MASTER IN
PROJECT MANAGEMENT
TABLE OF
CONTENTS
01.
About upGrad
02.
About Clark University
03.
Program Highlights
04.
Faculty and Industry Experts
05.
upGrad Learning Experience
06.
New Additions
07.
Industry Projects
08.
Learning Path
09.
MS in Project Management
10.
Meet the Class
11.
Hear from Our Learners
12.
Onshore Alumni Benefits
13.
Program Details and Admission Process
ABOUT
UPGRAD
upGrad has delivered over 20 million hours of learning, delivering programs by collaborating with
universities across the world including Duke CE, IIT Madras, IIIT Bangalore and Deakin Business
School among others.
Online education is a fundamental disruption that will have a far-reaching impact. upGrad was
founded taking this into consideration. upGrad is an online education platform to help individuals
develop their professional potential in the most engaging learning environment.
Since its inception, upGrad has delivered over 20 million hours of learning, delivering programs by
collaborating with universities across the world including LJMU, IIT Madras, IIIT Bangalore and
Deakin Business School among others. And it doesn’t end there.
upGrad, in collaboration with IIIT Bangalore, a renowned university and Clark University, US is
excited to offer a one-of-its-kind, academically rigorous and industrially relevant MS in Project
Management.
The faculty includes an average of 15+ years of experience. The faculty covers the conceptual
depths of topics such as Computer Science, data science, machine learning and artificial
intelligence. These will be complemented by industry-relevant case studies from major industry
verticals by industry leaders with 8+ years of experience from upGrad’s industry network.
Our aim is simple:
We strive to create
high-impact, on-campus
hands-on experiences that
prepare students for
meaningful and productive
careers.
Ronnie Screwvala
Co-founder & Executive Chairman
1
ABOUT
CLARK UNIVERSITY
Founded 1887
Clark School of Management,
Accredited by AACSB
International
More than 40,000+
Alumni
QS World University Rankings
2022, 600 - 650
More than 100
Student-to-faculty ratio: 10:1
Bachelor’s, Master’s &
certificate programmes
Home to a diverse community of students, Clark University was founded in 1887 as the first
all-graduate school in the US. The co-educational university offers bachelor’s, master’s, certificates,
and doctoral degree programs in various disciplines. The university’s mission is to prepare students
to solve complex problems of the rapidly changing society and contribute their share of knowledge
to help bring changes in society. With more than 3,000 students from 80+ countries of which 25%
are international students, and a 40,000+ strong alumni network, Clark University offers exceptional
support to its students by offering excellent academic facilities under the mentorship of experienced
faculties.
About us
2
ACHIEVEMENTS AT A GLANCE:
•
Clark University is accredited by the New England Commission of Higher Education, American
Psychological Association, and Association to Advance Collegiate School of Business (AACSB).
•
Over 200 full-time instructional faculty are employed at the university.
•
Clark University is ranked 601-650 by the QS World Rankings 2022.
•
As per the Times Higher Education University Ranking, Clark University is ranked 401-500
in 2021.
•
The US News & World Report National University Ranking gave the university 103rd position
in 2021.
•
Over 130 student clubs and organizations to take part in. Society includes singing, theatre troupes,
photography clubs, entrepreneurship club, pre-health society, and more.
•
The university has its student newspaper named The Scarlet and online radio, ROCU (Radio of
Clark University).
•
The university houses 6 Libraries and 7 Research Centers and Institutes.
•
The faculties conduct research across the globe and receive funding from the US Department of
Agriculture, NASA, National Institutes of Health, National Science Foundation, and more.
•
Clark University is Ranked 36 on Best Value Schools by US News Ranking (Focusing on the strong
ROI).
•
Ranked 21st among Small Universities in the World by Times Higher Education Rankings.
ELIGIBILITY CRITERIA
FOR TRANSFER TO CLARK UNIVERSITY
•
The applicant must complete the Advance Certificate Programme in Data Science from IIIT,
Bangalore with 3 or above CGPA.
•
IELTS: 6.5 overall, recommended sub-score minimum of 6, or TOEFL (ETS code 5969): 85 overall,
recommended sub-score minimum of 20, or Duolingo: 110, recommended sub-score minimum of
100, or PTE: 53 (No band less than 50)
3
PROGRAM
HIGHLIGHTS
Dual Accreditation and Alumni Status
Get certified by IIITB and Clark University, US.
High Employment Potential
Starting salaries of Project Management Professionals in US is $73,922 as per
indeed.com
Complete your course in 1 year
Only 8 subjects to be done which makes it easier to complete course in 1 year
unlikely.
