Curriculum

This course provides a comprehensive introduction to multivariable calculus and ordinary differential equations through the lens of mathematical modeling and computation. The course begins with the mathematical representation of curves and surfaces using Cartesian, cylindrical, spherical, and parametric forms, followed by the study of smoothness, continuity, differentiability, gradients, directional derivatives, and geometric properties of surfaces. It then develops techniques for measuring geometric and physical quantities, including curve length, surface area, volume, mass, and averages using single, double, and triple integrals.

The course concludes with analytical and numerical methods for solving ordinary differential equations and systems of differential equations arising from real-world applications. Throughout the course, Python-based computational tools are integrated to visualize mathematical objects, perform symbolic and numerical computations, and explore applications in science and engineering.

People move through life accepting what authority figures, books, or media present as true. Educational systems rarely teach rigorous questioning of the knowledge on offer, and unexamined assumptions quietly shape what one perceives, thinks, and does. Five hundred years ago, Europeans believed the earth was flat; for common people and scientists alike, this was not a belief to be questioned but an accepted fact. The history of knowledge is full of such constraints.

This course develops the skill of critical and scientific thinking: examining presuppositions, reasoning from evidence, and building habits of rigorous inquiry. Students learn to identify implicit beliefs, investigate assumptions, and respond to complex problems with greater clarity and intellectual honesty.

Coding Café is a hands-on introductory Python programming course. Students write programs from the ground up, focusing on logic, control flow, data structures, and algorithmic thinking with minimal theoretical overhead. The course prioritizes clarity in problem formulation, code readability, and iterative problem-solving over rote syntax, preparing students for advanced computing courses. Experimentation and ‘fail fast’ iteration are encouraged over memorization; clarity of thinking matters more than syntax perfection.

This course emphasizes experimentation, play, and iteration over memorization. Students are encouraged to explore and fail fast during labs; clarity of thinking matters more than syntax perfection.

Fundamentals of Computational Thinking is an introductory course for first-semester undergraduate students at Plaksha. It introduces programming and builds foundations in computational thinking and problem solving for advanced work in algorithms and programming, as well as for students pursuing other majors.

Students learn fundamental programming approaches, including conditional branching, iteration, recursion, and exhaustive search, and use them to solve problems. The course uses C and develops programming logic through illustrative problems such as checking whether a string is a palindrome.

This course provides an introduction to computational methods for solving optimization problems. Students develop the vector calculus foundations needed for optimization, explore applications in operations research and engineering, and learn linear programming and constrained optimization techniques. Laboratory work gives students practice applying these methods through projects.

This course introduces students to data science and artificial intelligence. Students work with different types of data, form hypotheses and regression models, learn the foundations of intelligent systems, and study techniques for building them. The course also surveys AI areas such as natural language processing and robotics.

Taking a design-centered approach to solving problems and developing technology-driven ideas in entrepreneurial settings, this course introduces core design thinking principles and their application to the creation and implementation of solutions. Tools are provided to navigate each stage of the design thinking process. Through project-based learning and real-world case studies, students gain practical insight into how the methodology fosters innovation and user-centric thinking.

This course introduces the fundamental principles of statics, electrical circuits, and mechatronics that form the foundation of modern engineering systems. Students will develop analytical and practical skills for modelling, analyzing, and designing systems that integrate mechanical and electrical components.

• Statics topics:  Adding vectors, components, unit vectors, dot product, cross product with drawings, components and computers; Free-Body Diagrams, force and moment balance for a particle, a rigid object, trusses, mechanisms and frames.
• Circuits topics: Voltage, current, Kirchhoff’s laws, analysis of resistor circuits, Wheatstone bridge, Thevenin and Norton Equivalent circuits, diodes, op amps, instrumentation amps,  simple sensors.
• Mechatronic topics:  Magnets and electromagnets, motors, commutation, strain gauges, force and distance measurement, transmissions, energy conservation and energy loss.

In the Fundamentals of Economics course, we develop models of how households make consumption decisions and then aggregate those results to the market level. We then turn to the supply side of markets, engaging in a detailed investigation of how firms make production decisions. Next, we combine demand and supply to understand how prices of goods are determined in perfectly and imperfectly competitive markets. The course will take a closer look at economic notions of efficiency, and the ever-present tradeoff between efficiency and equity. We will also take time to consider fundamentals of utility, game theory, and market failures – such as externalities (e.g. air pollution). This is important for understanding how demand and supply is derived for various goods and services and how changes in our environment (man-made or natural), can impact the magnitude and distribution of well-being.

