Courses

Master Degree in Data Science

Kick start your Data Science journey with Globally Recognized Master's Degree from International University of Applied Sciences, Germany. Receive dual Accreditation Executive PG Programme from IIIT Bangalore and Master Degree from IU, Germany on completion of course

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Intro Video
  • 40,000+
    Learners
  • 1st year online
    2nd year on-campus in Germany.
  • 24 Months
    Recommended 12-15 hrs/week
  • Aug 30, 2021
    Start Date
  • Save 20 Lakhs
    in degree cost
  • PSW Visa
    Eligible to apply for 18 months PSW Visa
  • Instructors
  • Syllabus
  • Specialisations
  • Projects
  • Benefits
  • Admission Process
  • Fees
  • Refer & Earn

About the Programme

Blended programme which combines 1st year online at IIIT Bangalore with 1 year on-campus at IU Germany, hence getting you best of both the worlds.

Customizable curriculum

6 Specializations at IIIT Bangalore and 10 electives at IU Germany

Student Support

Student support available 09 AM to 09 PM IST via email or use the Call Back option on the platform to get a response within 2 working hours.

End-to-end counseling and visa support

upGrad team will help you with application to IU Germany, securing confirmed offer letter & support with German visa application.

Programme Overview

Key Highlights

  • Complimentary Python Programming Bootcamp
  • Career Essential Soft Skills Programme
  • Eligible for 18 months Post Study Work visa application after graduation
  • 500+ Hours of Learning
  • No IELTS required to study in Germany for upGrad learners
  • 60+ Case Studies and Projects
  • IIIT Bangalore & IU, Germany Alumni Status
  • Fortnightly Group Mentorship with Industry Mentors
  • One-on-One with Industry Mentors
  • Timely Doubt Resolution
  • Support available all days 9 AM - 9 PM IST for queries
  • Global access to job opportunities
  • No Cost EMI Option
  • NASSCOM certificate
  • 94% of IU Graduate receive confirmed job offers within 6 months of graduation
  • Study in Europe's most resilient economy

10 electives to chose from at IU Germany
Software Engineering for Data Intensive Sciences, Management, Sales, Pricing and Brand Management, Consumer Behavior and Research, Corporate Finance, Innovate and Change, Cognitive Computing, Applied Autonomous Driving, Self Learning Systems & Industrial Automation and Internet of Things.

  • Top Skills You Will Learn
    Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, etc.
  • Job Opportunities
    Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst
  • Who Is This Programme For?
    Any specialization can be chosen regardless of background. The programme caters to: Engineers, Marketing & Sales Professionals, Freshers, Data Professionals, Domain Experts, Software & IT Professionals
  • Minimum Eligibility
    Bachelor’s Degree with minimum 50% or equivalent passing marks. No coding experience required.
Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos, cases, and projects.

Programming Languages and Tools Covered

Executive PG Programme from IIIT Bangalore

Complete all the courses successfully to obtain this prestigious recognition from IIIT Bangalore.

  • Connect with a global network of accomplished IIITB Alumni
  • Widely recognised Executive PG Programme in Data Science
  • Network with Data Science professionals across all industries
Executive PG Programme from IIIT Bangalore
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Instructors