Optional Practical Training (OPT)
STEM programs leading upto 3 years of OPT (post-study work visa).
4
FACULTY AND
INDUSTRY EXPERTS
Hindol Basu
Chandrashekar Ramanathan
S. Anand
CEO
Actify Data Labs
Dean | Academics
International Institute of Information
Technology Bangalore
CEO
Gramener
An alumnus of IIT and IIM with over 13
years of experience in Analytics with
industry leaders such as the CitiGroup,
Tata Industries etc.
Prof. Chandrashekar has a PhD from
Mississippi State University and over 10
years of experience in several
multinational organisations.
Tricha Anjali
Dr. Debabrata Das
Ex-Associate Dean, IIITB
Director, IIITB
Prof. Anjali has a PhD from Georgia
Tech as well as an integrated M.Tech.
(EE) from IIT Bombay. Her research
interests are computers and wireless
technology.
Dr. Debabrata Das is currently Director
of IIITB. He has received his PhD
degree from IIT-Kharagpur. His main
areas of research interest are IoT and
Wireless Access Network's MAC, QoS,
Power saving.
A Gold medalist from IIM Bangalore, an
alumnus of IIT Madras and London
School of Business, Anand is among
the top 10 data scientists in India.
5
Prof. G. Srinivasaraghavan
Ankit Jain
Dinesh Babu Jayagopi
Professor
International Institute of Information
Technology Bangalore
Sr. Research Scientist
Uber Ai Labs
Assistant Professor
International Institute of Information
Technology Bangalore
Prof. Srinivasaraghavan has a PhD in
Computer Science from IIT Kanpur and
18 years of experience with Infosys
Technologies as well as several other
companies.
An alumnus of IIT Bombay, UCB,
and Harvard Business School with over
9 years of experience.
Kalpana Subbaramappa
Mirza Rahim Baig
ex-AVP | Decision Science
Genpact
Lead | Business Analytics
Flipkart
Kalpana is the ex-AVP of Decision
Sciences at Genpact with over 20
years of experience.
Advanced analytics professional with
8+ years of experience as a consultant
in the e-commerce and healthcare
domains.
Prof. Dinesh has a PhD from Ecole
Polytechnic Federate Switzerland,
M.Sc. from IISc Bangalore in System
Science and Signal Processing, and
B.Tech.
6
UPGRAD
LEARNING EXPERIENCE
Coaching
Dedicated Student Support Team
Weekly real-time doubt clearing sessions
Live Discussion forum for peer-to-peer
doubt resolution monitored by technical
experts
Format
Online format with weekly live sessions from
industry experts to help with topic walk-throughs,
doubt resolution and personalised project
feedback. Offline sessions such as Basecamps and
Hackathons.
Peer-to-peer networking opportunities with
an alumni pool of 10,000+
Lab walk-throughs of 15+ industry-driven
case studies
Access to the program for up to 3 years
Hands-On Projects and Hackathons
7 + case studies to choose from and a hackathon
every quarter to apply learnings.
Mentorship
20+ live interactive sessions with
industry experts, Fortnightly
personalized group (1:12) coaching
sessions, 1:1 interaction with Industry
Mentors.
7
NEW
ADDITIONS
Career Essential Soft-skills Program
Excel in your personal & professional life with upGrad’s Soft Skills Program.
Study Three fundamental Skills - Interview & Job Search, Corporate & Business Communication
and Problem Solving.
Get access to 40+ learner hours of soft skills content delivered by the best faculty & Industry
experts
8
INDUSTRY
PROJECTS
IMDb Movie Analysis
Uber Supply-Demand Gap
Lead Scoring
Creditworthiness of
Customers
Speech Recognition
Image Captioning
Fraud Detection
Gesture Recognition
SHOP
Social Media Listening
Telecom Churn
Interactive Market
Campaign Analysis
Retail Giant Sales
Forecasting
And many more!
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LEARNING
PATH
Preparatory Course
Tools: Python,
Excel
IELTS Preparation
& Exam
Data Toolkit
13 weeks
Tools: Python,
Excel, mySQL
Machine Learning
10 weeks
Tools: Python, Excel
Course 3
6 weeks
Tools: Python, Excel
Advanced
Certification in Data
Science
Journey in
Elective
Master of Science (M.Sc) in
Project Management at Clark University
10
MS IN PROJECT MANAGEMENT
1. Advanced Certificate in Data Science from IIIT, Bangalore (8 months)
PROGRAM CURRICULUM
Pre-program Preparatory Content
Data Analysis in Excel
1. Introduction to Excel
Taught by one of the most renowned
2. Data Analysis in Excel - I: Functions,
data scientists in the country (S.Anand,
Formulae, and Charts
3. Data Analysis in Excel - II: Pivots and
Lookups
CEO, Gramener), this module takes
you from a beginner level Excel user
to an almost professional user.