In addition to understanding analytical models in Microeconomics, we will turn to Fundamentals of Macroeconomics. This is the study of the economy, focusing on large-scale economic variables such as national income, overall output, unemployment, inflation, and economic growth. It examines how aggregate demand and supply interact to determine the performance of an economy, and how fiscal and monetary policies influence these dynamics. Studying macroeconomics is vital for making sense of real-world issues like financial crises, inflationary pressures, and global trade dynamics. Topics in macroeconomics that will be covered include measuring the national income and cost of living, unemployment, the open-economy model, and the trade-offs between inflation and unemployment.

The broad goal of The Innovation Lab and Grand Challenges (ILGC) course is to enable students to observe, relate to, and solve societal challenges in a sustainable manner using the technical and engineering skills they will learn concurrently in other courses. These challenges may be drawn from their surroundings, including

• the campus where they will live for the next four years,
• the place they belong to: their city, town, village, state, or country, or
• global challenges identified through competitions and other opportunities they may aspire to pursue.

The course explores how simple observations can lead to ideas, solutions and ultimately products that address real-world problems. Through these success stories, students engage in discussions on empathy, observation, problem-solving techniques, lateral thinking and teamwork. The course also acts as a catalyst, encouraging students to identify and address similar opportunities and challenges in their own surroundings.

Building on the empathy, observation, lateral thinking, and teamwork developed in ILGC-I, this course focuses on breaking down complex societal challenges into specific, actionable problems, and identifying context-appropriate solutions. Students begin by examining campus-based gaps and proposing executable solutions, using tools such as gap analysis, root cause analysis, semi-structured interviews, and focused group discussions to gather primary data on underlying issues. A four-week summer community attachment follows, where students engage in hands-on work to identify real-world grand challenges across diverse contexts. Through the semester, students work both individually and in teams, culminating in an exhibition that presents the observed challenge, its root causes, and proposed solution(s).

Building on the foundational knowledge and exploratory skills from previous semesters, this course engages students with the complexities of grand challenges. Students brainstorm solutions to identified problems and evaluate their social and financial viability. The course emphasizes problem framing, perspective taking, negotiation, social and financial viability checks, and the application of learned concepts through active class discussion.

Designed for first-year undergraduate engineering students, this course develops foundational communication competencies through structured practice in reflection, presentations, conversations, and teamwork. It introduces communication as an essential engineering capability, enabling students to think critically, communicate clearly, collaborate effectively, and reflect on their own growth from the outset of their university journey.

Engineering graduates consistently face a gap between technical ability and communication skills, one that limits their capacity to collaborate and lead. This course addresses that gap by building cognitive, linguistic, and foundational communication skills, such as speaking, listening, reading, and writing, that are rarely part of a standard engineering curriculum.

Reading skills developed in the course equip students to locate relevant information, engage with current developments, and acquire new knowledge in a rapidly evolving field. Writing skills enable students to identify debates, articulate viewpoints, and produce clear, coherent academic papers; competencies that transfer directly to professional contexts. The course follows a transdisciplinary approach; writing assignments are completed in collaboration with other courses.

This course introduces robotics and cyber-physical systems through hands-on lab activities, assignments, projects, and guest lectures spanning research and practice. Topics include sensors and actuators, system modelling, kinematics, dynamics and controls, perception, planning and navigation, cyber-physical systems, communication, and hardware. Students graduate from the course able to design, build, and evaluate simple robotic and cyber-physical systems.

This comprehensive course on Probability and Statistics integrates theory with hands-on Python programming. Topics cover descriptive methods, probability concepts, random variables, statistical distributions, joint densities, sampling methods, and the Central Limit Theorem. Advanced modules address hypothesis testing, p-values, and statistical inference. Lab sessions build practical skills in data visualization, simulation, and implementation of probability rules. Students leave equipped to model uncertainty and draw informed conclusions from data in diverse contexts.

Students learn to build devices that use sensors and actuators controlled by a microprocessor. The course uses the Arduino "ecosystem" with a Raspberry Pi Pico computer, paired with sensors, lights, and motors, enabling students to build machines that can sense, think, and act.

This course offers an exploration of the remarkable machinery shaped by nature, spanning from human organ systems down to the intricate world of cells and genetic material. The course consists of four modules: Human Physiology, Fundamental Biology, Immunology, and The Science and Art of Biomimicry. In the Human Physiology module, learners will gain insight into the functionality of different organ systems and their regulation, essential for maintaining optimal bodily function. Moving on to the Fundamental Biology module, we take a deep dive into the mesmerizing micro and nano machinery present within cells and biomolecules. In the module on Immunology, we explore the intricate defence mechanisms that safeguard the human body. Finally, in the module on Biomimicry, we engage in captivating discussions about real-world design and engineering solutions, all inspired by natural designs. By the end of the course, the students will have a comprehensive understanding on the different mechanisms by which natural systems operate and will be able to connect these concepts and relate them to real world applications.

