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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 Business and Economics 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. Round 1 - Nov 24, 2021 to Jan 14, 2022
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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.
  • Semester 1
  • Semester 2
  • Semester 3
  • Semester 4
  • Semester 5
  • Semester 6
  • Semester 7
  • Semester 8
Innovation Lab & Grand Challenge Studio I

This course, referred to as ILGC, introduces the Grand Challenges at the interface of societal needs and technological capabilities. It will offer the opportunity for students to develop an interdisciplinary appreciation for engineering from a technical perspective as well as from a global and historical perspective. Students will embark on this integrated, project-based journey in the 1st semester and work on different projects throughout the four-year program. First semester topics include - Exposure to different Grand Challenges and project areas, design cycle, grand challenge thinking, geopolitics and global awareness, and entrepreneurial mindset.


Critical Thinking & Scientific Reasoning

What are the assumptions and beliefs that we have not examined in the modern age? How do we become aware of our implicit beliefs? What possibilities open up if we investigate and examine our presuppositions? How can we respond to the Grand Challenges of our time, if we don’t know how to think and reason critically? In this course, you will learn to meticulously develop the skill of thinking and enquiring critically, and being able to reason in a scientific, evidence-based manner. The course will focus on sharpening your intellectual abilities so that thinking critically and scientifically becomes a natural way of approaching the world. You will learn how to carefully analyze texts, structure arguments, develop technical reading and writing skills, and communicate your ideas in a coherent and logical manner to different audiences. Additionally, you will also learn to evaluate hypotheses and causal claims, observe and analyze data and patterns, construct reason-based arguments, and draw logical conclusions.


Engineering Mathematics in Action

In this course, students will be introduced to foundational aspects of engineering mathematics, specifically linear algebra, matrices, and ordinary differential equations. The course is divided into four modules, two modules each on linear algebra and ordinary differential equations. The topics included under the linear algebra modules are definition of vector spaces, concepts on linear independence of vectors, bases, rank of a matrix, solutions to linear systems, definition and interpretation of eigenvalue problems, and singular value decomposition and their applications. The topics under ordinary differential equations are methods to solve simple linear differential equations using both analytical (method of undetermined coefficients, variation of parameters) and numerical techniques (Runge-Kutta methods), basics of phase plane analysis, stability of solutions based on eigenvalue analysis, fixed points, and elementary concepts on bifurcation and chaos. Each module will comprise a computer-based laboratory project.


Fundamentals of Computational Thinking

This course provides students with an understanding of the role that computational thinking can play in solving problems. Students will be exposed to varied real-world problems and be taught how to approach them and design solutions using Python. In addition to learning key computing skills and concepts, the course will focus on how existing features in programming languages can be used to implement different concepts efficiently and how one can analyze different solutions. After taking the course students are expected to work on real-world projects that require building logical thinking processes that break down complex problems into smaller parts.


Foundations of Physical World

In this course, students will be introduced to classical mechanics, quantum mechanics, statistical mechanics, and connections to engineering thermodynamics. Molecular origin of macroscopic descriptions and constitutive relations for equilibrium and non-equilibrium behavior; fluctuations, kinetics, and limitations of macroscopic descriptions. Macroscale continuum origin of lumped models: ‘through’ and ‘across’ variables for analysis of electrical, mechanical, structural, thermal, acoustic, and fluidic systems.


Design Thinking

This course introduces students to harness the power of design thinking to develop innovative solutions to complex human-centered problems. The design thinking methodology will help students learn about the underlying context and the innovation need, brainstorming and developing prototypes, testing potential solutions, improving them, and developing new insights. Students will be exposed to core technology and design themes including principles, modes of thinking and analysis, and social and cultural aspects of design. They will learn how to use the ideation process to generate new ideas and select promising solutions, use prototyping tools to visualize and communicate ideas and develop the implementation plan for an effective solution.


Innovation Lab & Grand Challenge Studio II

Building on the ILGC journey from Semester 1, ILGC II will introduce students to starting their team projects, implementing principles of Design Thinking, embarking on a campus-wide project, and interacting with real-world stakeholders. Students will also learn how to work with various tools and build skills in machining, programming, instrumentation, among others.


Reimagining Technology & Society

In this course, we will rigorously enquire into the different meanings of the idea of technology and its relationship to society from the perspectives of philosophy, history, social anthropology, human evolution, and civilizational studies. We will look at examples from the past and present, but more importantly, also start imagining what the relationship between technology and society could be in the future. We will ask a number of questions pertinent to this inquiry: Does technology influence society, or, does society influence technology? What indeed is technology? Is technology perhaps first and foremost an ‘idea’? Or, is technology a particular way of knowing the world around us? If it is an idea then it must involve thinking of some kind. But clearly it also involves making something – usually an object that does something or has a particular function. In this course we will begin to understand technology from the standpoint of the threefold matrix of thinking, knowing, and making.


