Master of Science inData Science18 Months | Online

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upGrad is an online education platform to help individuals develop their professional potential in the most engaging learning environment. Online educationis a fundamental disruption that will have a far-reaching impact. At upGrad, we areworking towards transforming this online education wave into a tsunami! We are taking a full-stack approach of leveraging content, technology, marketing and services to o�er quality education at scale in partnership with corporates & academics to o�er a rigorous & industry relevant program. Based on our market research and conversation with the industry, we have identified Data Science as one of the sectors with critical supply demand imbalance. Our vision is to design and deliver a quality online Master of Science in Data Science to drive the growth of the sector and make India a global hub for data science. If you are reading this, you may wish to accelerate your career in Data Science. With upGrad, we promise to equip you with the perfect mix of business acumen and technical capabilities to help you achieve exactly the same.

Ronnie ScrewvalaCo-founder & ChairmanupGrad

INSIGHTS FROMLJMU FACULTY

GLOBAL ACCESSTO JOBSGet 360 degree career support and access to opportunities globally

GET ALUMNI STATUS +LIBRARY ACCESSEarn Alumni status of IIIT Bangalore and LJMU, with digital library access from LJMU

Get mentored by a thesis supervisor for the dissertation project.

ONE-ON-ONEMENTORSHIP

INTEGRATED MASTER’SFROM IIIT-B AND LJMUDo a PG Diploma from IIIT Bangalore & Master of Science from LJMU

WHY DATA SCIENCEWITH UPGRAD AND LJMU

DR ATIF WARAICHFaculty - Computer ScienceLJMU

PROF PAULO LISBOAHOD - Applied MathematicsLJMU

DR GABRIELA CZANNERFaculty - Engineering and TechLJMU

PROF DHIYA AL-JUMEILYAssociate DeanLJMU

CONCEPTS FROMINDUSTRY EXPERTS

ANSHUMAN GUPTADirector - Data SciencePitney Bowes

HINDOL BASUPartnerTata IQ

TEJAS SANGHVIVice PresidentFractal Analytics

SAI ALLURIPRO Analytics &Strategy ManagerUber

S. ANANDCEOGramener

UJJYAINI MITRAHead of AnalyticsViacom 18

KALPANA SUBBARAMAPPAEx-Assis. VP, Decision SciencesGENPACT

SAMEER DHANRAJANICSOFractal Analytics

INSIGHTS FROMIIIT-B FACULTY

G SRINIVASARAGHAVANProfessorIIIT Bangalore

CHANDRASHEKARRAMANATHANDean (Academics)IIIT Bangalore

TRICHA ANJALIAssociate ProfessorIIIT Bangalore

PROF. S. SADAGOPANDirectorIIIT Bangalore

DINESH BABU JAYAGOPIAssistant ProfessorIIIT Bangalore

MASTER OF SCIENCE INDATA SCIENCE.HOW DOES IT WORK?MASTER OF SCIENCE IN DATA SCIENCE

PG DIPLOMA IN DATA SCIENCE12 months 110 credits

RESEARCH METHODOLOGIES2 months 10 credits

MASTER’S DISSERTATION4 months 60 credits

After successful completion of the PG Diploma in Data Science upGrad and IIIT-BNext

PROGRAMCURRICULUMNote: This curriculum is subject to change based on inputs from IIIT-B, LJMU and industrymodules marked as (*) are optional.

(DURATION: 12 MONTHS)

DATA ANALYTICS IN EXCELTaught by one of the most renowned data scientists in the country - S. Anand, CEO, Gramener, this module takes you from a beginner level Excel user to an almost professional user

VISUALISATION USING TABLEAU Learn an important and widely used tool for Data Analysts - Tableau

INTRODUCTION TO DATA MANAGEMENT

INTRODUCTION TO PYTHON Sharpen your Data Analysis skills with Python, which is the choice of language for simplicity, readability and quick deployment

PROGRAMMING IN PYTHON Learn how to solve problems using programming

PYTHON FOR DATA SCIENCE Learn to clean and manipulate the data using Python's powerful data analysis libraries - NumPy and Pandas

DATA VISUALISATION IN PYTHON Make your data alive with visuals using Python and tools like Tableau

ANALYTICS PROBLEM SOLVING This module covers the concepts of the CRISP - DM framework for business problem-solving

EXPLORATORY DATA ANALYSIS Derive initial insights from the data using Excel. Learn from the best - S. Anand, CEO of Gramener

CASE STUDY - UBER SUPPLY DEMAND GAP Uber needs your help! Apply the statistical concepts to solve Uber's problem and present your results using engaging visuals

GDP ASSIGNMENT Be a part in the success story of India by helping the CMs focus on areas which will fostereconomic development for their respective states.