Building on Computational Thinking, this course develops students' ability to apply Object-Oriented Programming (OOP) principles through problem-solving using common data structures and algorithms. Topics covered include:

• Principles and application of OOP.
• Program complexity, recursion, and proofs by induction.
• Common data structures and algorithms, and their applications.
• Comparative analysis of data structures and their optimality.

What is technology? An instrument, a phone, a rocket, an electric car? An idea? A particular way of knowing the world? And what is its relationship to society: does technology shape society, or does society shape technology, or does that framing itself need to be rethought?

This course rigorously enquires these questions through the perspectives of philosophy, history, social anthropology, human evolution, and civilizational studies. Drawing on examples from the past and present, it asks what the relationship between technology and society could look like in the future. Central to the course is understanding technology through a threefold matrix of thinking, knowing and making.

 Artificial Intelligence (AI) is transforming every aspect of human life and raising urgent questions about productivity, creativity, employment and sustainability. This course introduces AI to engineering students with no prior experience in the field. It traces the historical evolution of AI, its applications across daily life and industry, and the ethical and environmental implications of its use. Students leave with a grounded understanding of AI terminology, its diverse applications, and the societal responsibilities that come with building and deploying these technologies.

Campus life brings new experiences alongside uncertainty and challenge. Universal Human Values II is designed to help students make sense of both. Over 15 interactive and reflective sessions, students examine the people, habits, and attitudes that have shaped them, develop skills in emotion regulation, and build healthy ways to cope and move forward.

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Microeconomics examines how individuals, households, and firms make decisions under resource scarcity. This math-intensive course develops the tools of modern microeconomic theory and their applications, offering a rigorous framework for understanding individual and firm decision-making in pursuit of utility and profit maximization.

The course begins with consumer behaviour and decision-making, then shifts to firms and optimal production decisions. It proceeds to competitive equilibrium and the associated welfare theorems, followed by individual decision-making under risk and uncertainty, with emphasis on expected utility theory, risk preferences, and market mechanisms such as insurance. The final section examines market failures arising from information asymmetry, externalities, and public goods.

This course covers fundamental concepts in probability, statistical theory, and methodology. Topics include probability distributions, multivariate normal distributions, transformations, sampling distributions, principles of inference (including Bayesian inference), maximum likelihood estimation, goodness-of-fit tests, likelihood ratio, significance testing, linear models and least squares, generalized linear models, model selection, and nonparametric density estimation. Python is used for data-analytic applications.

A core component of the DSEB curriculum, this course bridges economic theory, data science techniques, and real-world business strategy. Students move through the full analytics lifecycle: from framing complex business challenges to delivering data-backed, actionable recommendations. The course opens with descriptive and diagnostic techniques in Excel to understand past performance, then transitions to Stata for building and interpreting predictive models to forecast future outcomes. The final module covers data storytelling using Advanced Excel and Tableau to communicate analytical findings to executive audiences. This is an applied course in solving business problems with data, not a theoretical one. Class sessions combine lectures with live tool demonstrations; assignments provide hands-on practice.

This course provides a rigorous introduction to the principles of corporate finance. It focuses on the fundamental tools and analytical frameworks used to make financial decisions within firms. Students will learn how to value financial assets, evaluate investment projects, understand the relationship between risk and return, and analyze financing and payout policies. The course emphasizes the core building blocks of modern finance, including time value of money, discounted cash flow valuation, portfolio theory, the capital asset pricing model (CAPM), cost of capital, capital structure theory, and payout policy. It also covers advanced valuation techniques that integrate these concepts. In addition to theoretical concepts, the course incorporates hands-on applications using financial data. Students will gain experience with data science techniques and its applications in finance. Through problem sets, case discussions, and applied exercises, students will develop a strong understanding of how financial managers create value, make capital budgeting decisions, choose optimal financing structures, and return capital to shareholders.

Econometrics uses economic theory, mathematics, and statistical inference to quantify economic phenomena, converting qualitative statements (such as "the relationship between two variables is positive") into quantitative ones (such as "an increase in income by Rs 100 increases consumption by Rs 90"). Essential to the social sciences, public policy evaluation, and business practice, econometrics is particularly concerned with untangling cause and effect: inferring that one variable (e.g., education) influences another (e.g., worker productivity), all else equal.