Analytics Problem Solving
1. The CRISP-DM Framework - Business
and Data Understanding
2. CRISP-DM Framework - Data
This module covers concepts of the
CRISP-DM framework for business
problem-solving.
Preparation, Modelling, Evaluation and
Deployment
Course 1: Data Toolkit
Introduction to Python
1. Understanding the upGrad Coding
Console
2. Basics of Python
2 WEEKS
Build a foundation for the most indemand programming language of the
21st century.
3. Data Structures in Python
4. Control Structure and Functions in
Python
5. OOP in Python
*The Curriculum is subject to change as per the inputs from university or industry experts
11
Programming in Python
2 WEEKS
1. Logic and Syntax Building
Learn how to approach and solve
2. Data Structures: Lists, Strings,
logical problems using programming.
Dictionaries, and Stacks
3. Time Complexity
4. Searching and Sorting
5. Two Pointers
6. Recursion
1 WEEK
Python for Data Science
1. Introduction to NumPy
Humans are visual learners and hence
2. Introduction to Matplotlib
no task related to data is complete
3. Introduction to Pandas
without visualisation. Learn to plot and
4. Getting and Cleaning Data
interpret various graphs in Python and
observe how they make data analysis
and drawing insights easier.
1 WEEK
Data Visualization in Python
1. Introduction to Data Visualization
Humans are visual learners and hence
2. Data Visualisation using Seaborn
no task related to data is complete
without visualisation. Learn to plot and
interpret various graphs in Python and
observe how they make data analysis
and drawing insights easier.
1 WEEK
Exploratory Data Analysis
1. Data Sourcing
Learn how to find and analyse the
2. Data Cleaning
patterns in the data to draw actionable
3. Univariate Analysis
insights.
4. Bivariate Analysis and Multivariate Analysis
*The Curriculum is subject to change as per the inputs from university or industry experts
12
Credit Eda Case Study
1 WEEK
1. Problem Statement
Solve a real industry problem
2. Evaluation Rubric
through the concepts learnt in
3. Final Submission
exploratory data analysis.
4. Solution
1 WEEK
Inferential Statistics
1. Basics of Probability
Build a strong statistical foundation
2. Discrete Probability Distributions
and learn how to ‘infer’ insights from
3. Continuous Probability Distributions
a huge population using a small
4. Central Limit Theorem
sample.
1 WEEK
Hypothesis Testing
1. Concepts of Hypothesis Testing - I: Null
Understand how to formulate and
and Alternate Hypothesis, Making a
validate hypotheses for a population
Decision, and Critical Value Method
to solve real-life business problems.
2. Concepts of Hypothesis Testing - II:
p-Value Method and Types of Errors
3. Industry Demonstration of Hypothesis
Testing: Two-Sample Mean and
Proprotion Test, A/B Testing
1 WEEK
Data Analysis Using Sql
1. Database Design
Data in companies is definitely not
2. Database Creation in MySQL Workbench
stored in excel sheets! Learn the
3. Querying in MySQL
fundamentals of database and
4. Joins and Set Operations
extract information from RDBMS
using the structured query language.
*The Curriculum is subject to change as per the inputs from university or industry experts
13
Advaced Sql & Best Practices
1 WEEK
1. Window Functions
Apply advanced SQL concepts like
2. Case Statements, Stored Routines and Cursors
window-ing and procedures to derive
3. Query Optimisation And Best Practices
insights from data and answer perti-
4. Problem-Solving Using
nent business ques-tions.
1 WEEK
Sql Assignment: Rsvp Movies
1.
Problem Statement
In this assignment, you will work on a
2. Evaluation Rubric
movies dataset using SQL to extract
3. Final Submission
exciting insights.
4. Solution
Course 2 - Machine Learning I
2 WEEKS
Linear Regression
1. Simple Linear Regression
Venture into the machine learning
2. Simple Linear Regression in Python
community by learning how one vari-
3. Multiple Linear Regression
able can be predict-ed using several
4. Mutliple Linear Regression in Python
other variables through a housing
5. Industry Relevance of Linear Regression
dataset where you will predict the
prices of houses based on various
factors.
1 WEEK
Linear Regression Assignment
1. Problem Statement
Build a model to understand the
2. Evaluation Rubric
factors on which the demand for bike
3. Final Submission
sharing systems vary on and help a
4. Solution
company optimise its revenue.
