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MDAE’s one-year PGP in Data Science Diploma will provide you with the foundation you need to become a data scientist. Special emphasis is placed on Finance & Financial Technologies in this diploma program.
The rigorous and technical nature of the course also enables you to strengthen your foundations for higher education.
We teach using a hands-on approach, incorporating case studies and real-world situations. Each course and topic are designed to prepare you for the actual problems you will solve during your career.
Due to MDAE’s self-governed curriculum, we can integrate new skills, areas, and learning tools quickly & seamlessly, considering rapid industry developments and changes.
In a short period, MDAE’s course has become an ideal platform to help students get into the industry and leverage the academy’s growing stature and industry network.
This is the only PGP program in the country that integrates Finance, Data Science, and Technology. We believe that both technical skills and domain knowledge are essential for data scientists given that the financial services and fintech industries are the largest consumers of data scientists.
Your knowledge of critical data science, finance, and technology skills will include:
A 9-month classroom-based training course is followed by a three-month internship. The course allows you to apply some of the principles you’ve learned to real-world problems.
The program commences with a rigorous foundation course in Mathematics and Statistics. The pedagogy focuses on teaching the basics of data science, including combinatorics, calculus, linear algebra, and statistics. This course will prepare you to learn the principles and techniques of data analysis and machine learning learned in subsequent courses.
Continuous assessment patterns emphasize developing students’ problem-solving, algorithmic thinking, programming, data analysis, visualization, and machine-learning skills.
The course provides a healthy exposure to various tools such as Microsoft Excel, IDLE/Jupyter Notebook/PyCharm for Python programming MySQL, Power BI, and Tableau.
The year is broken into 4 distinct parts – Foundation, Core, Electives & Internship/Capstone Project, and students need to acquire 22 credits to clear.
Foundations Course – 8-week primer on Mathematics & Statistics.
Details of our core courses can be found here.
Electives – Students need to choose any 2 out of 6 electives (each 30 hours) Students can also audit electives should they choose to.
To view our electives – Please click here.
At the end of 9 months, students are assigned capstone projects or internships through the academy’s careers office where they have a chance to work with practitioners on real world problems and apply theories that they have been taught in class. Some of our project partners include –
Zebpay (a leading cryptocurrency exchange)
Breath AI – A leading health tech product company.
MDAE is a pioneer in providing applied economics and data science education in the country. Our global faculty, agility in course design and world class industry access ensures that our graduates are well-equipped when starting their professional journey.
To read about placements in detail, click here.
Candidates who wish to apply for the Post Graduate Diploma in Data Science and Finance need a minimum 50% marks from a recognised undergraduate degree in any discipline.
MDAE follows a rolling admissions policy, which means that candidates who apply early have a higher chance of being selected. It also means that once our seats are filled, applications will be closed. We have 2 cycles in our admissions process – Early Bird Applicants (From Nov – Jan) and then normal admissions cycle. Historically more than 60% of our seats have been filled during early bird admissions so it is important that candidates apply early.
The admissions process is a 4-step process –
The course fees for the Post Graduate Diploma in Data Science and Finance is INR 5,75,000 (excluding GST). The fees mentioned are tuition fees and do not include the cost of accommodation & living expenses.
Several central and private banks have granted loans to MDAE students. MDAE has also secured partnerships with leading fintech players to help secure loans. If you would like to connect with them, please reach out to the admissions team on 7045999326.
MDAE also provides an easy EMI facility for paying the fees in installments.
Merit based scholarships are available to students at the time of admission. Scholarships upto 50% of the tuition fee are available. The decision for awarding scholarships will be based on merit and will be decided post the interview of the candidate.
|Application Start Date||10th November 2022|
|Entrance Test||3rd December 2022|
|Interview Date||Contact the counselor on 7045999326 to get details|
Yash Beswala – 2021 Data Science Batch, Equity Researcher at JP Morgan:
MDAE has helped me bridge the gap between my undergrad in BBA and the quant intensive course which I want to take up in the future. It has helped me develop strong quantitative and programming skills from basic, which is not generally offered as a post-graduate course in the country. Overall, I think that this course has motivated me to dive further into the world of Fintech and has cleared my misconception of programming in the world of Finance.
The course is best suited for students as well as young working professionals who are looking at achieving the following outcomes –
You can apply for the PGP in Data Science Diploma even if you do not have a science degree. You do however need to demonstrate a certain degree of proficiency in Mathematics & Statistics to be able to cope with the rigour of the course. We have admitted students from disciplines such as commerce, physics, management, psychology, mass media, engineering, chemistry, and other diverse fields into our Data Science Diploma. Graduates of our Data Science course compete confidently with science and engineering graduates in Data Science jobs.
Yes this qualifies as a 16th year of education. Our alumni, over the past 5 years, have a strong track record of securing admissions into leading foreign universities for Masters, MBA and even Ph. D programs. Universities such as Harvard, Georgetown, Chicago, Rotman, Boston, Georgia State, George Washington, Oxford, LSE, and many more have recognized and admitted alumni from MDAE.
