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Best Online Data Science Course in India

Want to kickstart and build a career in one of the most demanded fields today? Enroll in India’s most comprehensive and best online course on data science.

Throughout the course, you will learn data science online from the industry’s top mentors, from basic to advanced modules. The full course covers six months of comprehensive data science training with Python, Machine Learning, Deep Learning, as well as Data Analytics. We also assist you in job placement and career growth.

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  • job-guarantee-icon
    1.5 Lakh+
    Aspirants Mentored
  • job-guarantee-icon
    350+
    Hiring
    Partners
  • job-guarantee-icon
    40+
    Industry
    Mentors
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Success Stories of Our Alumni

Our alumni now drive campaigns, manage brands, and lead growth in top agencies, startups, and global companies, with massive jumps in skills, salaries, and success.

Under WsCube’s Mentorship
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Curriculum at a Glance

Designed with insights from industry hiring managers & AI-driven industry demands.

  • Milestone
  • durationSeven
  • Duration
  • durationProgram Duration
    2 Months
  • Mode
  • durationOnline
  • Live Sessions
  • duration45+ hrs
  • Projects
  • duration2+
Placement Support
Milestone 1 - Duration: 4 weeks

Build strong programming and database fundamentals through Python, pandas, and SQL. Learn how to clean, query, transform, and prepare real datasets like a true data professional.

Week 1

Python Essentials & Data Structures

  • Installing Python and setting up Jupyter
  • Understanding variables and data types
  • Using operators and expressions
  • Writing control flow (if/else, loops)
  • Handling errors and exceptions
  • Using lists, tuples and dictionaries
Week 2

Advanced Python & Pandas Basics

  • Creating and reusing functions
  • Writing efficient list comprehensions
  • Understanding pandas Series and DataFrames
  • Loading data from CSV/Excel files
  • Filtering, sorting and selecting data
Week 3

SQL Fundamentals

  • Understanding relational databases
  • Writing basic SELECT queries
  • Filtering data with WHERE
  • Sorting and limiting results
  • Aggregating data with GROUP BY and HAVING
  • Joining multiple tables
Week 4

SQL Advanced

  • Writing subqueries
  • Using Common Table Expressions (CTEs)
  • Applying window functions (ROW_NUMBER, RANK, etc.)
  • Basics of query optimization

Project:

Apollo Hospitals Patient Flow SQL Lab

Analyze real hospital admissions & discharge data to uncover operational bottlenecks.Write SQL queries to track wait times, readmission rates, bed utilization & patient flow efficiency.

Case Study

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Nike Customer Purchase Analyzer

Build a Python-based tool to analyze customer purchase behaviour using CSV/Excel datasets, extracting insights with pandas.

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Swiggy Orders Analysis

Write advanced SQL queries with joins & aggregations to uncover operational insights for Swiggy’s delivery workflows.

Milestone 2 - Duration: 3 weeks

Master the art of cleaning messy data, exploring patterns, and extracting insights. Use Python visualizations and BI dashboards to tell compelling data stories.

Week 5

Data Cleaning & Preprocessing

  • Handling missing values
  • Identifying and removing duplicates
  • Detecting and treating outliers
  • Converting data types correctly
  • Working with dates and times
  • Creating derived features
Week 6

Python Visualization & EDA

  • Performing univariate analysis
  • Exploring relationships between variables
  • Creating histograms and boxplots
  • Building bar and line charts
  • Using pair plots for multivariate views
  • Applying visualization best practices
Week 7

BI Dashboards & Storytelling

  • Understanding measures vs dimensions
  • Defining business KPIs
  • Designing data models for BI tools
  • Adding slicers and interactive filters
  • Building executive‑ready dashboards

Project:

JioHotstar Exploratory Data Analysis (EDA)

  • Perform deep exploratory analysis on JioHotstar’s viewer engagement data.
  • Identify patterns, peak usage hours, audience behaviour & content preferences using Python visualizations.

Radisson Executive Performance Dashboard

Build a business-grade hotel performance dashboard with KPIs like RevPAR, occupancy & cancellation metrics. Add interactive slicers for hotel category, city, channels & segments for executive decision-making.

Case Study

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Zomato Order Quality Insights

Work with raw operational data to improve quality checks, detect anomalies and design reliable features for downstream ML.

Milestone 3 - Duration: 5 weeks

Learn the core statistical thinking behind real-world ML problems. Build, evaluate, and refine regression and classification models used across industries.