*The Curriculum is subject to change as per the inputs from university or industry experts
14
Logistic Regression
1.
Univariate Logistic Regression
2. Multivariate Logistic Regression: Model
Building and Evaluation
3. Logistic Regression: Industry Applications
2 WEEKS
Learn your first binary classification
tech-nique by determining which
customers of a telecom operator are
likely to churn versus who are not to
help the business retain cus-tomers.
1 WEEK
Classification Using Decision Trees
1.
Introduction to Decision Trees
Learn how the human decision making
2. Algorithms for Decision Trees Construction
process can be replicated using a
3. Hyperparameter Tuning in Decision Trees
decision treeand tune it to suit your
needs.
Unsupervised Learning: Clustering
1 WEEK
1. Introduction to Clustering
Learn how to group elements into
2. K-Means Clustering
different clusters when you don’t have
3. Hierarchical Clustering
any pre-defined labels to segregate
4. Other Forms of Clustering: K-Mode, K-Prototype,
them through K-means clustering,
DB Scan
hierarchical clustering, and more.
1 WEEK
Basics Of NLP and Text Mining
1.
Regex and Introduction to NLP
Do you get annoyed by the constant
2. Basic Lexical Processing
spams in your mailbox? Wouldn’t it be
3. Advanced Lexical Processing
nice if we had a program to check
your spellings? In this module learn
how to build a spell checker & spam
detector using techniques like phonet-ic hashing, bag-of-words, TF-IDF,
etc.
*The Curriculum is subject to change as per the inputs from university or industry experts
15
Business Problem Solving
1 WEEK
1. Introduction to Business Problem Solving
Learn how to approach open ended
2. Business Problem Solving: Case Study
real world problems using data as a
Demonstrations
lever to draw actionable insights.
Case Study: Lead Scoring
1.
Problem Statement
1 WEEK
Help the Sales team of your company
2. Evaluation Rubric
iden-tify which leads are worth pursu-
3. Final Submission
ing through this classification case
4. Solution
study.
Specialisation - Deep Learning
Course 3 - Machine Learning II
Bagging & Random Forest
1 WEEK
1. Popular Ensembles
Learn how powerful ensemble algo-
2. Introduction to Random Forests
rithms can improve your classification
3. Feature Importance in Random Forests
models by building random forests
4. Random Forests in Python
from decision trees.
Boosting
1.
Introduction to Boosting and Adaboost
2. Gradient Boosting
1 WEEK
Learn about ensemble modelling
through bagging and boosting and
understand how weak algorithms
can be transformed into stronger
ones.
Model Selection & General ML Techniques
1.
Principles of Model Selection
1 WEEK
Learn the pros and cons of simple and
2. Model Evaluation
complex models and the different
3. Model Selection: Best Practices
methods for quantifying model complexity, alongwith general machine
learning techniques like feature engineering, model evaluation, and many
more.
*The Curriculum is subject to change as per the inputs from university or industry experts
16
Principal Component Analysis
1.
1 WEEK
Prinicipal Component Analysis and
Understand important concepts relat-
Singular Value Decomposition
ed to dimensionality reduction, the
2. Principal Component Analysis in Python
basic idea and the learning algorithm
of PCA, and its practical applications
on supervised and unsupervised
problems.
Advanced Regression
1.
Generalized Linear Regression
2. Regularized Regression
1 WEEK
In this module, take a more advanced
look at regression models and learn
the concepts related to regularization.
Advanced ML Case Study
1.
Problem Statement
1 WEEK
Build a regularized regression model
2. Evaluation Rubric
to understand the most important
3. Final Submission
variables to predict the house prices in
4. Solution
Australia.
Specialisation - Natural Language Processing
Course 3 - Machine Learning II
Bagging & Random Forest
1.
Popular Ensembles
1 WEEK
Learn how powerful ensemble algo-
2. Introduction to Random Forests
rithms can improve your classification
3. Feature Importance in Random Forests
models by building random forests
4. Random Forests in Python
from decision trees.
Boosting
1.
Introduction to Boosting and Adaboost
2. Gradient Boosting
1 WEEK
Learn about ensemble modelling
through bagging and boosting and
understand how weak algorithms can
be transformed into stronger ones.
*The Curriculum is subject to change as per the inputs from university or industry experts
17
Model Selection & General ML Techniques
1.
Principles of Model Selection
1 WEEK
Learn the pros and cons of simple and
2. Model Evaluation
complex models and the different
3. Model Selection: Best Practices
methods for quantifying model complexity, alongwith general machine learning
techniques like feature engineering,
model evaluation, and many more.