BUSINESS PROBLEM SOLVING USING DATA ANALYSIS

110 CREDITS

INFERENTIAL STATISTICS Build a solid statistical foundation. This will help you understand data and the results of your analysis

HYPOTHESIS TESTING Understand how to formulate and test hypotheses to solve a business problem

STATISTICS ASSIGNMENTAn assignment based on the concepts learnt in Inferential Statistics and Hypothesis Testing

CREDIT EDA CASE STUDYUse EDA to analyse patterns in the data that will help you identify individuals capable ofrepaying the loan

LOGISTIC REGRESSIONUse logistic regression to solve a Case Study to predict employee attrition in a firm and help them plan their manpower

CLUSTERINGLearn how to create segments based on similarities using k-means and hierarchical clustering. Use this to create customer segments that can be targeted using di�erent marketing stategies

PRINCIPLE COMPONENT ANALYSIS (PCA)Learn how to reduce the dimensions of data to make it useful for analysis. Use it to understand how movie recommendations work

CLUSTERING & PCA ASSIGNMENT Use the concepts that you learnt in Clustering and Principal Component Analysis to clustera group of countries

LEAD SCORING CASE STUDYHelp the sales team of your company identify which leads are worth pursuing

LINEAR REGRESSIONLearn to implement linear regression. Help a digital media company understand why their viewership is falling and propose recommendations to increase viewership

LINEAR REGRESSION ASSIGNMENTBuild a model to understand how factors like car prices vary on and help a Chinese company enter the US car market

MACHINE LEARNING I

MODEL SELECTION Learn how to benchmark and select the best algorithm for a given dataset and problemstatement

MACHINE LEARNING II

ADVANCED REGRESSION This module takes a more advanced look at linear regression models

ADVANCED REGRESSION ASSIGNMENT Build a regularized regression model to understand the most important variables to predict the house prices in Australia

TREE MODELS Tree models represent the way we make decisions. Learn how decisions are made in thispowerful classification algorithm

BUSINESS PROBLEM SOLVINGLearn how to approach open ended real world problems using data as a lever to drawactionable insights

TIME SERIES Learn how to make predictions using time dependent data. Use it to forecast energyconsumption, stock prices and sales

CASE STUDY - TELECOM CHURN Help a telecom giant predict if a customer will churn or not. Apply multiple algorithms simultane-ously to identify the one that works the best

DATA ANALYSIS USING SQLLearn basic and advance concepts of SQL and add another language to your programmingtool kit!

ADVANCED SQL Learn the advanced concepts of SQL and gain mastery over this programming language

SQL ASSIGNMENT Apply the basics of investing and your knowledge of Data Science to determine when to buy and sell a stock

HADOOPHadoop is a distributed computing framework. Learn how to analyze data with millions of rows quickly using distribute frameworks

HIVE AND SQOOP Apache Hive is the query language for Big Data applications. Learn it from the inventor of the language himself - Joydeep Sen Sarma

HIVE - CASE STUDY Use Hive to analyse the taxi trips data collected by the New York Taxi and Limousine Commission

BIG DATA AND SQL

MARKET MIX MODELLING Learn how to optimise your marketing spends in order to maximise the ROI

RECOMMENDATION SYSTEMSLearn about the algorithms that power the recommendation engines of the e-commerce sites

ASSIGNMENT - RECOMMENDATION SYSTEMS*Build a recommendation engine based on beer preferences of users

PRICE OPTIMIZATIONLearn how prices are dynamically optimised on an e-commerce platform

A\B TESTING*Understand the concept behind A/B tests and also learn how to execute an A/B test in Optimizely

COURSE PROJECT Model the impact of di�erent marketing levers on the sales figure of ElecKart

E-COMMERCE

DOMAIN ELECTIVES

ACQUISITION ANALYTICS Understand the component of acquisition strategies and practice hands-on exercise of data analytics for acquiring the potential customers

ASSIGNMENT - ACQUISITION ANALYTICS*Build a response model based on the clients, campaign and economic information provided by the Portuguese Bank

ENGAGEMENT ANALYTICSNow that you have learnt how to acquire customers, learn how to engage them and prevent their attrition

RISK ANALYTICSLearn about the risk associated with customers who default on their loan or credit, and the analytics related to it

COURSE PROJECT Help CredX identify the ideal applicants to provide credit cards by building an application scorecard

BFSI

PAYER ANALYTICSIn this module, you will explore the di�erent analytics opportunities that exist in the healthcare payer space

HEALTHCARE

BASICS OF TEXT PROCESSINGGet started with the Natural Language Toolkit, learn the basics of text processing in Python

LEXICAL PROCESSINGLearn to extract features from unstructured text and build machine learning models on textdata

SYNTAX AND SEMANTICSConduct sentiment analysis, learn to parse English sentences and extract meaning from them

