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UG Programs | 4 years

B.Tech in Data Science, Economics & Business

This cutting-edge interdisciplinary degree is at the nexus of data science and understanding of humans, economy, businesses and government. The Data Science, Economics & Business program has a strong core in data science, which is blended with a study of how humans think, behave and make decisions, how our society works and how global economic and financial markets operate. Students will be encouraged to explore the real-world applications that emerge at these intersections to create solutions and value for the economy and society.

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8 Semester Curriculum The curriculum at Plaksha is dynamic and continuously evolving, based on inputs from faculty, latest research and industry insights. B.Tech in Data Science, Economics & Business syllabus outline is given below.
  • Semester 1
  • Semester 2
  • Semester 3
  • Semester 4
  • Semester 5
  • Semester 6
  • Semester 7
  • Semester 8
Computational Thinking

Instructor(s) - Dr. K Gopinath, Dr. Manoj Kannan


Coding Café (Python & LINUX)

Instructor(s) - Dr.Srinivasan Vishwanathan


Engineering Math in Action

Engines of Life

Environmental Science

Instructor(s) - Dr. Prashanth Suresh Kumar


The Art of Thinking and Reasoning

Universal Human Values

-


Communication Lab

Instructor(s) - Dr. Brainerd Prince


ILGC: Design and Innovation

Instructor(s) - Dr. Amit Sheth, Dr. Rucha Joshi


Programming & Data Structures

Instructor(s) - Dr. K Gopinath, Dr. Manoj Kannan


Mathematics of Uncertainty

Instructor(s) - Dr. Amrik Sen


Foundations of Physical World

Nature's Machines

Instructor(s) - Dr. Monika Sharma


Fundamentals of Microeconomics

Instructor(s) - Dr. Kriti Khanna


Reimagining Technology and Society

Communication Lab

Instructor(s) - Dr. Brainerd Prince


Yoga & Sports

-


Innovation Lab & Grand Challenge (ILGC-2)

Instructor(s) - Dr. Rucha Joshi


Data Science and Artificial Intelligence

Intelligent Machines

Optimization

-


Calculus of higher dimensions

Instructor(s) - Dr. Nitin Upadhyaya


Foundations of Physical World 2

Instructor(s) - Dr.Dhiraj Sinha


Ethics of Tech Innovation

Indian Constitution

Instructor(s) - Dr. Amit Sheth


Innovation Lab & Grand Challenge (ILGC-3)

Instructor(s) - Dr. Rucha Joshi


Optimization

Instructor(s) - Dr. Nitin Upadhyaya


Communication Lab

Instructor(s) - Dr. Brainerd Prince


Fundamentals of Macroeconomics

Instructor(s) - Dr. Kriti Khanna


Design and Analysis of Algorithms

-


Introduction to Data Mining & Pattern Recognition

Instructor(s) - Dr. Srikant Srinivasan


Machine Learning

Instructor(s) - Dr. Saumya JetleyDr. Subhasis Ray


Econometrics

-


ILGC-4

Instructor(s) - Dr. Rucha Joshi


Innovation Lab & Grand Challenge Studio V

Continuing their project progress from semester 4, the goal for Semester 5 and 6 will be to implement solutions via projects at the State level, with an eye for expansion at the National level. To achieve this, students will seek validation of concept from various stakeholders, complete the engineering design cycle of their project, while also developing an entrepreneurial spirit from their experiences. Mentored Leadership and Professional Development opportunities will be a constant feature across the 4 year ILGC experience, and will be integrated with project work. These serve to develop the student’s professional skills and also help in creating a more integrated socio-integrated understanding of engineering/design.


Data Analytics & Visualization

In this course, students will learn how data analytics is used in industries ranging from healthcare, e-commerce, sports, social media, etc. by working with a variety of real-world datasets and using concepts of modern algorithmic data analytics. The course will focus on learning methods that are required to analyze large datasets to be able to identify patterns and insights. Students will learn this process through both statistical data analysis tools as well as using computational and algorithmic resources. This course also includes modules that will help students learn mathematical principles, techniques, and applications of data visualization and design, and graphic techniques that are required to create novel and interactive visualizations. The course will culminate with student-led projects that will require the use of analytics, machine learning and visualization on contemporary datasets.