The first half of the course builds foundations in econometric theory, covering bivariate and multivariate regressions with both continuous and dummy variables, estimation using OLS, and the challenges and limitations of these methods. The second half turns to causal inference, introducing instrumental variables, difference-in-differences, and regression discontinuity. Classic research papers in economics that employ these methods are discussed throughout.

Examining how firms compete, collude, innovate, and grow in real-world markets, this course analyzes how these strategic choices shape consumer welfare and public policy. Using the tools of microeconomics and industrial organization, students assess firm behaviour across market structures ranging from competitive industries to monopolies and oligopolies.

A defining feature is the case-driven approach. Theoretical models are applied to high-profile, policy-relevant cases including airline price wars and mergers (IndiGo, American-US Airways), cartel behaviour and tacit collusion (De Beers, GE-Westinghouse), controversial pricing practices (Spirit Airlines, the Pink Tax), innovation and dominance in technology markets (Microsoft, Apple Watch), and network-driven platforms in emerging economies (IndiaMART).

Organized into four parts, the course opens with foundational tools for understanding consumer demand, firm pricing, efficiency, and market failure. The second part covers strategic interaction under oligopoly, introducing game theory and models of competition, collusion, and price wars. The third examines how industry structure evolves through entry barriers, mergers, foreclosure, and government intervention. The final part explores nonprice strategies (vertical integration, advertising, innovation, and networks) central to competition in digital and platform-based markets.

Instruction combines flipped-classroom discussions, data-driven group assignments, and applied case analysis.

Mathematical frameworks and real-world applications anchor this introduction to broad movements in the global economy. Key topics include long-run economic growth, technological change, booms and recessions, inflation, interest rates, monetary and fiscal policy, wage inequality, international trade, and exchange rates.

Beginning with national income accounting and the measurement of key macroeconomic variables, students move through long-run and short-run macroeconomics. Long-run frameworks include the Solow growth model and the Romer model. Short-run macroeconomics introduces the AD/AS framework (built on the IS curve, Monetary Policy, and the Phillips curve) in both closed and open-economy contexts. Case studies include GDP growth and income disparities across countries, the Great Recession, the Covid-19 recession, the European debt crisis, the Volcker disinflation, the Great Inflation of the 1970s, and the Asian Currency Crisis of the 1990s.

Students leave prepared to engage critically with macroeconomic discussions in sources such as The Economist, The Economic Times, Mint, and The Wall Street Journal.

An introduction to stochastic processes with emphasis on modern applications and computational implementation, this course builds from foundational concepts through discrete-time Markov chains, branching processes, and continuous-time processes including Poisson processes, birth-death processes, and Brownian motion. Each unit pairs mathematical rigour with applications across artificial intelligence, robotics, economics, business, finance, and biological systems.

A time series is a sequence of time-indexed observations associated with a random phenomenon, recorded in order over a period. Such series arise across econometrics and finance, engineering, medicine, genetics, sociology, and environmental science. Since observations in a time series are time-dependent, standard statistical methods are insufficient for their analysis.

Introducing both classical and contemporary approaches to time series analysis, the course covers exploratory time series analysis, stationary and non-stationary stochastic processes, ARIMA and seasonal ARIMA models (estimation and forecasting), spectral analysis, multivariate time series models, VAR models, Granger causality, impulse response functions for dynamic analysis, and volatility modelling.

An introduction to causal inference techniques used in empirical economic research, with emphasis on policy evaluation and impact assessment, this course is intended for students who have completed introductory econometrics and are ready to work with modern tools for uncovering causal relationships from observational and experimental data.

Beginning with the potential outcomes framework and the distinction between correlation and causation, the course covers quasi-experimental methods used to estimate causal effects when randomized control trials are not feasible: randomized experiments, matching methods, instrumental variables (IV), difference-in-differences (DiD), and regression discontinuity design (RDD). Each method is examined through its underlying assumptions, identification strategies, estimation techniques and threats to validity.

Students also develop the ability to critically assess empirical strategies in applied economic research and to implement these methods using Stata. Applications drawn from development economics, labor economics, public policy, and health economics illustrate the techniques throughout.

Developing applied machine learning skills in business contexts, this course covers customer segmentation and lifetime value, churn prediction with temporal features, credit scoring and fraud detection under class imbalance, uplift modeling for targeting, and recommender systems. The second half expands into text analytics, transfer learning for images, sequential models, transformers, and foundation model applications including an exercise building AI agents for market research.

Practical trade-offs are central throughout: when to use complex models versus simpler approaches, how to handle imbalanced data, how to evaluate recommenders beyond accuracy, and when API-based AI makes sense versus custom solutions. Four problem sets provide hands-on experience; a group project integrates technical and business analysis.