Week 8

Descriptive Statistics & Distributions

  • Calculating mean, median and mode
  • Measuring spread with variance and standard deviation
  • Understanding quantiles and percentiles
  • Recognizing the normal distribution
  • Interpreting correlation between variables
Week 9

Probability & Hypothesis Testing

  • Learning basic probability rules
  • Understanding sampling and sample size
  • Building confidence intervals
  • Running t‑tests and chi‑square tests
  • Interpreting p‑values
  • Framing and analyzing A/B tests
Week 10

ML Foundations & Regression

  • Understanding supervised learning concepts
  • Splitting data into train and test sets
  • Applying cross‑validation
  • Building linear regression models
  • Scaling features where required
  • Evaluating models with regression metrics
Week 11

Regularization & Regression Refinement

  • Understanding overfitting and underfitting
  • Applying Ridge regression
  • Applying Lasso and elastic net
  • Handling multicollinearity
  • Using learning curves for diagnostics
Week 12

Classification Basics

  • Introducing classification problems
  • Building logistic regression models
  • Using k‑Nearest Neighbors (k‑NN)
  • Training decision trees
  • Evaluating with accuracy, precision and recall, Using F1‑score and ROC‑AUC
  • Understanding Time Series Analysis (Basics)

Project:

Spotify Listener Stats Summary

  • Study how Spotify users interact with playlists, regions & genres.
  • Perform descriptive analytics to derive insights on consumption behavior & engagement trends.

Uber Fare Prediction Model

  • Build a regression-based ML model to predict fare prices using historical trip data.
  • Engineer features, validate models & deliver a prediction engine ready for real-world scenarios.

Case Study

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Amazon Homepage Experiment

Conduct an A/B test to evaluate a new homepage design using real statistical methods & conversion insights.

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Swiggy Order Churn Classifier

Build a churn prediction model using customer & order data to help Swiggy retain users effectively.

Milestone 4 - Duration: 5 weeks

Level up with ensemble methods, clustering, PCA, and text-intelligence foundations. Choose between NLP or Deep Learning to build more advanced AI-driven systems.

Week 13

Tree Ensembles & Feature Engineering

  • Understanding ensemble learning
  • Training random forest models
  • Using gradient boosting methods
  • Interpreting feature importance
  • Encoding categorical variables
  • Building pipelines for preprocessing + modeling
Week 14

Unsupervised Learning

  • Introducing clustering problems
  • Applying k-means clustering
  • Choosing k and reading silhouette scores
  • Understanding high-dimensional data
  • Reducing dimensions with PCA
  • Interpreting explained variance
Week 15

Deep Learning or NLP Track

  • Understanding neural network building blocks
  • Learning activation and loss functions
  • Grasping gradient descent and backpropagation
  • Cleaning and tokenizing text (NLP track)
  • Representing text with TF-IDF
  • Building simple text classification models
Week 16

Hyperparameter Tuning & Experimentation

  • Understanding hyperparameters vs parameters
  • Running grid search for tuning
  • Using random search for efficiency
  • Reading validation curves
  • Applying early stopping to prevent overfitting
  • Tracking experiments and results
Week 17

Model Evaluation & Interpretability

  • Performing detailed error analysis
  • Checking model calibration
  • Interpreting feature importance outputs
  • Using partial dependence ideas
  • Introducing fairness and bias concepts

Project:

Netflix Content Recommender & View Forecasting

Design a hybrid recommender system + time-series forecasting pipeline for trending content. Use watch history, ratings & temporal data to suggest shows & predict future view counts.

Mercado Livre E‑commerce Delivery Tuning Lab

As a data scientist for Mercado Livre, a leading Brazilian e‑commerce marketplace, refine a model that predicts whether an order will arrive late using the Brazilian e‑commerce dataset

Case Study

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Myntra Product Return Risk Model

Use ensemble algorithms to predict the probability of returns and reduce logistics & delivery costs for Myntra.

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MakeMyTrip Review Classifier

Build an NLP/DL-based model to classify hotel reviews as positive or negative to enhance customer experience.

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ICICI Bank Loan Fairness Audit

Evaluate a loan approval ML system for fairness, bias & model transparency - essential for responsible AI.

Milestone 5 - Duration: 3 weeks

Go beyond modeling by learning how real companies manage pipelines, warehouses & scalable systems. Deploy ML models as APIs and understand how production workflows operate.

Week 18

Data Pipelines & ETL

  • Understanding ETL vs ELT workflows
  • Ingesting data from files, APIs and databases
  • Designing end-to-end data workflows
  • Adding validation checks in pipelines
  • Implementing logging and basic monitoring
Week 19

Databases, Warehousing & Big Data

  • Understanding database design
  • Schemas – Star & Snowflake
  • Understanding OLTP vs OLAP
  • Introducing data warehousing concepts
  • Getting an overview of Hadoop and Spark
Week 20

Model Serving & APIs

  • Learning REST API fundamentals
  • Serializing and saving ML models
  • Designing batch inference workflows
  • Designing real-time inference workflows
  • Considering basic security for ML APIs

Project:

CRED Open-Banking Scoring Platform

  • Build an end-to-end data engineering + ML system connected to an Open Banking API.
  • Design pipelines, scoring workflows & risk analysis modules for fintech credit decisions.