Learn from leading Data Science faculty and industry leaders

Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions
  • Pre-Programme Preparatory Content (Year 1)
    • 4 Weeks
    Chandrashekar Ramanathan
    S Anand
  • Data Toolkit (Year 1)
    • 14 Weeks
    • 2 Assignments
    Mirza Rahim Baig
    Tricha Anjali
    Chandrashekar Ramanathan
    S Anand
    Srinivas Raghavan
  • Machine Learning (Year 1)
    • 9 Weeks
    • 3 Assignments
    Dinesh Babu Jayagopi
    Ujjyaini Mitra
    Hindol Basu
    Mirza Rahim Baig
  • Specialisation- Data Science Generalist (Year 1)
    • 22 Weeks
    • 5 Assignments
    Mirza Rahim Baig
    G. Srinivasaraghavan
    Deepak Kumar
  • Specialisation- Deep Learning (Year 1)
    • 25 Weeks
    • 4 Assignments
    Mirza Rahim Baig
    G. Srinivasaraghavan
    Anjali Rajvanshi
  • Specialisation- Natural Learning Processing (Year 1)
    • 25 Weeks
    • 4 Assignments
    Mirza Rahim Baig
    G. Srinivasaraghavan
    Anjali Rajvanshi
  • Specialisation- Business Intelligence/Data Analytics (Year 1)
    • 25 Weeks
    • 9 Assignments
    Prof. Chandrashekar Ramanathan
    Vishwa Mohan
    Sourabh Mukherjee
  • Specialisation- Business Analytics (Year 1)
    • 25 Weeks
    • 9 Assignments
    Mirza Rahim Baig
    G. Srinivasaraghavan
    Deepak Kumar
  • Specialisation- Data Engineering (Year 1)
    • 27 Weeks
    • 11 Assignments
    Vishwa Mohan
    Karthikeyan Sankaran
    Sourabh Mukherjee
  • Semester 3 (Year 2)
    • 6 Months
  • Semester 4 (Year 2)
    • 6 Months
  • 500+
    Hours of Content
  • 60+
    Case Studies and Projects
  • 20+
    Live Sessions
  • 25
    1:8 Coaching Sessions with Industry Experts
  • 14+
    Tools and Software

Specialisations

You can choose one of the 6 specialisation, based on your interest.

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

    Data Science Generalist
    • 22 Weeks
    • 5 Assignments
    Tree Models + Boosting (Optional)
    Model Selection & General ML Techniques
    ML Lab I: Classification
    Principal Component Analysis
    Advanced Regression - I
    Advanced Regression - II and ML Lab II: Regression
    Text Analytics & Processing + Text-Based Predictive Modelling
    Basic Visualisation using Tableau
    Data Storytelling
    Data Modelling
    Advanced SQL - Week II
    Algorithm Analysis + Recursion
    Searching and Sorting (Divide and Conquer included)
    Data Structures - Sets, Dictionaries, Stacks, Queues
    Python - OOPS
    Python Weeklong Lab
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Solve an end-to-end real-life industry problem from a wide variety of domain...
    Learn More
  • Topics Covered

    Deep Learning
    • 25 Weeks
    • 4 Assignment
    Prof. G. Srinivasaraghavan
    Ujjyaini Mitra
    Mirza Rahim Baig
    Ankit Jain
    S Anand
    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
    Introduction to Neural Networks
    Neural Networks Assignment
    Convolutional Neural Networks - Introduction and Industry Applications
    Recurrent Neural Networks
    Gesture Recognition
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Choose from a range of real-world industry woven projects on advanced topics...
    Learn More
  • Topics Covered

    Natural Language Processing
    • 25 Weeks
    • 4 Assignment
    Prof. G. Srinivasaraghavan
    Ujjyaini Mitra
    Mirza Rahim Baig
    Srinath Srinivasa
    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
    Text Processing
    Feature Extraction and Modelling
    Assignment- Natural Language Processing
    Intro to DL
    NLP Industrial Applications
    Chatbot Case Study
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Choose from a range of real-world industry woven projects on advanced topics...
    Learn More
  • Topics Covered

    Business Intelligence/Data Analytics
    • 25 Weeks
    • 7 Assignment
    Chandrashekar Ramanathan
    Mirza Rahim Baig
    S Anand
    Data Modelling
    SQL Best Practices
    SQL Assignment: IMDb Movies
    Advanced Excel
    NoSQL Databases and Best Practices
    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
    Plotly Case Study
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Choose from a range of real-world industry woven projects on advanced topics...
    Learn More
  • Topics Covered

    Business Analytics
    • 25 Weeks
    • 9 Assignment
    Mirza Rahim Baig
    Srinivas Raghavan
    S Anand
    Ujjyaini Mitra
    Tree Models
    Time Series Forecasting
    Retail-Giant Sales Forecasting Assignment
    Model Selection & General ML Techniques
    SQL Best Practices
    Advanced Excel
    Telecom Churn Case Study
    Structured Problem Solving using Frameworks
    Structured Problem Solving Assignment
    Operations Research
    Data Storytelling
    Operations Research Case Study
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Choose from a range of real-world industry woven projects on advanced topics...
    Learn More
  • Topics Covered