Principal Component Analysis
1.
1 WEEK
Prinicipal Component Analysis and
Understand important concepts relat-
Singular Value Decomposition
ed to dimensionality reduction, the
2. Principal Component Analysis in Python
basic idea and the learning algorithm
of PCA, and its practical applications
on supervised and unsupervised
problems.
Advanced Regression
1.
Generalized Linear Regression
2. Regularized Regression
1 WEEK
In this module, take a more advanced
look at regression models and learn
the concepts related to regularization.
Advanced ML Case Study
1.
Problem Statement
1 WEEK
Build a regularized regression model
2. Evaluation Rubric
to understand the most important
3. Final Submission
variables to predict the house prices in
4. Solution
Australia.
*The Curriculum is subject to change as per the inputs from university or industry experts
18
Specialisation - Business Analytics
Course 3 - Advanced Machine Learning
1 WEEK
Bagging & Random Forest
1. Popular Ensembles
Learn how powerful ensemble algo-
2. Introduction to Random Forests
rithms can improve your classification
3. Feature Importance in Random Forests
models by building random forests
4. Random Forests in Python
from decision trees.
Model Selection & General ML Techniques
1.
Principles of Model Selection
2 WEEKS
Learn the pros and cons of simple
2. Model Building and Evaluation
and complex models and the differ-
3. Feature Engineering
ent methods for quantifying model
4. Class Imbalance
complexity, alongwith general
machine learning techniques like
feature engineering, model evaluation, and many more.
Time Series Forecasting
2 WEEKS
1.
Introduction to Time Series and its Components
In this module, you will learn how to
2.
Smoothing Techniques
analyse and forecast a series that varies
3.
Introduction to AR Models
with time.
4.
Building AR Models
Model Selction Case Study
1 WEEK
1.
Problem Statement
Apply your business acumen to the
2.
Evaluation Rubric
newly learnt machine learning tech-
3.
Final Submission
niques, and select the right model most
4.
Solution
appropriate for a provided business
scenario.
*The Curriculum is subject to change as per the inputs from university or industry experts
19
Specialisation - Business Intelligence/Data Analytics
Course 3 - Advanced DBS and Big Data Analytics
1 WEEK
Data Modelling
1.
Database Design Recap
In this module, you will learn and use
2. Building Blocks of Data Modelling
data modelling on a dataset to solve a
3. Problem Solving using Data Modelling
business problem.
4. Data Modelling: Optional Assignment
Advanced SQL and Best Practices
1.
Window Functions
1 WEEK
Apply advanced SQL concepts like
2. Case Statements, Stored Routines, and Cursors
windowing and procedures to derive
3. Query Optimisation and Best Practices
insights from data and answer pertinent
4. Problem Solving using SQL
business questions
Introduction to Big Data and Cloud
1.
Big Data and Cloud Computing
1 WEEK
Understand the basics of big data and
2. Amazon Web Services
cloud and learn to work with an EMR
3. Big Data Storage and Processing - Hadoop
cluster on a cloud-based service.
4. EMR Cluster in AWS
Analytics using Spark
1.
Exploratory Data Analysis with PySpark
2. Predictive Analysis with Spark MLLIB
2 WEEKS
Use PySpark to do EDA and Predictive
Analysis using Spark's ML library.
Big Data Case Study
1.
Problem Statement
1 WEEK
Use your analytics skills to work on a
2. Evaluation Rubric
large dataset in cloud to solve an indus-
3. Final Submission
try problem.
4. Solution
*The Curriculum is subject to change as per the inputs from university or industry experts
20
Specialisation - Data Engineering
Course 3 - Data Engineering - I
Data Management and Relational Database Modelling
1.
Enterprise Data Management
1 WEEK
Understand the concepts of Data
2. Relational Database Modelling
Management and learn to model data
3. Normal Forms and ER Diagrams
from a Relational Database.
Introduction to Big Data(Optional)
1.
4Vs of Big Data
2. Big Data: Industry Case Studies
0 WEEK
This module you will learn what big
data is, its various characteristics,
and its determining factors. You will
also get an idea of the various sources of big data and the wide range of
big data applications in different
industries such as retail, healthcare,
and finance.
Introduction to Cloud and AWS Setup
1.
Introduction to Cloud
2. AWS Setup
1 WEEK
Understand what is cloud and setup
your AWS account which will be
required duing the program.
Introduction to Hadoop and MapReduce Programming
1 WEEK
1. Concepts Retailed to Distributed Computing
Understand the world of distributed data
2. Hadoop Distributed File System
processing and storage with Hadoop.
