Masters in Data Science
6 months of live, advanced learning across 14+ modules with a globally recognised certification. Learn regression, machine learning, deep learning and NLP, work on 10+ capstone projects with mentors from industry, and get interview preparation for your first data science role.
- 6 months live
- 14+ modules
- 10+ capstone projects
- Hybrid classes
- Minimum 5 interviews guaranteed (T&C apply)
- What it is
- A live data science course with 10+ capstone projects and a globally recognised certification
- Who it’s for
- Full-time professionals and students who want to work in data science
- How long
- 6 months of live learning, hybrid classes
- After it, you can
- Build and evaluate machine learning, deep learning and NLP models
- Why SkillCircle
- 10 years · Best Vocational Institute 2026 (Entrepreneur India) · Vishleshan i-Hub (IIT Patna) · NASSCOM FutureSkills
Knowing the theory isn’t enough to get hired
Data science roles need people who can turn data into working models and explain the results. That takes hands-on practice on real problems.
Where most beginners get stuck
- Scattered tutorials on statistics, Python and machine learning with no clear path
- No real projects to show
- No feedback from people who work in the field
- Unsure how to prepare for interviews
How this course fixes it
- A structured path of 14+ modules with live classes
- 10+ capstone projects with dedicated mentors from industry
- A globally recognised certification
- Interview preparation, including sessions with an HR professional from naukri.com
Learn, practise, build, get interviews
Every stage builds on the one before, so you finish with skills, projects and interview-ready confidence.
- 1
Learn
6 months of live classes across 14+ modules.
- 2
Practise
Regression, machine learning, deep learning and NLP on real data.
- 3
Capstones
10+ projects with dedicated mentors from industry.
- 4
Portfolio
Models and analyses you can show recruiters.
- 5
Interviews
Interview preparation and a minimum of 5 interviews via #JobCircle (T&C apply).
What you’ll be able to do after the course
What you’ll be able to do
- Collect, clean and preprocess data, including missing values
- Build and interpret linear and logistic regression models
- Train and evaluate machine learning models such as decision trees, random forests, XGBoost, SVM and K-NN
- Apply clustering with K-means
- Work with deep learning models in TensorFlow and Keras
- Process text and build sentiment analysis models with NLP
What you’ll build
- Capstone projects10+ projects such as fraud analytics, supply chain analytics and email spam detection
- ModelsRegression, classification and clustering models
- NLP workText classification and sentiment analysis
- PortfolioYour projects and certificate in one place
Upcoming Batches
| Batch | Start date | Timing | Mode / Centre | Action |
|---|
Don't see a timing that works for you? No worries. Talk to our admissions team and we'll try to find you a slot.
Get help with timingWant the full module list, schedule and batch options?
Get Complete Course DetailsSupport to land your first role
Resume & LinkedIn
Shape your resume and profile around your capstone projects.
Mock interviews
Interview preparation sessions by an HR professional working at naukri.com.
Interviews via #JobCircle
Openings from our hiring partners on #JobCircle, our in-house job portal.
Placement promise: Minimum 5 interviews guaranteed, or 1 year free learning (T&C apply). Getting hired depends on your skills, effort, interviews and the job market.
Data science course curriculum: 14+ modules
Pick a track to see its modules, then open a module for the topics covered.
01Introduction To Data Science
- Data science vs. data analytics
- The data science workflow
- Tools and environments (Python, Jupyter)
- Concepts of Regression and Classification
02Data Acquisition And Cleaning
- Data sources and collection
- Data cleaning and preprocessing
- Handling missing data
03Predictive Analytics
- Regression
- What is regression analysis?
- Types of regression (linear, multiple, polynomial, etc.)