You can write to us at firstname.lastname@example.org or call us on 7045999326.
MDAE assists students to take up private accommodations in the vicinity. We provide you with a verified list of brokers who then will assist the student in finding the accommodation.
You can be exempted from giving the test if you have – 600+ in GMAT, 90 percentile and above in CAT.
The purpose of these courses is to prepare students for the Core Courses that will follow. Since not all students accepted into the program will have an undergraduate education in mathematics and statistics, the Foundations Courses will introduce them to the essential elements of Mathematics and Statistical theory. For students who do join the program with some undergraduate training, the Foundations Courses will help to review concepts that they may have forgotten. Foundation courses will cover three subjects:
In this course students will learn:
Learning outcomes of this course:
Data Scientists work with data drawn from the real world, and students are required to become proficient with the basic tools of data analysis. In this course students will learn:
Learning outcomes of this course::
After clearing the Foundation exam, all students are required to take 5 Core Courses
1) Data Management & Programming using Python
Course objective: The primary objective of this course is to provide a firm foundation in the application of data science principles to various fields of study, especially, Economics and Finance. The course will be exclusively taught on Python and particularly caters to programming novices.
Installation; IDEs (IDLE, Jupyter Notebook); a programmer’s logic view of a computer; basic data types int, float, string, boolean; variables; the None type; print and input functions, escape sequences; operators – arithmetic, assignment, relational, Boolean and identity operators; the format function; the sys.argv object; string functions; iterability, subscripting and indexing of objects; Unicode strings; the list data type, list functions, list comprehension; list operators, the tuple data type; the class type; the range class; the for loop; the while loop; nested loops; the enumerate function; the zip function; introduction to itertools; python in-built functions; the if, elif, else statements; mandatory code indentation in Python; user-defined functions – declaration, arguments, body, return, variable number of arguments, named arguments; lambda functions; nested functions; generator functions; the dictionary data type; items, keys, values of a dictionary, dictionary methods; list of dictionaries; sorting a list of dictionaries; mutable and immutable data types; the numpy package; ndarrays; numpy methods; the pandas package; the dataframe type; the series type; the groupby type; dataframe indexing – iloc and loc; dataframe operations; filtering a dataframe using a boolean array; pandas functions; dataframe columns and index; multi-index; pivot tables; dataframe apply method; pandas merge; sorting of sortable types; the set data type.
Learning Outcomes: At the end of this course students would have grasped the essentials of core Python concepts and be extremely comfortable processing data on Python.
2) Core Finance
The Finance Core portion is designed as a natural extension to the Foundation course. The Foundation course gives a gentle introduction to various key areas in finance, whereas the Core course goes deeper into explaining the intricacies of the financial domain.
Course objectives: Empirical data analysis using statistical and mathematical tools forms an integral part of contemporary economics. The objective of this course is to introduce students to the fundamental ideas and tools in Econometrics in an applied and intuitive way. The distinguishing factor of this course is its copious use of the R programming language to illustrate the applications of the econometric tools learnt and hands-on modeling using real-world data. To illustrate, over the time of this course, students will be encouraged to answer questions of the following nature using econometric models with R.
Learning Outcomes: By the end of this course, students will have a good theoretical and practical understanding of the fundamental econometric models and be well equipped to apply them on real world data using tools such as R. This course will build the basic foundation for careers as economists, finance professionals and data analysts and in general for any job role involving data analysis.
4) Data Wrangling and Analysis
Course objectives: Data wrangling is important to learn if you want to be able to gather, select, and transform data to answer a question or solve a problem. This is essential for two reasons. First, the work of data wrangling can speed the time to develop and test models. Second, it allows for faster analysis and more accurate conclusions. Data wrangling makes data more usable in an organization. Understanding it can help your work better and make sounder decisions based on the data.
Shaping and clean-up of raw data
Metrics of numeric variables, Metrics of categorical variables
Quartile analysis, Z-Score analysis, test of normality, skewness, generating data for a distribution, outlier analysis, crosstab and pivot tables, covariance, correlation, linear regression, exponential regression
Relational databases and database objects; CRUD operations of a database; Anatomy of a SELECT statement; Basic SELECT statements; Clauses: SELECT, FROM, SELECT DISTINCT, WHERE, GROUP BY, ORDER BY, UNION, LEFT JOIN, IN, INNER JOIN, RIGHT JOIN, FULL JOIN LIMIT; Functions and keywords: DATEDIFF, COUNT, COUNT(*), SUM, MIN, MAX, AVG, BETWEEN, NULL; Operators: *, >, >=, <, <=, =, BETWEEN, IN, AND, OR, NOT, AS, IS
The philosophy of Data Visualization – the WHY, WHAT and HOW of Visualization; Univariate Visualization, Bivariate Visualization, Multivariate visualization.