Game Theory & Mechanism Design

This course introduces mathematical tools to model and analyse situations of interactive decision making. It will introduce concepts of ordinal and cardinal games, solution methods to solving static and dynamic games of complete and incomplete information, social choice theory and voting, mechanism design, auctions, etc. Game theory has found applications in various fields apart from economics such as biology, business, law, politics, sociology, and computer science. Real-life applications and case studies to be discussed include - card games, war strategies, understanding firm behaviour such as price-fixing, collusion, and marketing strategies; voting patterns, restaurant locations, etc.


Financial Accounting & Analysis

This course introduces students to the core principles of Management Accounting and Cost Accounting. Students will learn basic accounting concepts of assets, liabilities, double entry system, credits, debits, ledgers and trial balance, and other such topics. Students learn to analyze and interpret financial statements (BS, P&L, Cash flow statements) and key financial ratios, in addition to understanding different costs and costing approaches, cost-volume-profit analysis, and using the analysis to make organizational decisions.


Econometrics

This course will introduce students to basic econometric concepts and methods necessary for conducting empirical analysis. Core topics include simple and multiple regression analysis, hypothesis testing, classical model assumptions, omitted variables, serial correlation, heteroskedasticity, incorrect functional form, and multicollinearity. The course will also highlight the concept of endogeneity and introduce different methods of causal inference such as - randomized experiments, fixed effects and difference-in-differences, and instrumental variables. Topics on time-series analysis will include distributed lag models, dynamic models, forecasting and ARIMA models, etc. Significant part of the course will be devoted to practical application of the course material through hands-on experience using STATA/R.


Application Domain Track II

The Application Domain Tracks are a series of 1 credit modules that help students inculcate skills and mindsets related to research and entrepreneurship. Through these tracks, students will contribute to ongoing research projects in Plaksha's flagship grand challenge research centers, and may work with faculty on their research or on approved external projects in industry/government or startups. Across semesters, students will have the option to work across different disciplinary areas or focus on one area but the purpose is for them to appreciate the relevance of their coursework to a variety of challenges and areas.


Innovation Lab & Grand Challenge Studio VI

Continuing their project progress from semester 4, the goal for Semester 5 and 6 will be to implement solutions via projects at the State level, with an eye for expansion at the National level. To achieve this, students will seek validation of concept from various stakeholders, complete the engineering design cycle of their project, while also developing an entrepreneurial spirit from their experiences. Mentored Leadership and Professional Development opportunities will be a constant feature across the 4 year ILGC experience, and will be integrated with project work. These serve to develop the student’s professional skills and also help in creating a more integrated socio-integrated understanding of engineering/design.


Reinforcement Learning

Reinforcement learning is integral to help us realize the full potential of artificial intelligence. This course introduces students to the domain of reinforcement learning, and how it is applied in areas ranging from autonomous vehicles to advertising. Students will learn the basic principles of reinforcement learning, how to use deep RL algorithms and how to apply deep RL methods to contemporary problems. Given the dynamic nature of the discipline, class discussions will revolve around modern-day applications and innovations in reinforcement learning and challenges posed by it.


Operations & Supply Chain Analytics

This course focuses on the applications of data analytics methods in the field of operations and supply chain management covering areas such as logistics, inventory management and forecasting, operations planning, sales and purchase management, etc. Students will learn to apply data analytics methods and tools, and create models that lead to efficient and effective industry decisions. By the end of the course students will be able to apply analytical methods to topics such as forecasting demand, supply chain risk management, distribution economics, product development, and all stages of the supply chain cycle.


Industrial Organization

This course is about studying market power and competition, strategic interaction between firms and decision-making by firms, institutions, and industrial policy. It will explore some interesting topics such as pricing strategies, branding strategies, product bundling, asymmetric information, horizontal mergers and vertically integrated markets, antitrust, networks, intellectual property, entry deterrence, etc. The course will introduce multiple economic and game-theoretic models, numerous real life examples and case studies. Some examples of real-life applications that will be discussed include - Airbus vs Boeing and the market for aircrafts, selling used products over Ebay, adverse selection in second-hand car markets, patents in the pharmaceutical sector, reputation on amazon marketplace, expensive popcorn cost at the movies, etc.


Technical Elective I

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment


Application Domain Track III

The Application Domain Tracks are a series of 1 credit modules that help students inculcate skills and mindsets related to research and entrepreneurship. Through these tracks, students will contribute to ongoing research projects in Plaksha's flagship grand challenge research centers, and may work with faculty on their research or on approved external projects in industry/government or startups. Across semesters, students will have the option to work across different disciplinary areas or focus on one area but the purpose is for them to appreciate the relevance of their coursework to a variety of challenges and areas.


