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
Introduction to AI

Instructor: Dr. Kanchi Gopinath


Fundamentals of Computational Thinking

This course is aimed to provide students with an understanding of the role that computational thinking plays in problem solving. They will be exposed to programming concepts and how to use them to design solutions using C language, starting from simple problems to interesting ones using ideas such as recursion and backtracking. In addition to learning key computing concepts and skills, the course will focus on laying necessary foundations in computational thinking going forward. For example, paradigms such as 'divide and conquer' and dynamic programming will be introduced through examples.


Instructors: Dr. Deepan M., Dr. Manoj Kannan


Design Thinking

Instructor: Prof. Amit Sheth


Engineering Math in Action

Instructors: Dr. Amrik Sen, Dr. Vivek Deulkar


Nature's Machines

Instructors: Dr. Prashanth Kumar, Dr. Monika Sharma, Dr. Navjot Kaur


Entangled Worlds: Technology and the Anthropocene (Part - 1)

What do we mean when we say Entangled Worlds? Entanglement as such implies a state of intertwining, of interpenetration, deep connectivity, interlocking and irreducible, fundamental interdependency and interrelationship. Although it would seem obvious that we live in a profoundly interconnected world in which both processes initiated by humans and non-human biological and non-biological entities continuously impact one another, our actions as organic, conscious and sentient beings do not reflect the obvious fact of interconnectedness.

Human beings by and large continue to operate as though their actions are isolated events that do not impact the rest of the world including other human beings, plant and animal species. Environmental degradation, climate change, species extinction, economic inequality, various forms of injustice, war and so on perhaps point towards a fundamental flaw in the way we think and act. In fact, we could claim the way we think and act is no longer commensurate with the kinds of immense global challenges we are facing. This seems to be the critical question of the Anthropocene.

How does one begin to return to a fundamental understanding of the embeddedness of all forms of life as well as so-called inorganic material in a web of interdependence, of inexorable entanglement? Is it even possible for us to engender a radical shift in the way we think about ourselves and planet Earth? Or have we already gone too far in our quest for creating a solely human-centric world?

In this course we will explore and reflect on the question of entanglements from a variety of transdisciplinary perspectives including those of art, music, imagination, biological systems, quantum mechanics, language, mathematics, design, thought, time, space, history, philosophy and technology. In particular, we will be experimenting with the idea of contrapuntal music as a model from which to think about ways of interconnecting different, independent melodies (read: concepts) in a dynamic, lattice-like structure through which a new emergent harmony is created. 


Instructors: Dr. Aditya Malik, Dr. Brainerd Prince, Dr. Saikat Chakraborty


Communication Lab - 1

Some of you might wonder about the value of spending so much time and effort on this writing course while having signed up for a professional degree – an engineering program. While this course covers foundational skills that are central to an academic writing course, these skills are also of critical value for any engineer or technology enthusiast. They will give you an edge over other engineering and technology students in terms of language and presentation. Reading skills are important tools, necessary to procuring relevant information and data on the latest technology and acquiring new knowledge in a rapidly evolving world of engineering and technology.

Finally, the writing skills you develop here will enable you to identify debates, put forth your viewpoints, and argue effectively in order to produce a clear and coherent academic paper. The writing skills are transferable and will enable you to argue for your case/point of view and empower the decisions you take in any professional situation. This course follows a transdisciplinary approach. The two main writing assignments are done in collaboration with two other first-semester courses – Nature's Machines and ILGC. 


Instructor: Dr. Brainerd Prince


Innovation Lab & Grand Challenge Studio - 1

Instructor: Dr. Malini Balakrishnan


Programming & Data Structures

Instructors: Dr. Sandeep Manjanna, Dr. Manoj Kannan


Mathematics of Uncertainty

Instructor: Dr. Amrik Sen


Foundations of Physical World

Instructors: Dr. Rudra Pratap, Dr. Dhiraj Sinha


Nature's Machines Lab

Instructors: Dr. Prashanth Kumar, Dr. Navjot Kaur, Dr. Saikat Chakraborty


Fundamentals of Microeconomics



Communication Lab

Instructor: Dr. Brainerd Prince


The Art of Thinking & Reasoning



Innovation Lab and Grand Challenge Studio - 2

Instructors: Dr. Rucha Joshi, Dr. Amit Sheth, Dr. Vishal Garg


Universal Human Values



Electronics Systems Engineering

The objective of the course is to train the students in the field of basic and applied electronics, which forms the backbone of the modern semiconductor and telecommunication industry. The course covers the fundamental and applied aspects of the subject aligned toward the design and development of novel electronic devices and systems. The course starts with an introduction to the broader field of electronics engineering and its relevance for other industry verticals against the framework of significant inventions and innovations. It will cover the essential aspects of circuit theory and evolves towards encompassing the operation of semiconductor devices which form the backbone of computational and communication systems.

A special focus of the course is on how simple devices and circuits get interconnected to form complex units which play a defining role in the operation of sophisticated gadgets. Towards the completion of the course, the students would be able to conceive and prototype new artifacts, systems, and gadgets, while using the foundation of analog and digital electronics. 


