduce foundational concepts before progressing to data analysis, statistics and machine learning. Basic computer skills and school-level mathematics are recommended.

No. Advanced Python knowledge is not required to begin. Python fundamentals can be learned as part of the course before moving into data analysis and machine learning applications.

You will learn Python, NumPy, Pandas, SQL, statistics, data cleaning, exploratory data analysis, data visualization, machine learning, model evaluation and practical project development.

Depending on the curriculum, projects can include sales analysis, customer segmentation, customer churn prediction, exploratory data analysis and end-to-end machine learning projects.

Yes. Statistics helps you understand datasets, identify relationships, evaluate results and make more reliable data-driven conclusions. A strong foundation in statistics is an important part of learning data science.

Data analytics focuses primarily on examining data to discover insights and support decisions. Data science covers analytics as well as areas such as statistical modeling, machine learning and predictive analysis.

Depending on your skills and experience, data science knowledge can support career paths such as Data Scientist, Data Analyst, Business Analyst, Machine Learning Engineer and other data-focused roles.

The time required depends on your starting knowledge, learning pace and amount of practice. A structured learning path combined with regular hands-on practice and projects can help you build a strong foundation.

No course can guarantee employment. Career outcomes depend on factors such as technical skills, practical experience, projects, interview performance, qualifications and the requirements of individual employers.

Yes. Machine learning is an important area of data science, but data science also includes data preparation, statistics, exploratory analysis, visualization, problem formulation and communicating insights.

A computer science degree is not the only pathway into data-related careers. Learners from different educational backgrounds can develop relevant programming, statistical, analytical and project skills and pursue suitable data-focused opportunities.

Common myths

You need advanced mathematics to start data science.
Data science is only machine learning.
You need to be an expert programmer.
Certificates alone guarantee a data science job.

Beginner mistakes to avoid

Trying to learn machine learning before understanding data preparation
Ignoring statistics
Memorizing Python syntax without practicing
Working only with tutorials instead of real datasets
Focusing on algorithms without understanding the problem
Not evaluating machine learning models properly
Building projects without documenting the process
Learning too many tools without developing core skills

Interview topics

Python fundamentals NumPy and Pandas Data cleaning Exploratory Data Analysis Statistics Probability SQL Data visualization Feature engineering Machine learning algorithms Supervised vs unsupervised learning Model evaluation Overfitting and underfitting Classification and regression Project-based questions Data interpretation