- Use cases and applications of regression in data science
04Linear Regression
- Simple linear regression
- Multiple linear regression
- Assumptions of linear regression
- Model interpretation and coefficients
- Model evaluation metrics (R-squared, MSE, MAE)
05Logistic Regression
- Introduction to logistic regression
- Logistic regression vs. linear regression
- Binary and multinomial logistic regression
- Odds ratio and log-odds interpretation
- Model evaluation for classification
06Machine Learning
- Understanding machine learning
- Types of machine learning (supervised, unsupervised, reinforcement)
- Machine learning workflow
07Data Preprocessing For Machine Learning
- Data cleaning and transformation
- Data scaling and normalization
- Handling missing data
08Model Evaluation And Validation
- Cross-validation and train-test split
- Evaluation metrics (accuracy, precision, recall, F1-score)
- Bias-variance trade-off
09Supervised Learning Algorithms
- Decision Tree
- What is a decision tree? and How decision trees work.
- Decision Tree Splitting Criteria
- Dealing with categorical features.
- Advantages and Disadvantages of Using a Decision tree Algorithm.
10Random Forest
- What is a Random Forest?
- The concept of bagging and boosting
- Random Forest Features and Hyperparameters
- Handling Imbalanced Data
- Feature Importance and Interpretability
11XGBoost (Extreme Gradient Boosting)
- Introduction to Gradient Boosting
- Understanding boosting and the concept of weak learners.
- How XGBoost improves upon traditional gradient boosting.
- XGBoost Hyperparameters.
12Naive Bayes Algorithm
- Introduction to Naive Bayes
- Types of Naive Bayes
- Probability Distributions
- Training and Classification
13SVM (Support Vector Machine)
- Introduction to Support Vector Machines
- Support Vector Classification (SVC)
- Support Vector Machines for Regression (SVR)
- Kernel Trick and Non-Linear SVM
14Unsupervised Learning Algorithms
- Introduction to Clustering
- K-Means Algorithm
- Objective Function and Optimization
- Challenges and Limitations
15K-Nearest Neighbors (K-NN)
- Introduction to K-NN
- Distance Metrics
- Hyperparameter K
- Decision Boundary and Majority Voting
16Deep Learning
- Introduction to Deep Learning and TensorFlow.
- Convolutional Neural Networks (CNNs) with TensorFlow.
- Recurrent Neural Networks (RNNs) and Sequence Models.
- Advanced Deep Learning with TensorFlow and Keras.
17Introduction to NLP
- Tokenization
- Stop words removal
- Stemming and Lemmatization
- Text cleaning and normalization
18Text Preprocessing
- Tokenization
- Stop words removal
- Stemming and Lemmatization
- Text cleaning and normalization
19Text Representation
- Bag of Words (BoW)
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word embeddings (Word2Vec, GloVe)
- Document-term matrices
20Sentiment Analysis And Text Classification
- Sentiment analysis techniques
- Binary and multi-class classification
- Building a sentiment analysis model
Capstone projects
10+ capstone projects with dedicated mentors from industry. Examples:
- Web & Social Media Analytics
- Fraud Analytics
- Finance and Risk Analytics
- Color Detection
- Marketing and Retail Analytics
- Gender and Age Detection
- Supply Chain Analytics
- Email Spam Detection
Data science tools you’ll learn
Get hands-on with the tools and libraries used in data science.
Career paths after the data science course
Data science opens the door to roles like these. Some, such as data architect or AI engineer, usually need further experience.
- Data Scientist
- Data Analyst
- Business Analyst
- Data Engineer
- ML Engineer
- AI Engineer
- Data Architect
- Data Administrator
Trusted by learners, recruiters and the media
Best Vocational Institute 2026
Entrepreneur India
Vishleshan i-Hub (IIT Patna), NASSCOM FutureSkills
Plus IBM and Meta certifications on selected programs.
10 years, 15 centres
A decade of training learners, online and in classrooms across India.
Our hiring partners
SkillCircle in the news
Learn from Industry Practitioners
Our mentor network of 100+ industry experts includes founders, business leaders and specialists.
Amit Gupta
VP, Amar Ujala
Pranav Jha
Founder, AP Web World
Nishkarsh Sharma
Dropshipping Expert
Parul Mehta Bhargava
Founder, vCommission
Girish Singh
Head of Marketing, Uber
Amit Chawla
Business Clarity Coach
Not sure if this course is right for you?
Talk to a career counsellor about your goals, the next batch and the admission process.