Mathematics of Uncertainty

This is an introductory level UG course on probability and statistics. Topics include conceptual introduction to probability axioms, conditional probability, Bayes' theorem, law of total probability and expectation, measures on central tendency and dispersion, probability distributions, simple discrete and continuous probability distribution models, elementary concepts on discrete and continuous time Markov processes, least squares regression analysis, sampling distributions and elementary ideas on hypothesis tests. Students will have to complete an end-of-term project on a topic of their choice.


Programming & Data Structures

Building on the Python course taught in the previous semester, students will be introduced to the Object Oriented Programming (OOP) paradigm and the associated benefits. They will learn to write structured and efficient programs in the OOP style. Besides testing and debugging programs, students will learn about common data structures, and where and how to apply them to solve computational problems.


Nature’s Machines

This course introduces students to how nature’s machines work. It covers various aspects that relate the design of living matter/things to engineering. We will discover how to think about: a) the human cell as a factory, the circulatory system as a transport network, musco-skeletal system as a structural network for load-bearing, magnetoreception in animals as a communication system, among other topics. b) Aspects of biomedicine such as the design of impedimetric sensors, digital health, and tissue and genetic engineering. c) Diversity and dynamics of nature pertinent to engineering through several demo examples and recent state-of-the-art applications.


Fundamentals of Microeconomics

The course introduces core microeconomic models of consumers, firms, and markets, and develops their application to a broad range of economic and social issues in the real world. It will cover concepts of equilibrium, markets and competition, market demand and market supply, behavior of consumers and producers, consumer and producer theory, pricing, tax incidence, making choices under uncertainty, economic efficiency, etc., in the context of contemporary real-world applications around us such as Uber surge pricing, telecom price wars, e-commerce models, recent ed-tech acquisitions, etc.


Innovation Lab & Grand Challenge Studio III

In this course, students continue to build on their IL/GC projects, with an eye for expansion at the city level. They will be able to develop multidisciplinary approaches and interdisciplinary perspectives by interacting with and making connections between disciplines; analyzing the humanistic, social, historical, economic, and technical contexts of problems. In addition, they will continue to iterate their designs and work on skills such as developing innovation and entrepreneurial mindset, becoming a better communicator and leader, and understanding the social and human consequences of actions and responsibilities to others in local, national, and global communities.


The Ethics of Technological Innovation

The advent of technology since the turn of the century has led to many advancements in the way that humans live and operate. This progress, however, also comes with apprehensions, uncertainty, and questions regarding the ethical considerations associated with the owners, regulators, and users of the technology. In this course, we will examine the manifold ethical issues surrounding the use and development of AI, Robotics, Biology, and Business based technologies in the contemporary world. Students will be introduced to ideas of safety, privacy, regulations, and related consequences of bias, manipulation, and fairness, from a multidisciplinary perspective. Students will be introduced to ideas from traditional philosophical ethics texts before delving into real world case studies focussing on contemporary issues.


Mathematics for Continuous Systems

This course will introduce foundational ideas on mathematical transforms like Laplace and Fourier transforms. Additionally, there will be a two month long module on partial differential equations and vector calculus. In addition to lectures, students will participate in a semester long, state of the art physical laboratory immersion program which will involve training and tinkering with experimental mathematics, such as profiling heat conduction through a metallic rod to understand the mechanics of the heat equation and its solutions, performing fluid mechanics experiments using a rotating tank of water and high-speed camera to collect data and understand the solution states of the Navier Stokes equation, using wavemaker and wave guides to calculate wave velocity and comparing the data with theoretical calculations, performing laboratory experiments with spectrometers to estimate power spectral density of signals and compare the results with mathematical calculations in Fourier space, etc.


Data Science & Artificial Intelligence

This course offers an introduction to the areas of Data Science, Artificial Intelligence, and Machine Learning (DS/AI/ML), combining philosophical, biological, and psychological perspectives with computational design concerns. Students will begin the course by understanding the principles underlying learning and how it translates to various applications and categories of AI. Besides multiple classes of models, students will be introduced to fundamental concepts in data science that will help them identify, compare and implement solutions to problems. Internal critiques and external perspectives of AI solutions will also be discussed to ensure students obtain a holistic view of the challenges and opportunities in this field.