Instructors: Dr. Sanjay Kumar Bose, Dr. Dhiraj Sinha


The Philosophy and Foundations of Computing and AI

This course explores philosophical and foundational issues concerning computers, computing, and artificial intelligence. It addresses a range of fundamental questions, including: What is a computer? Could a computer be conscious? How could you test whether a computer is thinking? Are thinking and consciousness the same or different? Is the human brain a computer? Are there limits to what is computable?

The course also describes the work of Alan Turing, and his revolutionary ideas and legacy. While a graduate student, Turing invented the fundamental logical principles of the modern computer. He is responsible for the model of computability that underlies modern computer science—the universal Turing machine. The course investigates this important model and the scope and limits of the universal machine. It also includes an introduction to the early years of the computer revolution, covering the secret origins of electronic computers during World War II and the earliest work on artificial intelligence.


Instructor: Dr. Jack Copeland


Universal Human Values - II

Instructor: Dr. Shalini Sharma


Intelligent Machines

This course provides a comprehensive introduction to robotics and cyber-physical systems. Through hands-on lab activities, assignments, projects, as well as through guest lectures spanning research and practice the students learn about topics such as- sensors & actuators, system modeling, kinematics, dynamics, and controls, perception, planning, and navigation, cyber-physical systems, communication, and hardware. These are all the ingredients for designing intelligent machines. At the end of this course the students will be able to gain the skills to design, build and evaluate simple robotic and cyber-physical systems that will give them the confidence to pursue more complex projects in their future endeavors. 


Instructors: Dr. Sandeep Manjanna, Dr. Shashank Tamaskar


Innovation Lab & Grand Challenge Studio - 03

Instructor: Dr. Rucha Joshi


Communication Lab - 3

Instructors: Dr. Brainerd Prince, Dr. Sumita Ambasta


Intro to Data Science



Computational Methods & Optimization



Entangled Worlds: Technology & the Anthropocene (Part - 2)

What do we mean when we say Entangled Worlds? Entanglement as such implies a state of intertwining, of interpenetration, deep connectivity, interlocking and irreducible, fundamental interdependency and interrelationship. Although it would seem obvious that we live in a profoundly interconnected world in which both processes initiated by humans and non-human biological and non-biological entities continuously impact one another, our actions as organic, conscious and sentient beings do not reflect the obvious fact of interconnectedness.

Human beings by and large continue to operate as though their actions are isolated events that do not impact the rest of the world including other human beings, plant and animal species. Environmental degradation, climate change, species extinction, economic inequality, various forms of injustice, war and so on perhaps point towards a fundamental flaw in the way we think and act. In fact, we could claim the way we think and act is no longer commensurate with the kinds of immense global challenges we are facing. This seems to be the critical question of the Anthropocene.

How does one begin to return to a fundamental understanding of the embeddedness of all forms of life as well as so-called inorganic material in a web of interdependence, of inexorable entanglement? Is it even possible for us to engender a radical shift in the way we think about ourselves and planet Earth? Or have we already gone too far in our quest for creating a solely human-centric world?

In this course we will explore and reflect on the question of entanglements from a variety of transdisciplinary perspectives including those of art, music, imagination, biological systems, quantum mechanics, language, mathematics, design, thought, time, space, history, philosophy and technology. In particular, we will be experimenting with the idea of contrapuntal music as a model from which to think about ways of interconnecting different, independent melodies (read: concepts) in a dynamic, lattice-like structure through which a new emergent harmony is created. 


Instructors: Dr. Aditya Malik, Dr. Brainerd Prince, Dr. Saikat Chakraborty


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


Machine Learning and Pattern Recognition

This course deals with the design, analysis, and methodology of algorithms used to recognize patterns in real-world data of any sort (images, audio, videos, text, financial, speech, biosensing, medical, etc.). This is the foundational course in Artificial Intelligence which has transformed the world around us in innumerable ways such as online search (ChatGPT), voice recognition (“Hey Google!”), facial recognition applications (iPhone screen lock), and medical diagnosis (DeepMind). Today, Machine Learning is perhaps the most-vibrant area of engineering research and is witnessing the most lucrative careers due to its sheer number of applications in every other field of research. It is the one field which has truly become interdisciplinary because of its capability to be useful in analyzing data from other branches of knowledge, from physics to psychology, medicine to meteorology, and politics to philosophy.

Since Machine Learning and Pattern Recognition encompasses hundreds of algorithms and mathematical concepts, the goal of this course is not to give an overview of each one of them. Rather, it is to impart to students a strong fundamental background on these topics (such as feature clustering, dimensionality reduction, classification, and neural networks) with the ability to undertake real-world projects and build an end-to-end application. Overall, the students will gain a working knowledge of using these tools and algorithms and get a tangible idea of using them to solve real-world problems around them.


Instructor: Dr. Siddharth

Watch a video about this course here


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

Dec 5, 2023

Round 1 Deadline

Jan 15, 2024

Round 2 Deadline

Feb 28, 2024

Round 3 Deadline

Apr 30, 2024

Round 4 Deadline

Jun 17, 2024

Round 5 Deadline

*Round deadlines are subject to change.

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