SYNTACTIC PROCESSING ASSIGNMENT*POS tagging is a crucial part of Syntactic Analysis. Build a POS tagger using a CRF classifier and by modifying Viterbi

OTHER PROBLEMS IN TEXT ANALYTICSExplore the applications of text analytics in new areas and various business domains

BUILDING CHATBOTS WITH RASA Learn the fundamentals of building chatbots using open source chatbot buildingframework -Rasa

NATURAL LANGUAGE PROCESSING

ASSIGNMENT - PAYER ANALYTICS*Stratify patients according to the risk of cost they pose to the healthcare payer

PROVIDER ANALYTICSIn this module, you will explore the di�erent analytics opportunities that exist in the healthcare provider space

ANALYTICS IN THE PHARMACEUTICAL INDUSTRIESLearn how pharmaceutical companies harness the power of data analytics

COURSE PROJECT Decipher the CMS hospital star rating system using supervised and unsupervised models

INFORMATION FLOW IN A NEURAL NETWORKUnderstand the components and structure of artificial neural networks

TRAINING A NEURAL NETWORKLearn the cutting edge techniques used to train highly complex neural networks

NEURAL NETWORKS - ASSIGNMENT*Implementing multiclass classification on MNIST dataset using raw NN model in Numpy

CONVOLUTIONAL NEURAL NETWORKS Use CNNs to solve complex image classification problems

DEEP LEARNING AND NEURAL NETWORKS

WHAT IS RESEARCH?Familiarise with di�erent aspects of research- Intro to research- Importance of research- Criticism in research and its importance- Peer reviews in research and its importance

TYPES OF RESEARCHDevelop an understanding of various research design and techniques- Descriptive vs Analytical- Applied vs Fundamental- Quantitative vs Qualitative- Bayesian vs Frequentist Approach

RESEARCH PROCESSLearn about the di�erent steps in the research process and how to evaluate a literature- Research question- Hypothesis and aims- Formulating a Problem- Literature review

RESEARCH METHODOLOGY(DURATION: 2 MONTHS) 10 CREDITS

RECURRENT NEURAL NETWORKS Study LSTMs and RNNs applications in text analytics

CREATING AND DEPLOYING NETWORKS USING TENSORFLOW AND KERASBuild and deploy your own deep neural networks on a website, learn to use tensorflowAPI and Keras

NEURAL NETWORKS PROJECT - GESTURE RECOGNITIONIn this module, you'll experiment and create a model that identifies the gestures withconsiderable accuracy

BOOSTINGLearn about another type of ensemble modelling which is boosting

MINI CAPSTONEApply the concepts learnt in the program to solve a real industry use case

MINI CAPSTONE

RESEARCH PROJECT MANAGEMENTLearn how to plan the project timelines and arrange for data & software- Understand the di�erent steps involved in a project cycle- Project Requirements on Data- Identifying the milestones in a research project- Learn how to track the progress using Gantt Chart

REPORT WRITING AND PRESENTATIONMaster good scientific writing and proper presentation skills- Art of writing papers- Parts of a paper- Tools to write papers- Publishing papers: Journals + Seminars

SCIENTIFIC ETHICSDevelop an understanding of the ethical dimension in research- Citation Methods and Rules- Honor Code- Research Claims- IPh

MASTER’S THESISArticulate, research and present your project.- Monthly Checkpoints- Submission

An example of project outlines is here:- Investigate the risk factors for eye disease from complex longitudinal datasets- Investigate a diagnosis of eye diseases using imaging ophthalmic data- Multi-task learning for drug design and discovery- Using stacking for brain tumour discrimination- Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and Clustering methods- Longitudinal studies to investigate the complex link between corporate environment engagement, green disclosure, business model transformation and supply chain performance- Preventing credit card fraud through pattern recognition- Developing a recommender system for a Media giant- Using social media feed to place tweets regarding natural disasters on a map

MASTER’S DISSERTATION(DURATION: 4 MONTHS) 60 CREDITS

ROHIT SHARMA Program Directormastersds@upgrad.com

upGrad Education Private LimitedNishuvi, 75, Dr. Annie Besant RoadWorli, Mumbai - 400018

COMPANY INFORMATION

PROGRAMDETAILSPROGRAM STARTSPlease refer to the website for program start dates

DURATION18 months

PROGRAM FLOW12 months - PG Diploma in Data Science

2 months - Research Methodology

4 months - Master’s Dissertation

WEEKLY COMMITMENT12 hours per week

PROGRAM FEE₹4,85,000 (Incl. of all taxes)Flexible Payment Options Available

For further details, call us at +91 9372718764 or contact:

Kunal Naresh BabuChief Admissions Counsellorkunal.babu@upgrad.com