This course builds foundational understanding of computational algorithms as applied in data science, with relevance to autonomous systems, finance, economics, econometrics, natural language processing, and personalized recommendations. The course covers simulation, optimization, and sequential modelling techniques for operating under uncertainty; dimensionality reduction and feature extraction for uncovering structure in high-dimensional spaces; and regularization strategies for maintaining model performance as data complexity increases.

A strong emphasis on hands-on learning runs through the course. Students work on projects including building dynamic models, implementing particle filters, exploring debiased regularization, and developing robust approaches for decision-making. By the end of the course, students have the skills to design, implement, and refine computational algorithms for machine learning and intelligent decision-making systems.

A seminar course exposing undergraduate students to frontier topics in data science, economics, and business through invited talks by external speakers from academia and industry, and student-led presentations. Emphasis is placed on contemporary research directions, critical engagement with the literature, and effective communication of technical ideas.

Applying introductory microeconomic principles to environmental and natural resource policy, this course addresses issues including air pollution, global climate change, population growth, forest management, and endangered species protection. Key concepts covered include externalities, public goods, property rights, and cost-benefit analysis. Policy approaches examined range from regulatory measures to market-based solutions such as taxes and tradable permits. The course also covers sustainable development, the clean energy transition, and non-market valuation methods. Instruction draws on reading material, lectures, case studies, and homework assessments.

Experimental economics is the application of experimental methods to study economic questions. Economic experiments replicate real-word incentives in a controlled manner and may be conducted in the laboratory, the field or even over the internet. The objective of this course is to test the validity of economic theories, analyse deviations from theoretical predictions and identify patterns of behaviour useful to build new theories. Further, we will review the popular behavioural results obtained in the literature so far.

Financial econometrics integrates finance, economics, probability, statistics, and applied mathematics to analyze complex financial market data. The course addresses a field shaped by decades of technological innovation, trade globalization, and the development of numerous new financial products: all of which have made rigorous quantitative analysis increasingly central to understanding modern markets.

Examining how gender shapes economic outcomes, organizational structures, and business practices, this course is organized into four sections. Foundational Frameworks and Macroeconomic Gaps covers the social construction of gender, the economics of care, and gender disparities in labor force participation and pay. Gender Dynamics in the Modern Workplace addresses leadership, workplace culture, harassment, intersectionality, and legal frameworks for equality. Advanced Topics and Future Frontiers explores the intersections of gender with technology, health, social norms, and corporate responsibility frameworks. Synthesis and Application culminates in hands-on data workshops and a final research project in which students apply course concepts to real-world issues.

Students develop analytical tools for critically evaluating gender-related challenges in contemporary organizations and public policy.

Building on the core Finance course, this course examines how capital is allocated by investors, priced in financial markets, and channelled through investment banking institutions. Starting from the investor's perspective, it covers portfolio construction, asset pricing, market efficiency, the role of AI, and behavioural biases. It then examines how investment banks and capital markets intermediate investor capital through underwriting, mergers and acquisitions advisory, trading, and market making.

This course develops strategic thinking and planning in marketing, drawing on the textbook Marketing Management: A South Asian Perspective. Divided into eight modules, the course covers marketing strategy, research, consumer behaviour, segmentation, targeting, positioning (STP), branding, competition, the marketing mix, and digital trends. Students learn to analyze markets, identify the right audience, craft effective marketing messages, and develop comprehensive, real-world marketing plans.

Focused on the economic challenges facing developing countries, this course covers the evaluation of interventions aimed at solving them at the micro level. Topics include measuring development outcomes at macro and micro levels; analyzing and evaluating market failures, including their demand and supply-side causes; assessing the impact of interventions implemented by policymakers and researchers in developing countries; and synthesizing findings to design new policy interventions amenable to rigorous impact measurement.

The course draws on economic research employing data science across areas including health, education, discrimination, labor, psychology, corruption, and civil wars. Students also write a technical research proposal addressing an unsolved issue in one of these areas.

This course explores the economic foundations of cities and local governments. It integrates urban economics, real estate markets, and the fiscal mechanisms that shape local governance. Students will examine why cities form and grow, how land and housing markets function, and how public finance instruments like taxation, zoning, and public spending influence urban outcomes. Emphasis will be placed on workhorse models in Urban Economics (e.g., Rosen-Roback spatial equilibrium, Alonso-Muth-Mills, Tiebout sorting etc.), state-of-the-art empirical methods, and policy debates in both developed and developing country contexts. Students will engage with data, case studies, and analytical tools to critically evaluate the challenges of urbanization, housing affordability, and local public goods and service provision.