3. MapReduce Programming in Python
Learn to write MapReduce jobs in
Python.
*The Curriculum is subject to change as per the inputs from university or industry experts
21
Assignment (Optional)
1. Introduction, Problem Statement and Grading
Rubrics
0 WEEK
Solve an assignment to brush up the
skills learnt so far.
NoSQL Databases and Apache HBase
NoSQL Databases and MongoDB (Optional)
1 WEEK
1. Concepts of NoSQL Databases
Learn the concepts of NoSQL databas-
2. Introduction to Apache HBase
es. Understand the working of Apache
3. HBase Python API
HBase.
4. Comparision of NoSQL Databases
Data Warehousing (Optional)
0 WEEK
1. Introduction to Data Warehouse and Data Lakes
Understand the intricacies behind
2. Designing Data Warehousing for an ETL
designing a data warehouse and a data
Data Pipeline
lake for use case/s.
3. Designing Data Lake for an ETL Data Pipeline
Data Ingestion with Apache Sqoop and Apache Flume
1 WEEK
1. Introduction to Data Ingestion
Get familiar with the challenges involed
2. Structured Data Ingestion with Sqoop
in data ingestion. Use Sqoop and Flume
3. Unstructured Data Ingestion with Flume
to ingest structured and unstructured
data into Hadoop.
Map reduce Programming Assignment
1 WEEK
1. Problem Statement and Sample Dataset
Practise MapReduce Programming on a
2. Solution
Big Dataset.
*The Curriculum is subject to change as per the inputs from university or industry experts
22
STUDY ABROAD CURRICULUM
IELTS PREPARATION
• Preparation of IELTS Exam
ACADEMIC ENGLISH
•
•
•
•
Language Development
Academic Writing Skills
Spoken Academic Communication
Reading and Listening in Academic Context
RESEARCH METHODOLOGY
• Introduction to Research
• Research Sampling
ACADEMIC INTEGRITY & RESEARCH
•
•
•
•
Introduction to Academic Integrity
Introduction to Plagiarism
Plagiarism : Advanced Understanding
Ethical Considerations
ACADEMIC REFERENCING
• Academic Referencing
• Referencing Style Guides
FORMS OF ACADEMIC WRITING
• Different forms of Long Format Writing
• Dissertation or Thesis Writing
• Writing SOPs
23
CRITICAL THINKING
•
•
•
•
Being a Critical Thinker
Reasoning Skills
Persuasion
Critical Thinking as a Life Skill
NETWORKING AND PROFILE BUILDING IN FOREIGN DESTINATION
• Networking
• Profile Building
FINDING JOBS AND INTERNSHIPS
• How to Find Jobs And Internships in Foreign Destinations
IMMIGRATION AND VISA RULES
FINANCING EDUCATION
24
2. MS IN PROJECT MANAGEMENT
FROM CLARK UNIVERSITY, US (YEAR 2)
1. IT PROJECT MANAGEMENT FUNDAMENTALS
This course provides participants with the foundation, techniques and tools to manage each stage
of the project life cycle, working within organizational and cost constraints, setting goals tied
directly to stakeholder needs, getting the most from their project team, and utilizing
state-of-the-art project management tools to get the work done on time and within budget. It
covers all Project Management Knowledge Areas: Integration, Scope Management, Time
Management, Cost Management, Scheduling, Estimating, Risk Management, Contract
Management, Quality Management, and Leadership & Communication, and covers the entire
project life-cycle as well as all Project Management Process groups (Initiating, Planning, and
Executing, Controlling and Closing). It also provides an introduction to the principles and
practices of project management. A blend of lecture, discussion and practical application in
managing projects and teams this course provides the students with an optimized learning
process and practical application of the technical and soft-skills learned.
2. AGILE SOFTWARE DEVELOPMENT METHODOLOGY
Whether you work at a startup (breaking into a market) or a large corporation (attempting to
maintain market share), your customers demand delivery of frequent innovations that make their
user experience better and differentiate your products from the growing competition. To ensure
this, organizations need to embrace a new way of developing these strategic features.
Continuing the project management journey, this course builds on the foundations gained in the
Project Management Fundamentals course and extends them to the Lean Agile delivery
methodology. By acknowledging that delivery methodologies are not ‘one size fits all’, we
become versed in the one that meets the challenges faced by today’s technology innovators.
This is the basis on which we can layer the progressive techniques found in Lean Startup and the
Scaled Agile Framework.
As a project leader, you’ll investigate new roles, responsibilities, checkpoints (ceremonies), and
metrics. Our course goals are accomplished through reading, homework assignments, in-class
discussions, group presentations and an agile project simulation.
