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Master of Science in Data Science

Master the skills of Machine Learning & AI with this advanced Data Science program

  • Choose from five unique specializations as per your background and career aspirations
  • Master skills in predictive analysis, Data Analytics, Python, Machine Learning, and more
  • Dual accreditation from Liverpool John Moores University and IIIT Bangalore
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Advance your Data Science career

Create robust predictive models and build confidence and credibility to tackle complex machine learning problems on the job. Master the skills of Machine Learning and AI with this advanced Data Science program, aligned to competency standards developed by NASSCOM in collaboration with industry and approved by the Indian government.

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Highlights

  • 18 months of industry-relevant learning

  • Alumni status from LJMU, IIIT Bangalore

  • Choose specialization from 5 functional tracks

  • Complimentary Python Programming Bootcamp

  • Aligned to NASSCOM standards

  • Recognized by World Education Services (WES)

  • Fortnightly Group Mentorship with Industry Mentors

  • 500+ Hours of Learning

  • 60+ Case Studies and Projects

  • Global Access to Job Opportunities

The upGrad Advantage

Learn by Doing

Work on real-world projects, assignments. Get exposure to practical data problems across industries.

Real-World Focus

Personalized feedback from experienced industry professionals, teaching support to clear your doubts.

Job Opportunities

Gain exclusive access to upGrad's Job Opportunities portal with 100+ openings at any given time.

Career Assistance

Career mentorship sessions, high-performance coaching, interview prep, career bootcamps, more!

Continued Learning Support

Live sessions, focused group workshops with leading industry experts covering curriculum + advanced topics.

Industry Readiness Assessments

Industry-oriented tests, prepared and validated by domain experts for continuous evaluation, improvement.

prerequisites for Master of Science in Data Science [LJMU]

Prerequisites

A Bachelor’s degree with a minimum of 50% or equivalent marks is required to apply for this course.

Who Should Attend the Course

Engineers

Marketing & Sales Professionals

Freshers

Data Professionals

Domain Experts

Software & IT Professionals

What You Will Learn

Predictive Analysis

Master Python skills to make future analysis by leveraging different forms of data

Data Visualization

Learn how to graphically represent data and help in making data-driven decisions

Machine Learning

Get introduced to the key concepts and terminologies of Machine Learning

Advanced Statistics

Analysis of variance, linear regression, model building, and reduction techniques

Master Deep Learning

Understand artificial neural networks and acquire in-demand Deep Learning skills

Understand Data Analytics

Learn how to make the most of data. Use advanced analytics tools to evaluate data