This course focuses specifically on the nuances of venture investing at both early stage and late stages. Students will learn to navigate diverse venture investment scenarios, including seed funding, Series A investments, and pre-IPO rounds. Through practical exercises, case studies, and interactive workshops, participants will develop comprehensive skills in deal evaluation, due diligence, financial analysis, and strategic decision-making tailored for venture capital. This course serves as a continuation of the Entrepreneurial Finance course offered in the fourth semester, building upon foundational financial concepts.

This course begins by examining the civilizational idea of India—its cultural continuity, 
philosophical depth, and longstanding traditions of knowledge-making. Students engage with 
foundational concepts from the Darshanas, the Upanishads, and the epics to understand how 
India historically conceived of ethics, governance, human flourishing, and social harmony. A 
central focus of the course is the Arthashastra, which provides one of the world’s earliest and 
most sophisticated frameworks for statecraft, diplomacy, economic management, and public 
administration. Students study Kautilya’s sophisticated models of statecraft, intelligence, 
economic planning, and innovation, and connect them with contemporary domains such as AI 
governance, cybersecurity, digital infrastructure, data sovereignty, and technological autonomy. 
By placing ancient strategic thinking in dialogue with modern technological challenges, the 
course equips students to envision India’s role in an increasingly competitive global tech 
landscape.
Drawing from both classical thought and contemporary policy frameworks, students engage 
with forward-looking questions around India’s technological future: How can India build human-centric AI grounded in its philosophical traditions? What might a dharma-based framework for 
data governance look like? How can ancient models of education and knowledge transmission 
inspire 21st-century digital learning ecosystems? What forms of strategic autonomy in 
technology are essential for India to emerge as a global leader?

 This course helps students harness emerging technologies to drive business innovation across products and services, processes, and business models. Whether launching a startup or optimizing corporate strategy, students will explore how cutting-edge technologies fuel both sustaining and disruptive innovation, because not all change is revolutionary, but all progress matters. Through hands-on projects, case studies, and spirited discussions, students will learn frameworks to build systems for innovation, measure impact, and create scalable, market-ready solutions.

An interdisciplinary, case-based overview of the strategic and analytical skills essential for consultants in top-tier strategy firms, this course is built around three core pillars: structured thinking and problem-solving, quantitative analysis, and persuasive communication, with emphasis on a hands-on approach to learning. Through case studies from companies such as Netflix, Disney, and Uber, students develop the ability to dissect complex business problems, craft data-driven recommendations, and communicate insights that influence key stakeholders. Students gain a strong foundation in strategic analysis and practical consulting techniques applicable across industries.

Taking a design-centered approach to solving problems and developing technology-driven ideas in entrepreneurial settings, this course introduces core design thinking principles and their application to the creation and implementation of solutions. Tools are provided to navigate each stage of the design thinking process. Through project-based learning and real-world case studies, students gain practical insight into how the methodology fosters innovation and user-centric thinking.

What philosophical debates took place in early modern India, and what did it mean to lead a philosophical life at this time? This course explores philosophical arguments, changing concerns, and shared questions among philosophers active in India from the sixteenth to eighteenth centuries, reading them in their own words (in English translation). With major intellectual centers ranging from Banaras to Thanjavur, this period stands as an exceptionally rich time of philosophical writing.

Arguments are examined in areas including metaphysics, ontology, emotions, linguistics, reality, and existence, drawing on philosophers associated with Vedanta, Mimamsa (Vedic Hermeneutics), Jainism, Logic (Nyaya and Mantiq), and Linguistics, situated within their historical context.

This flagship course is where ideas meet execution. The Entrepreneurial Challenge Lab immerses students in the real-world experience of building a startup from idea to impact. Working in teams, students engage in customer discovery, prototype testing, market validation, and business model development. With a strong emphasis on learning by doing, the course goes beyond theory to develop the mindset and behaviours of successful entrepreneurs. Starting with an idea or opportunity space, students spend the semester refining it through feedback, mentorship, and real-world engagement, leaving not only with a venture concept but with practical tools to turn problems into scalable solutions.

The course equips aspiring startup founders with the financial acumen needed to build, scale, and sustain high-growth ventures. Through six focused modules, students will develop a strong foundation in financial statement analysis, cap table management, venture capital fundraising, valuation techniques, financial ratio interpretation, and unit economics.

Blending theory with hands-on application, the course includes practical exercises such as dissecting income statements, constructing cap tables, and modeling CAC vs. LTV. Students will learn how to effectively apply key financial concepts to strategic decision-making and communicate with investors in entrepreneurial contexts.