3. ORGANIZATIONAL BEHAVIOR & LEADERSHIP
Develops an understanding of concepts, analytical tools and communication skills underlying
behavior in organizations. The course explores the relationship between task accomplishments
and human fulfillment in the context of planned organizational change.
25
Various learning experiences are incorporated, including case studies, simulations, role playing
and group discussion. Issues of public involvement, participatory decision making, employee
empowerment and forms of leadership are also addressed.
4. IT ECONOMICS, FINANCES AND BUDGETING
The ongoing evolution of IT deployment impacts how companies view and execute projects of all
sizes. This course will provide students a foundation to understand how to consider development
and deployment options with IT projects, consider the impact of globalization of IT on cost and
negotiate 3rd party participation in projects.
5. RISK, QUALITY AND CHANGE MANAGEMENT
Assessing risk, managing quality and effectively managing change are 3 of the critical success
factors of any project. This course will, through a series of readings, lectures and selective case
studies introduce methodologies for each area and provide an understanding of how they
collectively affect the ultimate success of any project.
6. PROGRAM AND PORTFOLIO MANAGEMENT
Larger organizations in all industries often manage large projects with or as part of Program
Management organizations. This course, through a combination of lecture, discussion and case
studies, introduces students to the strategy’s organizations use to prioritize and manage a
portfolio of complex and often geographically distributed projects.
7. MANAGING TROUBLED PROJECTS
Projects may start without formal management or project managers may be called on to rescue
trouble projects. Regardless of the circumstances, project managers will, at some point in their
career, be responsible for “finding a path forward”. This course, through a mix of case study
analysis, lecture and classroom discussion will provide students with the skills necessary to be
successful.
8. EXPERIENTIAL LEARNING
Experiential Learning is the culminating experience of MSPM students. Students will demonstrate
the professional competencies gained in the classroom through one of several options;
Capstone Projects and Capstone Research or Case Studies. Each option provides the opportunity
to apply and improve their professional skills, analytic and research skills and pragmatic problem
solving in areas specific to their professional aspirations.
For more details visit:
www.clarku.edu/academics/graduate/programs/masters/masters-in-project-management/
26
MEET
THE CLASS
INDUSTRIES
OUR LEARNERS COME FROM
15% Others
1% Finance
1% Manufacturing
1% Telecom
1% Education
1% Consulting
57% IT
3% Retail
5% E-commerce
5% Healthcare
10% BFSI
WORK EXPERIENCE (IN YEARS)
15% 6.1-9 years
11% 9.1-12 years
21% 3.1-6 years
33% 0-3 years
20% 12.1+ years
A few of the companies our students are from: Accenture, Amazon, Cognizant, Deloitte, Infosys, Microsoft, Wipro, EY,
CitiBank, Cisco, Thomson Reuters
27
HEAR FROM
OUR LEARNERS (IIITB)
Kunwar Alok, Experience: 15+ Years
“You may not believe but I have never done coding in my life. I did it during this program and
was thrilled to see the outcomes coming out of those codes. Just the way I used to get happy
after solving a good (tough) math problems during my school age. Thanks to upGrad for doing
a great service to people like us who at the age of 43 can dream to study with budding talents
around.”
Sachin Aggarwal, Experience: 18+ Years
“Learning with IIITB and upGrad has been an experience like no other. Being an online
program, you have your worries about how the program and teaching methods will be. My
favourite part about the learning experience has been programming through well designed
and thoughtful content shared by IIITB professors and industry experts on upGrad platforms.
Kudos to upGrad.”
Savita Upadhaya, Experience: 4 Years
“It has been amazing journey with upGrad till now. Starting with their Program material to Live
sessions to Mentor support helps one to always be on track and progress efficiently with Data
Science program. My sincere Thanks to the entire team of upGrad. And Profs of IIITB to show
me the path and direction for my dream to become a Data Analyst.”
Sidharth Mahapatra, Experience: 3 Years
“The concepts of R programming and Machine Learning will be taught by Prof. Chandrasekhar
Ramanathan and Prof. G Srinivasaraghavan respectively. Both of them have been listed in top
twenty prominent data science academicians list published by Analytics India Magazine. So
you need not worry about quality of teaching in this program.”
28
Tuhin Pal, Experience: 5 Years
“I appreciate the platform upGrad how they have arranged the modules and the assignments
are quality ones. You will relish your college days again as the exams are felt like semester
ones, you can’t talk to anybody . Modules are locked till you complete the previous one so it
feels like clearing a semester and going to the next one.”
