Post Graduate Diploma in Data Science & Finance

• 1) Rigorous training to be a Data Science expert
  • 425 hours of classroom learning spread over 9 months + 3 months of industry internship/capstone project.
    • 200 hours of industry driven core courses
    • Learn fundamental theories behind data science tools/machine learning models and Artificial Intelligence.
    • Become skilled at creating your own models and deep learning software, while many other courses only teach you how to apply the tools.
• 2) Become a Leader in the field – understand how to define a problem, identify which machine learning models to use to solve the problem and even create the models, the task of a leader/manager, rather than just apply the tool, the task of a worker.
• 3) Gain knowledge of Finance – create and optimise financial portfolios, create hedging strategies, portfolio optimisation using machine learning, which is essential to be a scientist in the industry, which other courses fail to teach. 60% of data scientists are employed in Finance.
• 4) Learn from the best – strong mix of academics and practitioners in faculty. Our faculty are from IIT – IIM and other premier institutes in the country, as well as top Data Analytics firms such as the founder of Decimal Point Analytics. Low student/faculty ratio of 7:1 to ensure personal attention.
• 5) Access to Top Corporates – support from distinguished corporate mentors who can help shape your future and give life–changing advice. Placement assistance is provided through MDAE’s strong industry connect and alumni network which help in fetching better internships and full-time placements.
• 6) Develop critical skills – communication skills, presentation, debate, public speaking and team-work skills which are necessary to succeed in any career.

Eligibility:

• Graduate from any stream with 50% or equivalent score.

Selection Process:

• Application form with CV/Resume and SOP.
• 1-hour online Quantitative Techniques test (evaluation fees – Rs. 2000/-).
• Panel interview .

Next Entrance Test Date: 21st March.

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, Statistical theory and Finance. 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 four subjects:

1) Mathematics

In this course students will learn:

• Linear algebra,
• calculus,
• graphs,
• Optimisation,
• numerical methods.

Learning outcomes

• Familiarize students with undergraduate to postgraduate mathematics
• Learn how mathematics is useful in analytics.
• Ability to solve mathematical problems
• Preparedness for the Core Course

2) Statistics

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:

• What a random variable is, and how to describe it using the concept of probability
• How to work with common probability distribution functions such as the Binomial, Poisson and Normal distributions
• How to compute the expected value and variance of a random variable
• How to work with jointly distributed random variables
• How to formulate a statistical hypothesis, and how to test it
• How to compute confidence intervals
• Markov Chain methods.

Learning outcomes
The purpose of this course is:

• to familiarize students with undergrad to post graduate statistics.
• Applicationsof statistics into business and data analytics, economics and finance, and variance fields of studies.

3) Programming

• Principles of programming,
• variables and data structure,
• functions and modules,
• I/O file,
• handling large projects.

Learning outcomes
The purpose of this course is:

• to familiarize students with imperative programming style.
• To construct algorithms and then program these algorithms in python programming language
• To familiarise students with a wide range of real finance problems with actual data sets.

4) Finance

• Financial Accounting, Double-entry bookkeeping
• Time value of money
• Risk & risk management

After clearing the foundation exam, all students are required to take 5 Core Courses

1) Foundation and Advanced Machine Learning

Contents

• Finding patterns in data,
• handling missing and incomplete data,
• dimension reduction techniques,
• numerical methods in ML,
• decision tree and random forests,
• clustering,
• Support vector machines,
• genetic algorithm,
• modeling real life problems into graphs,
• product recommendation methods, deep learning and neural network,
• boosting methods,
• stochastic gradient methods, kernel methods,
• fundamental theorem of learnings.

Learning outcomes

• How to model and solve the real-world problems of analytics using machine learning methods.

2) Economics of Financial Markets

Contents

• Asset Pricing (50%):
  • Pricing bonds, interest rates, YTM
  • Pricing stocks
  • Efficient markets theory
  • CAPM
  • Pricing options and other derivatives
• Financial Markets & Institutions (50%):
  • Depository Institutions & the Central Bank
  • Non-depository Institutions
  • Fixed Income markets
  • Equity markets
  • Derivatives markets

3) Data Visualisation and Management

Modern computing abilities allow the data to tell its own story. But you still have to be able to understand what the data is saying. Modern data sets are large and varied. Specialized data visualization techniques can help us to physically see the various plots and subplots of the story that is embedded in the data before we proceed to build complex models. Data visualization is also a great way to tell a complex story to the audience in a way that is visually appealing and intuitive.  The course on data visualization will provide an understanding of the basic techniques of data visualization using R. Since most data are too noisy and messy for direct use, researchers have to have at their disposal easy and computationally efficient ways of cleaning and handling the data. Often researchers need to carry out several operations on the data or its subcomponents. Cleaning, massaging and transforming large data sets is a crucial skill. The course will also focus on these aspects using open source languages like R.

Learning outcomes

• Clean and manipulate large and varied data sets.
• Perform preliminary operations on data and its subcomponents before graduating to more complex analyses.
• Create data plots and subplots to understand the basic patterns/stories embedded in the data.
• Graduate to more complex models/narratives that have graphical/visual representations.
• Use tools for visualisation Appreciate the aesthetics of graphical/visual representation of data.
• Building statistical models
• Work with all of the data handling and visualization features of R and Tableau.

Textbook – Data Science with R

4) Chaos Theory and Complex Systems

5) Operational Risk

There are 4 electives on offer and students can choose any 2 electives out of the four

1) Computation finance

Contents

• Risks, returns and various ratios,
• portfolio optimizations and markowitz theory,
• Monte-carlo simulationsmethods in finance,
• investments strategies,
• demand and supply matching methods.

Learning outcomes

• How to use computers to better understand finance.
• How to make decision using analytic before investing.

2) Behavioural Finance

3) Fintech (Payments/Lending)

4) Fintech (Insurance/Wealth)

Data security workshop:

Cyber Ethics and Security: (14 – 16 Hours)

• Data preservation techniques.
• Digital security
• Private Laws
• Public Laws
• EU Law

Internship/Capstone Project (3 months)

Professional readiness program (throughout the course):

• Resume building
• Interview preparation
• Development of communication skills.

Assessment

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:

• Trimester examinations
• Periodic problem sets
• Writing assignments/projects/presentations
• Contribution to classroom discussion
• Classroom Attendance

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.