Case Study

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Airbnb Data Warehouse Blueprint

Design a scalable warehouse architecture for bookings, stays, host behavior & guest analytics using modern data modeling.

Milestone 6 - Duration: 4 weeks

Build interactive data applications, add monitoring layers, and understand responsible AI. Work with LLMs, RAG, prompt engineering & GenAI fundamentals to stay future-ready.

Week 21

Data Apps & Model Monitoring

  • Building interactive apps around models
  • Designing prediction input forms and outputs
  • Combining visualizations with model results
  • Tracking model performance over time
Week 22

Advanced Topics & Electives

  • Understanding time series forecasting – advanced
  • Learning recommendation system approaches
  • Exploring advanced NLP tasks
  • Introducing modern language models
Week 23

Data Ethics, Governance & Privacy

  • Recognizing different types of model bias
  • Evaluating fairness in ML decisions
  • Improving interpretability of models
  • Understanding data privacy regulations (GDPR/CCPA)
  • Learning core data governance principles
Week 24

Generative AI & Large Language Models

  • Understanding how large language models work
  • Learning effective prompt engineering techniques
  • Knowing when and how to fine-tune models
  • Introducing RAG (Retrieval-Augmented Generation)
  • Applying ethical and responsible GenAI practices

Project:

Spotify Listening Analytics Platform (End-to-End)

  • Build a complete analytics and prediction platform integrated with Spotify’s Web API.
  • Ingest, preprocess, model & visualize listening patterns using a modern data architecture.

GenAI Assistant for FinBank Knowledge Hub

  • Create a RAG-based internal Q&A tool for financial knowledge documents.
  • Use LLMs to answer policy, product, compliance & FAQ queries intelligently.

Case Study

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CityStay AI Experience Lab

Build AI-driven guest experience enhancements for a global hospitality brand using advanced data workflows.

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Ethical AI Governance at MedCare Hospitals

Audit healthcare ML systems for fairness, transparency & privacy to ensure responsible AI adoption.

Milestone 7

A capstone project where learners solve a real business problem using data, analytics, and selective AI assistance. You will work on an end-to-end data science use case, from understanding the problem to analyzing data, building models, and generating insights that support business decisions. AI tools may be used to assist analysis, improve efficiency, and enhance insights, similar to how data teams work in modern organizations.

Business Problem & Data Understanding

  • Identify a real-world business problem and understand the data available to solve it.
  • Use data exploration and AI-assisted questioning to define objectives, success metrics, and scope.

Data Extraction & Preparation

  • Work with structured datasets using SQL and Python.
  • Clean, transform, and prepare data for analysis and modeling.

Exploratory Analysis & Insights

  • Perform exploratory data analysis to uncover trends, anomalies, and relationships.
  • Use visualizations, statistics, and AI-assisted summaries to highlight key insights.

Model Building & Evaluation

  • Apply machine learning techniques to address the business problem.
  • Train, evaluate, and compare models using appropriate metrics, with AI support for experimentation and tuning.

Insights, Decisions & Recommendations

  • Translate analysis and model outputs into clear business insights.
  • Use AI tools to help structure recommendations and explain results in business language.

Final Presentation

  • Present the complete solution, approach, and outcomes.
  • Demonstrate both analytical thinking and responsible use of AI in decision-making.
Bonus

Excel

  • Understanding data analysis concepts and distinct data types
  • Mastering Excel interface, validation, and formatting tools
  • Organizing data with sorting, filtering, and cleaning rules
  • Applying text, logical, numeric, and date function formulas
  • Transforming raw data using Power Query Editor cleaning tools
  • Merging files and automating workflows with advanced queries
  • Building data models with Power Pivot relationships and keys
  • Summarizing insights using Pivot Tables and custom measures
  • Visualizing trends with standard Excel charts and graph tools
  • Designing interactive dashboards using slicers and buttons

Be in the Spotlight with a Certification that Speaks for You!

Prove your skills, boost credibility, and get one step closer to your dream role.

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Industry-Recognized Certificate

A badge trusted by top companies.

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Stand Out in the Job Market

Turn your profile into a recruiter magnet.

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Your Passport to Career Growth

Step into better roles and higher salaries.

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Industry-Recognized Certificate

A badge trusted by top companies.

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Stand Out in the Job Market

Turn your profile into a recruiter magnet.

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Your Passport to Career Growth

Step into better roles and higher salaries.