Intelligent Machines

This course introduces the students to the idea of intelligence and how intelligent machines are transforming the world around us. Students will learn about the concept of cyber-physical systems and how intelligence stems from the ‘perception, reasoning, and action loop’. Students will explore several examples of cyber-physical systems in the real world in areas such as robotics, smart grids, and autonomous cars. They will learn about mathematical techniques for modeling cyber-physical systems, how to build intelligent machines by combining sensors, actuators, and embedded devices. Through hands-on lab activities, assignments, projects, as well as through guest lectures spanning research and practice, students will be able to gain the skills to design, build and evaluate simple cyber-physical systems that will give them the confidence to pursue more complex projects in their future endeavors.


Foundations of Optimization

This course offers a multidisciplinary overview of the field of optimization. Students will learn how to formulate an optimization problem, apply different optimization techniques, translate problems into Python and Matlab code, and identify conditions under which each one works best. Students will be introduced to numerical, constrained, unconstrained, univariate and multivariate optimization methods and techniques, while also being introduced to topics such as Pareto optimality, multiobjective and global optimization. The final project will allow students to apply their knowledge to real-world engineering and business problems.


Innovation Lab & Grand Challenge Studio IV

In Semester 4 students will decide the IL/GC project (coincides with choosing their major) and team they will be part of, and initiate the first steps of the project that will culminate with their capstone. Projects chosen by students will be connected and coherent in design, looping in a wide range of technology such as sensors, IoT, automation, robots, AI/ML, data science, biosystems design, etc. The core focus would be on implementing the engineering design cycle and reflecting on progress, to create solutions implementable at a city level, with an eye for expansion at state level. Mentored Leadership and Professional Development opportunities will be a constant feature across the 4 year IL/GC 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.


Design & Analysis of Algorithms

Modern computers of all scales rely on efficient algorithms for solving diverse sets of computing problems. Efficient data structures are the backbone of efficient algorithms, guiding the scheme of storage and retrieval of data for speedy and effective processing. This course enables students to recognise the best data structures suited for a given computing problem, design algorithms based on them, and prove bounds on time and space complexity of their algorithms. As part of the course work, the students will also study the implementation and complexity of fundamental algorithms such as max flow in networks, discrete fourier transform, RSA cryptosystem, and real-world systems such as google search engine, and social network graphs.


Machine Learning & Neural Networks

The advent of deep learning and its application to big data analysis has worked to transform our industries. This course is meant to give students a detailed introduction to the science of deep learning. We undertake a study of various neural network architectures adapted to different problem settings, their training parameters, and algorithms for optimal network performance. We organise the discussion around existing learning paradigms and the specificities of data that guide the network design (via inductive biases). We anchor the study around seminal papers that have, and that continue, to shape the field of deep learning, to give students a flavour for the dynamic nature of the field. As part of the lab work, students will be expected to apply these concepts to real world case studies requiring ML tasks such as scene recognition, speech recognition, image segmentation, etc.


Foundations of Data Science

This course will provide students with the tools to use statistical techniques and computational methods to work with data, analyze large datasets, gain insights for specialized problems and ultimately extract meaningful outcomes. Through project work, students will learn all aspects of the data analysis cycle - ranging from what questions to ask, how to devise a data collection strategy, data acquisition and cleaning, understanding data set variables and the relationships between them, data management and storing, processing and analysis of data, summarizing the main characteristics of the dataset, how to discover patterns and spot anomalies, and how to eventually communicate using data.


Fundamentals of Macroeconomics

The course offers practical tools and multiple case studies to understand and analyse the macroeconomy. It will cover issues on measurement of macroeconomic variables, and analysis of the macroeconomy in the short-run and the long-run. Topics in short-run macroeconomics include the monetary system, how central banks set interest rates, the IS curve, the Phillips curve, aggregate demand and supply framework, analysis of the evolution of output, inflation, and interest rates in response to economic shocks and changes in policy. Key models in long-run macroeconomics include the Solow growth model and growth models with ideas. The course will also introduce state-of-the-art DSGE business cycle modelling, dynamic and stochastic dynamic optimization techniques, and applications of AI/ML to growth theory. Real-world applications will include analysis of the great recession and the Covid-19 recession, the European debt crisis, China’s impact on US jobs and wage inequality, income per capita disparities across countries, etc.


Application Domain Track I

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 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 IL/GC 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 IL/GC 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.

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Dates to Remember

Jan 14, 202210

Round 1 Deadline

March 30, 2022

Round 2 Deadline

May 31, 2022

Round 3 Deadline

July 20, 2022

Round 4 Deadline

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