Visualizations covered across Microsoft Excel, Python (matplotlib), Power BI, Tableau:
Histogram, box and whisker plot, bar and column charts (simple, clustered, stacked, 100% stacked), line chart, area chart, dual-axis line and column chart, heat map, diverging bar chart, waterfall chart, funnel chart, scatter plot, pie chart, donut chart, treemap, sunburst chart, geographical map charts, pareto chart, cards, tornado (butterfly) chart, animated race chart, dashboards
5) Machine Learning
Course objective: The purpose of this course is to familiarize the students with modern machine learning models. Most of the courses in this area either focus on the concept or application of machine learning algorithms. This is an introductory course for theoretical machine learning. Furthermore, in this course, most models will be explained with great details such as problem statements, solutions, algorithms, implementation, and solving real-world problems with actual data.
Learning outcome: By the end of this course, students will be able to formulate real-world problems and their solutions using mathematics and move on to advance machine learning techniques. The course will also prepare students with advanced skills for programming with machine learning models.
In this semester you are required to take 2 out of 6 electives:
1.Time Series Econometrics-
Course objective: Much of the data in economics and finance are indexed over time and present specific challenges in modeling and forecasting. This course is designed to equip students with the econometric tools used specifically in time series data analysis and implement those tools on real-world economic and financial data. The course will make rigorous use of the R programming language to illustrate the concepts and give students a hands-on experience of modeling and forecasting time series data. Some of the questions that this course will motivate students to answer are:
Did the pandemic cause a structural break in India’s IIP?
Learning Outcomes: By the end of this course, students will bTe familiar with the theoretical underpinnings of time series analysis, be able to build models with time-series data, run them and interpret the results on R. they shall be ready to take up roles in the industry in the fields of finance, macroeconomics and data analytics that involve building models and forecasting trends for the economy.
2.Advanced Econometrics for policy analysis–
Course Objectives: Recent Nobel prizes in Economics given to the likes of Abhijeet Banerjee, Esther Duflo, David Card and others are enough evidence to show the growing importance of natural experiments in the field of economic policy analysis. This important course covers recent developments in the field of Econometrics where causal inferences are used to understand and evaluate policy outcomes. The course will equip students with advanced tools in Econometrics used in evaluating public policy outcomes and beyond. R programming will be used extensively to support the teaching. During this course, students can expect to answer the following type of questions based on econometric modeling.
Learning Outcomes: By the end of this course, students will be able to assess the different contexts in which these econometric techniques can be applied and how to apply them on real-world data. They will be in a position to evaluate public policies using modern econometric tools of causal inference. This course will prepare students for a career in development economics, public policy, data analytics and other allied fields.
3.Behavioral and Experimental Economics: A Public Policy Perspective
Course Objectives: The goal of this course is to help students think creatively and critically about public policy issues by providing an understanding of how behavioral and experimental economics can be used to understand policies in the real world. The course will introduce cutting-edge research in behavioral and experimental economics and its implications for public policy. A particular emphasis will be given on behaviorally informed tools, such as default rules, and norms to study a range of issues at the intersection of behavioral economics, and public policy.
Learning Outcomes: By the end of the course, students will be able to conduct experiments (both in the laboratory and in the field) and use the data from the experiments to evaluate theories as well as to test and fine-tune policies that could not be easily tested with naturally-occurring data. Students will also be able to analyze complex public policy issues as well as compare the merits and demerits of different policy approaches to a particular problem using insights from behavioral economics.
4) Technologies in Finance
Course Objectives: The primary objective of this course is to provide a firm understanding of the various upcoming fintech companies and its evolution. The course will guide the students in understanding the fintech market better.
Learning outcomes: At the end of this course, the students would have grasped in detail about the most prominent fintech sectors and the trend within these sectors.
Course Objectives: Corporate finance is concerned with understanding what financial managers should do to increase company value. This course is designed to introduce essential aspects of financial decision-making in businesses. The primary objective is to provide the framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. We will work with live examples to study the application of these concepts. This course will provide Economics students the financial acumen which they can apply to not just for economics-based roles, but also to core finance and risk roles offered in the industry.
Quantitative Finance is an important area that has a lot of applications in the domain for finance/banking. This specialized skill set has been around for a while now, and it will continue to be in demand from the industry going further. Further, many concepts covered under this course integrate well with ideas in data analytics too as both these topics build on various concepts from math and statistics. Candidates with expertise in quantitative finance theory along with hands on skills to build models will be able to work in good roles in the areas of quantitative analytics/ risk / pricing in banks/financial institutions.
Big Data workshop: (14 – 16 Hours)
Internship/Capstone Project (3 months) Professional readiness program (throughout the course):
The program adheres to a philosophy of continuous assessment throughout the academic year to ensure that students effectively master the course materials and get enough opportunities to test their mastery. To this end, the students will be graded on the following:
The precise breakup of the final course grade into these diverse components, and also the precise form that these components will assume in each course, will remain at the discretion of the faculty instructor, and will be communicated to students via the course syllabus that the instructor will hand out on the first day of the course.
Equity Research, J P Morgan
Data Scientist, ZebPay
Data Scientist, Kotak
University of Texas at Dallas