Leadership is essential for anyone navigating technology-driven careers, whether launching a startup, managing a team, or driving innovation. This intensive bootcamp, led by instructors from UC Berkeley's Sutardja Center, prepares students to lead in high-stakes, fast-paced environments. Through interactive exercises and real-time simulations, students explore leadership styles, group dynamics, crisis response, and decision-making under pressure, reflecting on their leadership identity and building practical tools for self-awareness, influence, and leading through disruption.

This interdisciplinary course examines how Artificial Intelligence (AI) is reshaping the way we work, innovate, and build careers. Through guest sessions with entrepreneurs, thought leaders, and industry experts, students will explore the opportunities and challenges of AI across areas such as the future of work, ethical AI, the creator economy, and sustainable entrepreneurship. Designed to spark curiosity and entrepreneurial thinking, the course encourages students to reimagine businesses and careers that are resilient, ethical, and impact driven. Whether launching a startup or joining an AI-powered enterprise, students will gain strategic insights to navigate and thrive in the AI era.

We call the earth our home, but do we ever really turn to ‘face’ it? This course explores questions around what the earth is, our relation to the earth, and how earth has been discussed in philosophy, history, and science. The goal of the course is an interdisciplinary investigation into the earth and closely associated concepts such as planet, world, and globe. Exploring earth in an interdisciplinary manner constitutes planetary humanities: a field that studies connections between humans and planet earth. The course engages with diverse areas such as phenomenology, ontology, astronomy, philology, Earth System Science, and the Gaia hypothesis. The course encourages students to think creatively about the earth and emphasizes understanding how the earth has been conceptualized in philosophy, history, and science before and after the Copernican revolution.

Building on the problem framing and viability assessments from the previous semesters, this course transitions students from ideation to execution. Students move beyond evaluating solutions and begin developing functional prototypes that address their identified grand challenges. The course introduces structured methodologies for iterative design, rapid prototyping, and user testing, equipping students with the tools to translate concepts into tangible, testable artefacts. Students engage closely with end users and communities to gather feedback and refine their prototypes through multiple cycles of iteration. Emphasis is placed on technical rigour, interdisciplinary collaboration, and the ability to communicate design decisions to both technical and non-technical audiences. The semester culminates in a prototype demonstration where teams present their solution, the design journey, and key learnings from user feedback.

Building on the iterative prototyping work of ILGC 4, this course guides students through the critical transition from a working prototype to a solution that is viable, scalable, and ready for real-world deployment. Students stress-test their solutions against the complexities of actual implementation, accounting for resource constraints, stakeholder ecosystems, regulatory considerations, and long-term sustainability. Implementation roadmaps are developed alongside pathways for scale, including social enterprise models, policy engagement, and technology transfer, with collaboration encouraged with external partners, NGOs, industry stakeholders, or government bodies where relevant. The course also introduces frameworks for measuring social impact, enabling students to articulate the tangible outcomes and broader significance of their work.

The final course in the ILGC series brings the student's four-year grand challenge journey to a close. This semester is centred on reflection, communication, and impact. Students will consolidate their project work into a comprehensive showcase: - documenting the full arc of their journey from initial observation to implemented or deployable solution, and articulating the social, technical, and personal dimensions of their learning. Beyond project completion, students are encouraged to reflect critically on what worked, what did not, and what they would do differently - developing the habit of structured retrospection that defines thoughtful engineering practice. The course also situates students' individual projects within the broader landscape of the SDGs and global grand challenges, helping them understand the systemic context of the problems they have engaged with. The semester culminates in a public exhibition where students present their work to faculty, peers, and external stakeholders, marking the completion of their ILGC journey.

Professional Communication equips students with critical communication competencies, enabling them to present themselves professionally to diverse audiences, including industry recruiters and higher education admission committees for BTech engineering students. Four key areas are covered: building a professional portfolio, delivering impactful technical presentations, mastering interview strategies, and developing networking skills. These competencies enable students to communicate with clarity, confidence, and impact across academic and professional contexts. The course complements Plaksha's engineering curriculum, preparing students for successful engagement with both industry and academia.

The Research Communication course introduces undergraduate engineering students to the foundational skills, methods, and processes of academic research writing. Students learn to abstract research themes from real-world problems, identify themes within research literature, conduct a critical literature review, identify research gaps and articulate research questions, and formulate hypotheses supported by evidence. The course culminates in a full-length research review manuscript that conforms to academic norms and engineering writing standards.