Harkirat Dhillon, Experience: 8 Years
“A dedicated regimen for studying the program and keep learning is the key to be successful
and pass the program. This program will help build a strong foundation for successful
transition to data science. Additionally, participating in hackathons and Kaggle competitions to
solve real world problems will definitely give you an edge and land a job if one is willing to
work hard.”
Shravani Shahapure, Experience: 16 Years
“For someone who really wants to pursue career in the field of data science, it is worth to opt
for the complete program by IIIT B and upGrad. Data science is experimental science. We
need to develop right kind of thinking ability to approach to the problems. And to develop this
ability, we need experts direction. Knowing tools won’t solve the problems always, we need to
use them wisely and correctly. IIITB and upGrad‘s online program on data science gives this
opportunity and develop student for their future as they provide best professors, thought
provoking assignments and case studies .”
Sagar Tekwani, Experience: 2 Years
“A very well-structured and well-balanced program content which you won’t get in other
programs/nano-degrees. Being a beginner in DS, I found the structure of Executive PG
Programme from IIITB and upGrad most helpful. They even teach you most of the prerequisites
with prep sessions before you even start the program. Being a working professional, it was
neither too difficult nor too easy to keep up with the pace of the program.”
29
UPGRAD ABROAD
ONSHORE ALUMNI BENEFITS
Our support does not end up once you board a flight to your study destination but also continues
in the University you join. Once you reach the University, you will be provided following additional
support by upGrad abroad:
•
upGradabroad Onshore Buddy
Entering a foreign land can be a blissful experience when one knows that someone is always
there to assist in attuning to the culture and other important aspects related to the country.
With upGradabroad courses, the students will be entitled to one such service which is onshore
upGradabroad buddy.
As the name suggests, the onshore upGradabroad buddy will extend the support in many
ways like helping them with the orientation session, arranging the city tours, guiding you with
the local visa process, finding the accommodations, city registration, opening a bank account,
and other mandatory services.
Not only this, the onshore buddy will organize insightful sessions on destination country
cultures so that the students can easily get along with the local students at the university. The
onshore buddy will extend his support in finding part-time work at the university or outside
University (if allowed) and providing guidance on how to apply for a post-study work visa /
OPT (optional practical training).
•
Extended upGrad Career Services
There are also extended career services offered by upGradabraod and that include building a
strong resume, organizing mock interviews, and providing guidance on the best practices to
find onshore jobs.
•
Alumni Portal
As an upGradabroad student, the students are entitled to access the Alumni portal where they
can build a professional network with other upGradabroad Alumni for assimilating information
related to finding jobs or social integration.
•
upGrad Alumni Discount
Under the upGrad Alumni discount, the students can avail of great discounts on the courses
that they would like to take up in the future which can help them in upskilling their careers and
suit their resume for jobs abroad.
30
ADMISSION PROCESS
PROGRAM DURATION AND FORMAT
8 months Online | 12 months On-Campus in United States
PROGRAM FEE
Online Course Fee - INR 3,00,000
(incl. of all taxes)
On-Campus at Clark University
- Indicative Tuition Fees - USD 17,052
- Indicative Cost of Living - USD 19,330
ELIGIBILITY CRITERIA
• Applicants with 4 year bachelor’s degree need to have minimum 66% marks.
Maximum backlogs (ATKTs) accepted will be 6.
• Applicants with 3 year bachelor’s degree need to have minimum 60% marks.
Maximum backlogs (ATKTs) accepted will be 4.
• A 4-year Undergraduate degree is preferred. However, if a learner has completed 3 years of
Undergraduate, then the awarding University must be NAAC A or A+ (in year 2019/ 2020/2021 and
preferably from a Science background i.e. BCA/B.Sc/BIT. If an applicant is coming from any other
stream (Business, Humanities etc) the applicant needs to have substantial work experience in relevant
area of application.
PROGRAM START DATES
Please refer to the website for program start dates.
SELECTION PROCESS
Step 1: Complete
Application Form
Fill out an application
form Online
Step 2: Get Shortlisted & Received your
offer Letter
Our admissions committee will review your
test score & profile. Upon qualifying, an
offer letter will be sent to you.
Step 3: Block your Seat & Begin the
Prep Course
Block your seat with a payment of INR
25,000 to enroll on the programme.
Begin with your Prep course and start
your Project Management journey!
For further details, contact:
admissions@upgrad.com | 1800-210-2020
upGrad Education Private Limited
Nishuvi, 75, Dr. Annie Besant Road Worli, Mumbai - 400018
info@upgrad.com | 1800-210-2020