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Curriculum

1.
  • Data Analysis in Excel  
  • Analytics Problem Solving  
2.
  • Introduction to Python  
  • Programming in Python  
  • Python for Data Science  
  • Data Visualization in Python  
  • Exploratory Data Analysis  
  • Credit EDA Case Study  
  • Inferential Statistics  
  • Hypothesis Testing  
  • Data Analysis using SQL  
  • Advanced SQL & Best Practices  
  • SQL Assignment: RSVP Movies  
3.
  • Linear Regression
  • Linear Regression Assignment
  • Logistic Regression 
  • Unsupervised Learning: Clustering  
  • Business Problem Solving  
  • Clustering Assignment (Optional)  
  • Case Study: Lead Scoring  
4.
  • Tree Models  
  • Model Selection & General ML Techniques  
  • Bagging and Boosting  
  • Advanced Regression  
  • Advanced Regression Assignment  
  • Principal Component Analysis  
  • Time Series Analysis  
  • Telecom Churn Case Study  
  • Introduction to Neural Networks  
  • Convolutional Neural Networks - Introduction and Industry Applications  
  • CNN Assignment  
  • Recurrent Neural Networks  
  • Gesture Recognition  
  • Capstone Project  
5.
  • Tree Models  
  • Model Selection & General ML Techniques  
  • Principal Component Analysis  
  • Advanced Regression  
  • Advanced Regression Assignment  
  • Bagging and Boosting  
  • Time Series Analysis  
  • Telecom Churn Case Study  
  • Lexical Processing  
  • Syntactic Processing  
  • Syntactic Processing -Assignment  
  • Neural Nets for NLP  
  • Semantic Processing  
  • Case Study: Automatic Ticket Classification  
  • Capstone Project 
6.
  • Tree Models  
  • Time Series Forecasting  
  • Retail-Giant Sales Forecasting Assignment  
  • Model Selection & General ML Techniques  
  • Advanced Excel  
  • Visualisation using Tableau  
  • Telecom Churn Case Study  
  • Structured Problem-Solving using Frameworks  
  • Structured Problem-Solving Assignment  
  • Operations Research  
  • Data Storytelling  
  • Business Case Study  
  • Capstone Project  
7.
  • Data Modelling  
  • Advanced SQL and Best Practices  
  • SQL Weeklong Lab  
  • Advanced Excel  
  • NoSQL Databases and MongoDB  
  • Introduction to Big Data and Cloud  
  • Hive and Querying  
  • Hive Case Study  
  • Visualisation using Tableau  
  • Sports Analytics - IPL Visualisation Assignment  
  • Visualisation using PowerBI  
  • Visualisation using Plotly  
  • Data Storytelling  
  • Business Case Study  
  • Capstone Project  
8.
  • Introduction to Big Data (Optional)  
  • Introduction to Cloud and AWS Setup  
  • Introduction to Hadoop and MapReduce Programming  
  • Assignment (optional)  
  • Data Management and Relational Database Modelling  
  • NoSQL Databases and Apache HBase and NoSQL Databases and MongoDB(Optional)  
  • Data Warehousing (Optional)  
  • Data Ingestion with Apache Sqoop and Apache Flume  
  • Hive & Querying  
  • Assignment (optional)  
  • Amazon Redshift  
  • Introduction to Apache Spark  
  • Project: ETL Data Pipeline  
  • AWS Cloud Infrastructure (Optional)  
  • Optimising Spark for Large Scale Data Processing  
  • Apache Flink (Optional)  
  • Real-Time Data Streaming with Apache Kafka  
  • Real-Time Data Processing using Spark Streaming  
  • Assignment (Optional)  
  • Building Automated Data Pipelines with Airflow  
  • Analytics using PySpark  
  • Project: Real Time data processing  
  • Capstone Project
9.
  • Tree Models  
  • Boosting (optional)  
  • Model Selection & General ML Techniques  
  • Telecom Churn Case Study (optional)  
  • Principal Component Analysis  
  • ML Lab 1: Classification  
  • Advanced Regression + ML Lab 2: Regression  
  • Text Analytics & Processing + Text-Based Predictive Modelling  
  • Visualisation using Tableau  
  • Data Storytelling  
  • Business Case Study  
  • Data Modelling  
  • Advanced SQL - Week II  
  • SQL Weeklong Lab  
  • Data Structures - Sets, Dictionaries, Stacks, Queues  
  • Algorithm Analysis + Recursion  
  • Searching and Sorting  
  • Python Weeklong Lab-I  
  • Python Weeklong Lab-II  
  • Capstone Project  
10.
  • Introduction to Research and Research Process  
  • Research Design  
  • Literature Reviewing  
  • Research Project Management  
  • Report Writing and Presentation Skills  
  • Scientific Ethics  
11.
  • Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and clustering methods  
  • Investigate a diagnosis of eye diseases using imaging ophthalmic data  
  • Structure medical images with information geometry  
  • Using social media feed to place tweets regarding natural disasters on a map  
  • Preventing credit card fraud through pattern recognition  
  • Developing a recommender system for a Media giant  
  • Risk modelling for Financial Activities and Investment Banking  

Master of Science in Data Science FAQs

Master of Science in Data Science Training

Expect to carry out several industry-relevant projects simulated as per the actual workplace, making you a skilled data science professional at par with leading industry standards.

By the end of this course, you’ll learn:

  • Predictive Learning
  • Data Visualization
  • Data Analytics
  • Python Programming
  • Machine Learning
  • Deep Learning
  • And a lot more...

Master of Science Data Science Certification

The certificate is issued by Liverpool John Moores University - ranks among the top 100 Young World Universities and top 50 in UK by student satisfaction.

To apply for this course, you need a Bachelor’s degree with a minimum of 50% or equivalent marks.

There are 6 specializations offered:

  • Deep Learning  
  • Business Intelligence/Data Analytics  
  • Data Engineering 
  • Data Science Generalist  
  • Business Analytics  
  • Natural Language Processing  

The admission process involves 3 basic steps:

Step 1: Take the Online Eligibility Test

Complete application and take the 17 minutes online admission test

Step 2: Get Shortlisted

Our admission committee will review your profile and test score. You will receive an offer letter once you are selected

Step 3: Block Your Seat

Pay the block amount to book your seat and start your Data Science journey with us.

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