    Data Engineering
    • 27 Weeks
    • 11 Assignment
    Introduction to Big Data(Optional)
    Introduction to Cloud and AWS Setup
    Introduction to Hadoop and MapReduce Programming
    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
    Hive Assignment (optional)
    Amazon Redshift
    Introduction to Apache Spark
    NYC Parking Assignment (Optional)
    Project: ETL Data Pipline
    Optimising Spark for Large Scale Data Processing
    Apache Flink(Optional)
    Real-Time Data Streaming with Apache Kafka
    Real-Time Data Processing using Spark Streaming
    Stock data Analysis Assignment (Optional)
    Building Automated Data Pipelines with Airflow
    Analytics using PySpark
    Classification Assignment (Optional)
    Project: Real Time data processing
    Capstone Project
    Tools Covered

    Project

    Capstone Project
    Choose from a range of real-world industry woven projects on advanced topics...
    Learn More

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

  • Engage in collaborative projects with student-mentor interaction
  • Benefit by learning in-person with expert mentors
  • Personalised subjective feedback on your submissions to facilitate improvement
  • Uber Supply-Demand Gap
    Use analytics to identify why Uber sometimes faces a supply-demand challenge...
    Learn More
  • Creditworthiness of Customers
    Learn how Predictive Analytics can be used to decide the creditworthiness of...
    Learn More
  • Speech Recognition
    Learn how Google speech-to-text works and make your own model.
    Learn More
  • Image Captioning
    Build a model which will caption a given image and describe it in sentences ...
    Learn More
  • Social Media Listening
    What if you could predict the popularity of a brand like upGrad in India?
    Learn More
  • Telecom Churn
    Telecom companies often face the problem of churning customers due to the co...
    Learn More
  • IMDb Movie Analysis
    Analyze movie data from the past hundred years and find out various insights...
    Learn More
  • Lead Scoring
    Learn how the knowledge of basic machine learning algorithms can be used to ...
    Learn More
  • Fraud Detection
    Decline fraud transactions on a credit card in real-time to minimize the los...
    Learn More
  • Retail-Giant Sales Forecasting
    Help a retail-giant to have a clear idea of how their sales will look like i...
    Learn More
  • Interactive Marketing Campaign Analysis
    Research and analyse data from a marketing campaign to gain valuable insight...
    Learn More
The upGrad Advantage
Strong hand-holding with dedicated support to help you master Data Science
  • Learning Support
    Industry Mentors
    • Receive unparalleled guidance from industry mentors, teaching assistants and graders
    • Receive one-on-one feedback on submissions and personalised feedbacks on improvement
    Student Support
    • Available all days 9 AM - 9 PM IST.
    • For urgent queries, use the Call Back option on the platform.
  • Q&A Forum
    • Timely doubt resolution by Industry experts and peers
    • 100% Expert-verified responses to ensure quality learning
    Expert Feedback
    • Personalised expert feedback on assignments and projects
    • Regular live sessions by experts to clarify concept related doubts
  • upGrad BaseCamp
    • Fun-packed, informative and career building workshops Sessions by industry professionals and professors
    • Group activities with your peers and alumni
    Industry Networking
    • Live sessions by experts on various industry topics
    • One-on-one discussion and feedback sessions with industry mentors

upGrad Alumni

Admission Process

There are 3 simple steps in the Admission Process which is detailed below:

Programme Fee - 1st Year
INR 3,35,000 (incl. taxes)
No Cost EMI options available
    Programme Fee - 2nd Year
    Indicative Tuition Fees at IU - EUR 8,428*
    Living cost per annum in Germany - EUR 10,332
      Note-
      • *Indicative Tuition Fees includes enrollment and graduation fees.

      Empowering learners of tomorrow

      Over 10,000 students go to Germany to learn every year. Connect with our counselors to know how you can be next!

      How You Benefit From This Program
      • Master degree from a reputed German University
      • Opportunity to apply for 18 months Post-Study-Work visa after graduation
      • Save 20 Lakhs for an equivalent degree from Germany
      • Free German language course
      • Study in Europe's economic power house

      Student Reviews

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      *Referral offer is valid only on the Master Degree in Data Science.

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      Frequently Asked Questions

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