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12 courses with an estimate student effort of 15-18 hours a week
Offered by O.P. Jindal Global University
Hands-on learning from anywhere, no travel required
Option to choose cross electives in International Relations. Learn more
The M.A. in Public Policy uses a transdisciplinary approach to comprehensively study and analyse contemporary political, economic, and social issues. The curriculum is carefully structured to impart intense training in policy theory, covering key literature and policy debates. You will develop and build on your quantitative and qualitative analytical skills and receive exposure to real-world policy-making and related management processes. The program's first year builds the foundations required for a broad-based understanding of public policy before offering students the choice of the data analytics and policy design tracks in the second year. The second year of the program will also give you the option of working on a faculty-mentored master's dissertation to help develop the capacity to conduct independent research.
12 courses; 60 credits
12 months (estimated learning load of 15 - 18 hours a week)
The curriculum comprises core and elective courses.
Each course accounts for five credits and requires a total study time of 75 hours. This includes time for assimilation of instructional material (pre‑recorded lectures and essential readings), live interactive sessions, discussion, and assessments besides time for self‑study.
Each course is spread over 11 weeks, with the last two weeks reserved for end-of-course evaluations.
Students will develop and build quantitative and qualitative analytical skills and receive exposure to real-world policymaking and related management processes.
A unique feature of the programme is the option of a master's dissertation in the last quarter, allowing students to work on a faculty-mentored master's dissertation that will develop the capacity to conduct independent research.
(Core courses, 5 credits each)
Microeconomics: Foundation and Insights
Theories of Public Policy
Qualitative Analysis and Survey Design
(Core courses, 5 credits each)
Law, Governance and Public Policy
Macroeconomics: Foundation and Insights
Statistical Methods and Data Analysis
(2 Core courses, 1 Elective - 5 credits each)
Data Analytics Track
Econometrics Theory and Practice
Introduction to Data Science
Elective 1
Policy Design Track
International Organizations and Policy Regimes
Public Finance
Elective 1
(1 Core course, 2 Electives - 5 credits each)
Data Analytics Track
Machine Learning, AI and Public Policy
Elective 2
Elective 3
Policy Design Track
Programme Design and Evaluation
Elective 2
Elective 3
Dissertation Track
You can also choose to work on a faculty-guided master's dissertation instead of two electives.
Based on their research interest, students will be assigned a faculty mentor to help them develop their dissertation thesis.
Students must indicate their preference for the dissertation track at the end of the third quarter.
The dissertation workshop must be completed before students commence their dissertation writing.
The dissertation is worth eight credits and runs in the last quarter.
During the fourth quarter students focus primarily on developing the dissertation thesis under the guidance of their faculty mentor.
The Non-dissertation track which allows students to choose up to three electives from the following list:
School Electives:
Complexity and Public Policy
Democratic Governance
Development Economics
Economics of Reforms and Regulations
Environmental Issues, Policies and Practice
Gender and Development
Identity and Policy
Introduction to Health Policy
Social Foundations of Public Policy
Methods of Impact EvaluationUrbanisation and Development
Cross Electives:
International Relations Theory
Law and Ethics in International Relations
Climate Change, Migration and Human Security
Negotiating and Resolving International Conflicts
Foreign Policy of Great, Middle and Small Powers
Data analytics track
Policy design track
Upon completing the recommended industry certifications, students can receive waivers for up to two courses or ten credits toward their degree.
Prepare for the M.A. Public Policy Online Program by completing the following MOOC courses (non-credited) hosted on Coursera. These courses will help you build familiarity with the program and the field of Public Policy:
Ethics in Public Policy: Delve into the ethical considerations that shape policy decisions and learn how to navigate complex moral dilemmas in public service.
Essence of Leadership: Explore the fundamental principles of effective leadership and how they apply to public policy.
Introduction to Academic Writing: Enhance your academic writing skills to excel in the program and effectively communicate your policy analysis and research findings.
Documents Required
Programme Schedule
The programme is offered in 2 intakes: April and October.
Not ready to enrol yet? Complete these professional certificates stacking into your degree credits.
Upto 15 Credits will be recognised for learners who have completed ANY one of the following: -
- Google Data Analytics Professional Certificate
- IBM Data Analyst Professional Certificate
- IBM Data Science Professional Certificate
- Fractal Data Science Professional Certificate
Not ready to commit? These courses may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.
Want to learn more about the programme?
Have questions? please contact online@jgu.edu.in.
Documents Required
Programme Schedule
The programme is offered in 2 intakes: April and October.
Not ready to enrol yet? Complete these professional certificates stacking into your degree credits.
Upto 15 Credits will be recognised for learners who have completed ANY one of the following: -
- Google Data Analytics Professional Certificate
- IBM Data Analyst Professional Certificate
- IBM Data Science Professional Certificate
- Fractal Data Science Professional Certificate
Not ready to commit? These courses may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.
Want to learn more about the programme?
Have questions? please contact online@jgu.edu.in.
Want to learn more about the degree before you commit? Enrol in one of the University of Leeds' open courses to get a preview of the topics, materials and instructors in the MSc Data Science (Statistics) programme.
Programming for Data Science: Explore the basics of programming and familiarise yourself with Python.
Exploratory Data Analysis: Learn how to analyse and investigate data sets and explore ways to visualise data.
Statistical Methods: Understand the role of statistics in data analysis and gain experience using RStudio for creating numerical and graphical summaries.
This course will introduce students to basic techniques, which can be used to perform a preliminary investigation of data sets. Exploring data involves visualising the variables and relationships to help determine outliers, identify trends, suggest suitable statistical models and inform future data gathering.
On completion of this module students should be able to:
The module provides a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.
Indicative content for this module includes:
This module introduces the fundamental skills of programming in python. The aim is for students to develop the skills and experience to independently translate a broad range of data science related problems into functioning computer programs and communicate the results.
Indicative content for this module includes:
Students will undertake a sequence of programming exercises starting with the fundamentals of programming and building up to a system that performs significant data analysis on real data:
The objective of this course is to equip students with the skills necessary to undertake project work as a data scientist. Project planning, reviewing existing methodologies and the presentation of outputs in different forms all form part of this. This module will also include ethical considerations of data usage.
On completion of this module students will be able to:
Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.
Indicative content for this module includes:
In big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
On completion of this module students should be able to:
This course will equip students with understanding of the theory of linear models and be able to fit multiple linear regression models to data and interpret the results. The content will develop an appreciation of the limitations of linear models and the use of link functions to generalise the linear regression model. In particular, the module will explore logistic regression and log linear models.
On completion of this module students should be able to:
Indicative content for this module includes:
This course introduces key concepts and techniques in statistical learning which are relevant to a number of practical applications. These techniques include statistical machine learning for classification and regression.
On completion of this module students should be able to:
Data scientists work in a wide range of fields of application. This module gives an insight into some general principles of the work of a data scientist and some of the underpinnings of artificial intelligence and statistics in the practice of data science.
Indicative content for this module includes:
The module aims to equip students with the ability to apply standard methods for random number generation and apply different Monte Carlo methods and develop understanding of the principles and methods of stochastic simulation. The module will also instruct students on how to implement statistical algorithms for a given problem and develop familiarity with software for advanced statistical computing.
On completion of this module students should be able to:
Indicative content for this module includes:
The objective of this course is to introduce Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. This module also introduces the ideas of quantitative decision theory and rational decision making.
On completion of this module students should be able to:
The objective of this course is for students to plan, carry out and present the results of a short project in data science. The project will be presented in a professional format that could serve as an exemplar of their work for a future employer or client.
On completion of this module students will be able to:
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