Why do stories fascinate, and how do they shape personal and cultural identity? Questions of kinship, power, gender, locality, and the relationship between past and present are formulated and expressed differently across narrative genres, carrying social, political, religious, historical, and personal themes. This course examines a variety of narrative forms in India, oral and written, traditional and modern, including regional folk tales, sung and performed oral epics, songs sung by women about women, ancient myths, medieval historical legends, contemporary biographies, feature films, and modern prose and verse.

Theories of orality and literacy, spoken word and visual image, ritual, theatre, and performance deepen this exploration, building toward an understanding of how culture, society, and history are constructed, enacted, and represented through narrative in India and beyond.

Understanding the customer is the starting point, but product market fit is the breakthrough. This course demystifies what product market fit means and why it is essential for any venture, helping students define the right problem, identify the right customer, and design business models that deliver real value. Through case studies, founder sessions, and workshops, students learn to turn ideas into solutions customers care about, building the mindset and tools to move forward with clarity, confidence and traction.

Equipping undergraduate engineering students with the ability to explain science and technology clearly, accurately, and meaningfully to stakeholders outside their field, this course addresses a growing need: engineers today are expected not only to build solutions but also to communicate their ideas to policymakers, industry leaders, investors, communities, and everyday users. Technology achieves lasting impact only when society understands and trusts it, making science communication essential for every engineer.

Students learn the principles and practice of communicating scientific and technical concepts to public audiences, developing skills to simplify, narrate, and adapt complex information into engaging, scientifically accurate content. Unlike traditional academic writing, the course emphasises creativity, storytelling, structure, and clarity.

Frameworks and tools for understanding pressing societal problems and challenges anchor this course, along with how these challenges can be addressed through entrepreneurial ventures that often leverage technology to create impact at scale. Causes that affect society at large, such as education, health, agriculture, and climate, are unique in their complexity and need to be approached, analyzed, and tackled differently than conventional venture-building spaces. Students examine how social ventures are built, and why they succeed in creating impact at scale or not.

Through examples and case studies, students learn to deeply understand a social problem, identify the broader systemic drivers behind it, incorporate field and market research and stakeholder mapping into potential solutions, and develop and test pilots and methods for assessing both quantifiable and non-quantifiable outcomes.

Designed for students who have completed the Entrepreneurial Challenge Lab and are ready to turn validated concepts into scalable ventures, this course translates entrepreneurial theory into actionable strategies, covering refining value propositions, go-to-market planning, financial modeling, valuation and funding pathways. A distinctive feature is hands-on learning with successful startup founders, who lead sessions on topics including growth hacking, leveraging technology for scale, and building compelling pitch decks. These insights are reinforced through workshops, practical exercises and investor pitch sessions.

Rising incomes coupled with rapid advances in agricultural and food technologies have driven dramatic improvements in food production and global food and nutrition security over the past six decades. Growing concerns persist, however, over the sustainability of the global food system, driven by depletion of natural resources, deterioration of ecological systems, extreme weather events, and an epidemic of chronic diseases. More than 700 million people suffer from hunger, and approximately 2 billion are deficient in one or more micronutrients. Between 1 and 2 billion people suffer from water scarcity or lack access to safe drinking water. Meanwhile, the world remains largely dependent on fossil fuels as its predominant energy source, generating greenhouse gas emissions that drive climate change. The US, China, and India account for more than 40% of the world's population and collectively consume approximately 50% of global food, energy, water, and natural resources. As the largest developed and developing countries, respectively, these three nations have substantial and sustained impacts on the ability of the rest of the world to address existing and emerging challenges in the global food, energy, and water nexus. They share many common interests while facing distinct differences and challenges in meeting their needs for food, energy, water, and economic development. This interdisciplinary course is offered by Cornell University, with joint efforts from two leading agricultural universities in China (China Agriculture University and Nanjing Agricultural University) and the Tata Institute of Social Sciences (TISS), a premier institution offering undergraduate and graduate degrees in social sciences in India. The University of Arkansas joined the collaboration in Fall 2020, Hefei University of Technology (Hefei, China) joined in Fall 2022, and the University of Puerto Rico-Mayaguez joined in 2023. Plaksha University in India joined the collaboration this year.

This course focuses specifically on the nuances of venture investing at both early-stage and late-stage. Students will learn to navigate diverse venture investment scenarios, including seed funding, Series A investments, and pre-IPO rounds. Through practical exercises, case studies, and interactive workshops, participants will develop comprehensive skills in deal evaluation, due diligence, financial analysis, and strategic decision-making tailored for venture capital. This course serves as a continuation of the Entrepreneurial Finance course offered in the fourth semester, building upon foundational financial concepts.