Economics of Networks

Networks are everywhere in society - be it social media platforms like Facebook and Twitter, our network of friends and colleagues, or the complex trading networks that underlie financial markets and supply chains, etc. These networks have a major impact on the economy, in multiple ways. This course will introduce a variety of tools relevant in studying the economics of networks including - graph theory, optimization, and game theory; and cover a wide variety of applications including - financial and trading networks, formation of social groups and racial segregation, information networks and the world wide web, spread and control of epidemics, voting behaviour, etc.


Marketing Analytics

This course introduces students to the principles of marketing and how the field has been transformed by the advent of data. Students will learn the fundamentals of marketing analytics, including data modelling and web analytics. Through practical exercises and real-world case studies, students will learn how to leverage data to be able to draw key insights, understand consumer preferences and trends, how analytics can boost marketing and business outcomes, and how data can be used to create marketing strategies and campaigns. Key topics include demand forecasting, market segmentation, revenue forecasting, customer lifetime value, product entry, pricing mechanisms, marketing return on investments, among others.


Technical Elective II

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment


Humanities & Social Science Elective I

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment


Innovation Lab & Grand Challenge Studio Capstone

ILGC transforms and culminates as a two semester capstone design project. By the end of the seventh semester a detailed design of the final product (this could be a device, system, process, software, etc. that results from this design experience) needs to be completed. This includes but not limited to the following: Description of the overall project, including a description of the customer and their requirements, the purpose, specifications, and a summary of the approach. Description of the different design approaches considered and evaluation of each design approach. Detailed description of the final proposed design.


Corporate Finance and Banking

This course focuses on theoretical and institutional aspects of banking and corporate finance. It will explain the role of financial intermediaries in the economy and point out the structural weaknesses of the banking sector (exposure to runs and panics, persistence of rationing on the credit market, recurrent solvency problems) that may justify public intervention. Topics to be covered include functions of financial intermediaries, market structures in the banking industry, lender-borrower relationship, credit market equilibrium and its macroeconomic implications, bank runs and systemic risk, managing risks (including credit risk, liquidity risk, interest rate risk, market risk, etc.) , regulation of banks and its economic justifications, etc.


Technical Elective III

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment


Technical Elective IV

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment


Humanities & Social Science Elective II

Sample electives include: AI for Social Good, Technology, Policy and Law, Decision Making Under Uncertainty, Fairness, Transparency, Accountability, and Ethics in Data Science


Innovation Lab & Grand Challenge Studio Capstone

ILGC transforms and culminates as a two semester capstone design project. By the end of the eighth semester, students will have a working product (this could be a device, system, process, software, etc. that results from this design experience). Therefore, the focus of this semester is to implement, test and evaluate the design approach chosen in your first semester. The following are the expected requirements and deliverables for this semester: Working final product Testing and evaluation of product design Demo of the final product Completed Project Description, Final Reflection and Completed Outcomes Matrix


Learning Experiences

Experiential Learning

Integrated learning experience across 4 years. Students work on authentic, real world projects through industry and community engagement or by research with faculty.
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By having access to state-of-the-art makerspaces and coding cafes and incorporating them in the curriculum, students will become more context-aware, develop critical thinking abilities, and learn by creating. This will help foster a tinkering and problem solving mindset, immersing students in experiential learning from day one. These areas will be open to students to explore, create, prototype and design, while also housing equipment and technologies like 3D printers, sensors, etc.
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The core curriculum will not just be limited to engineering and sciences, but bring in exposure to entrepreneurship and design which will enable humane and empathetic outcomes through technology. Each student will undertake multiple different experiences to develop skills like finding opportunities, creating value, and embracing risks. Students will be mentored and supported by Plaksha founders and professionals from industry.
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At Plaksha, learning and skill development do not stop in the classroom. Students will have the opportunity to create and immerse themselves in pursuing their academic and creative interests. Student led clubs will be autonomous bodies that operate under the purview of the Office of Student Life. Being the founding batch, students will be encouraged to help establish a vibrant culture through clubs and societies on campus.

Hear about the course from the experts

Top academicians and faculty talk about this B.Tech major and application of data science in business and economics.

Find the answers to your questions in some of our frequently asked questions by students
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Dates to Remember

Nov 23, 2022

Admission Starts

Jan 13, 2023

Round 1 Deadline

Apr 14, 2023

Round 2 Deadline

June 2, 2023

Round 3 Deadline

Jul 28, 2023

Round 4 Deadline

*Round deadlines are subject to change.

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