Fee structure of this program

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    Total Program Fee:

    ₹40,000/-

    ₹30,000/-

    • Live instruction from Industry Veterans
    • Official certification in Human Resources
    • Vibrant community just like a College Campus
    • Hand-on curriculum with Real-Life Projects

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Data Science Course FAQs

Here’s everything you may ask.

This program is designed for students, working professionals, career switchers, and tech enthusiasts who want to build a strong, practical career in Data Science, Machine Learning, and AI—even if they are starting from basics.
No prior Data Science experience is required. Basic familiarity with programming or logical thinking is helpful, but the program starts from core foundations and gradually moves to advanced AI topics.
This is a Mentorship Program, not just a recorded course. You’ll learn through live sessions, mentor-led jam sessions, guided projects, mock interviews, and continuous feedback from industry experts.
Unlike theory-heavy programs, this mentorship focuses on:
  • Real-world datasets & case studies
  • End-to-end projects (build → deploy → showcase)
  • AI, ML, LLMs & modern workflows
  • Strong placement & career preparation support
You don’t just learn—you build and prove your skills.
You’ll work on industry-inspired projects such as:
  • Predictive & classification models
  • Recommendation & forecasting systems
  • ML pipelines & APIs
  • Dashboards & data apps
  • Generative AI & LLM-based applications
Each milestone ends with a portfolio-ready project.
Yes. Upon successful completion, you’ll receive a WsCube Tech Certificate of Completion, which validates your skills and projects in Data Science & AI.
Yes. Once you are eligible for placements, a dedicated placement services team works with you to identify opportunities, prepare you, and line you up with recruiters. You will continue receiving placement support for up to six months from the final session of the bootcamp—or until you get placed, whichever comes first.
Graduates typically apply for roles such as:
  • Data Scientist
  • Machine Learning Engineer
  • AI Engineer
  • Data Analyst
  • Applied AI / GenAI Engineer
  • Duration: 7 Months
  • Format: Live Online Sessions
  • Includes: Live classes, jam sessions, projects, assignments, mock interviews, and community support
Yes. You’ll get:
  • Live mentor access
  • AI-focused Discord community
  • Continuous doubt-solving, feedback, and peer learning
You’re never learning alone.
On average, learners dedicate 8–12 hours per week, including live sessions, practice, assignments, and project work.
You can click to ‘Talk to Program Advisor’ or ‘Download Curriculum’ directly from the landing page. Our team will guide you through eligibility, batch details, and next steps.

Data science is the field that brings together statistics, scientific methods, data analysis, as well as machine learning (ML), and artificial intelligence (AI). The purpose of data science is to find value from heaps of data from websites, customers, smartphones, sensors, software, etc.

 

The job profile in data science is usually a data scientist. A data scientist’s work is to utilize his skills for analyzing data, cleansing, aggregating, and manipulating it. This data analysis helps in making data-driven business decisions and uncovering unknown patterns.

The primary subjects covered in the data science and analytics courses are Python, Machine Learning, Deep Learning, Data Analytics, and Artificial Intelligence (AI).
While it is not necessary to have professional knowledge of programming or technical stack, but if you have some basic knowledge, it is an add-on and helps you learn data science fast.
In order to become a data scientist, you must have the right skills and command of several subjects and technologies. These include Python, data analytics, machine learning, deep learning, etc. To start with, you must enroll in the best data scientist certification course. Then you can get placement or get hired by top companies in the country.
The job role of a data scientist is to collect large amounts of data and analyze it intelligently. With the right skills, you can implement your analytics to solve critical challenges for businesses, customers, and other problems.

Since it is still one of the unexplored IT fields in India, many people wonder:

  • Is there a demand for data scientists in India?
  • Is it hard to get a data science job in India?

The answer is that it is one of the top careers in the country and abroad today. Skilled data scientists are in high demand. Startups to SMBs to large organizations are looking for qualified candidates in their teams.

There are dedicated data analytics companies providing services to other organizations. By doing data science with Python course and practicing analysis of data, you can grab these opportunities.

A few of the top companies hiring data scientists include LensKart, Microsoft, Accenture, Oracle, Pinterest, Slack, Intel, Uber, Ernst & Young (EY), IBM, Aditya Birla Group, etc.

You can enroll in our online content writing course and gain the right skills to start your career without any prior experience.

 

The average data scientist salary in India is INR 7.00 LPA. A fresher’s salary starts at INR 5 LPA, whereas someone with 1-4 years of experience can make INR 6 to 10 LPA. Data scientists with 5+ years of experience make more than 11 LPA.

Yes. You will get the data science certificate on course completion.
Not to worry. In case you miss a live class, you will get the recording of the class, which you can watch according to your time. For any doubts, you can ask the mentor in the next class or the doubt-clearing session.
Yes. On completion of the course, we will prepare you for the interview. Following preparation, we will align your interviews with several top companies in the country and help you get placed.