Summer Semester 2026 - Courses
Foundation Course: Indian Civilizations
Course Code: FC-0201-1
Faculty: Gopalkrishna Gandhi, Professor, 51²è¹Ý
Course Description: The course will reflect on early philosophical and political thought in India, examining Asokan Edicts, Sangam age thinking in India’s Tamil tracts, the non-religious imaginations of Sarmad and the Sufis. It will also examine Yatric India with the India of Visitors through the ages, studying the journeys of ancient travelers such as Fahein to the current Dalai Lama. It will study the history of imprisonment in India from early times, including that of a serving emperor jailed- Shah Jehan, through colonial times to our own times when penology has changed from its emphasis on punishment to reform. The Course would reflect on the testamentary role of the Constitution of India in the way we are evolving or not evolving as a people, a nation and a civilisation.
Pre Requisites: None
Cross listing: None
Grading Policy: Students will be required to write one assignment paper due at the end of the term, for which students will be given an adequate number of prompts from the subjects discussed in class.
Foundation Course: Literature and the World
Course Code: FC-0701-1
Faculty: Abir Bazaz, Assistant Professor, 51²è¹Ý
Course Description:What is literature? And what does it teach us about who we are, the world we live in, and where we are going? Does literature help us make better sense of the lives of others or the necessary strife involved in the human condition? Does literature put us more genuinely in touch with ourselves and one another? What is the relation between literature and
politics? Or literature and religion? In this course, we will search for answers to some of these questions by reading modern literature from all over the world. We will be studying literary texts by Fyodor Dostoyevsky, Anton Chekhov, Franz Kafka, Ryunosuke Akutagawa, V.S. Naipaul, Tayeb Salih, Toni Morrison, Jorge Luis Borges, Samantha Schweblin, Clarice Lispector and Flannery O¡¯Connor among others.
Pre-requisites: None
Cross listing: None
Grading Policy: Regular attendance, classroom posts, midterm paper or exam and final exam or paper.
Foundation Course: Quantitative Reasoning and Mathematical Thinking
Course Code: FC-0306-1
Faculty: Aalok Thakkar, Assistant Professor, 51²è¹Ý
Course Description:“Wonder not then, what God for you informs,
If your objects may be steps to ascend to God.”?
¡ª John Milton
There is a particular kind of grief that belongs to those who reach for the infinite and find, at the last moment, that their arms are too short. The mystic knows it. The poet knows it. And, as this course will argue, so does the mathematician.
Once exiled from the Heaven of the Infinite, we have found ourselves in a world that feels chaotic, broken, and opaque. Since that imagined abandonment, human beings have attempted to reconstruct a stairway to the skies: through art, music, poetry, science, and even mathematics. However, we are not made for mathematics. We are creatures of bone and breath and endings, furnished with ten fingers, a little din in our throats, and a brief corridor of years between birth and disappearance. And yet, from within this corridor, we have had the audacity to attempt a description of the infinite.
In the first module of this course, we will trace how different civilisations have constructed systems for counting, comparing, and compressing the world into symbol and language. What we discover, when we look closely, is that every such attempt bears the fingerprints of its makers. What presents itself as the neutral grammar of reality turns out, on closer inspection, to be a series of choices made by particular people in particular moments: what symbols to use, how many to use, what operations to permit, when to divide by zero, and what to use as a yardstick for proof.
We will then turn to what may be the most ambitious intellectual project in human history: the attempt to build a formal language capable of capturing all of mathematics, a tower of symbols tall enough to touch the sky.?This is the Babelian dream, and like Babel, it ends in a?ruin. A language powerful enough to describe arithmetic is powerful enough to describe, most fatally, itself. It can encode well-formed sentences like “this statement cannot be proved,” which are neither provable nor refutable.?With merciless rigour, Kurt G?del showed that this is not a flaw of a?particular construction but an inescapable feature of logic. In the second module of the course, we will?sketch the proof of this incompleteness?and show that any symbolic language powerful enough to do arithmetic will contain truths it cannot prove. We will see that mathematics is not merely incomplete in the way any unfinished project is incomplete, but rather?broken from the inside and?beyond repair.
And yet, somehow, most mathematicians wake up every morning and go to work. This is because mathematics works, with an extravagance that borders on the offensive. Structures conjured in abstraction turn out to describe the world with almost indecent precision. The same incomplete, historically contingent?mathematics turns out to be unreasonably effective in physics, economics, and the social sciences. In the final module of this course, we will sit with utility as a consolation for incompleteness.
By the end of this course, you will have witnessed the grief of the subject. You will also have witnessed something else:?human beings, when confronted with a proof of their own limitations, chose to keep rolling the boulder anyway. This is, in the end, what mathematics is. Not a stairway to the infinite; not a view from above. It is?the stubborn, fingerprinted, perpetually incomplete work of creatures like us who refused to stop counting.
Pre-requisites: None
Cross listing: None
Grading Policy: 1. Assignments (40%): The course includes four problem sets designed to supplement lectures and develop practical application skills. 2. Examination (20%): One 2-hour, closed-book in-class exam. 3. Class Participation & Attendance (15%): Regular attendance and active engagement in discussions and activities. Participation quality matters more than quantity – thoughtful questions and contributions are valued. 4. Final Paper (25%): A collaborative final paper. Includes proposal submission, draft review, and final revision.
Foundation Course: Great Books
Course Code: FC-0601-1
Faculty: Vivek V. Narayan, Assistant Professor, 51²è¹Ý
Course Description: This course is built around six foundational texts: books that not only articulated a theory but also shaped a political movement. The ¡°great books¡± we will read are Marx and Engels¡¯s The Communist Manifesto (1848), Du Bois¡¯s The Souls of Black Folk (1903), Ambedkar¡¯s Annihilation of Caste (1936), Simone de Beauvoir¡¯s The Second Sex (1949), Edward Said¡¯s Orientalism (1978), and Anzaldua and Moraga¡¯s This Bridge Called My Back (1981). Each of these books has inspired ways of thinking in disciplines across philosophy, political theory, sociology, anthropology, history, and literature. Moreover, their ideas have empowered intersectional struggles against capital, colonialism, race, caste, and patriarchy. By tracing the particular histories of these ideas and struggles, this course will explore the relationships between analysis and transformation, and, more broadly, critical theory and political action.
Pre-requisites: None
Cross listing: None
Grading Policy: Class participation: 20%; In-class essays: 4 x 20% each = 80%.
Foundation Course: Mind & Behaviour
Course Code: FC-0503-1
Faculty: Tatyana Aleksandrovna Kostochka, Assistant Professor, 51²è¹Ý
Course Description: Day to day, all of us are trying to make our way in the world, to live our best life: we socialize, we make difficult choices, we learn, we create. But what is a good life? Is it a pleasant life? A long life? A moral life? Are there multiple ways to have a good life? How we answer these questions partially depends on what we are. So, in this course, we will start out by looking at the nature of our mind and our relationship to it. Having gotten this foundation, we will dive into the nature of wellbeing and what it means to have a good life. In the process, we will not only consider what it is for something to be good for you but also how it relates to what is morally good. Finally, we will move on to explore the various things that people do: how we make decisions, look for the truth, and create art.
Pre-requisites: None.
Cross-listing: None
Grading Policy: In-class assignments: 100%. This will include: quizzes (~30%) In-class writing: (~70%)
?
Foundation Course: Introduction to Computer Science
Course Code: CS-1102
Faculty: Aalok Thakkar, Assistant Professor, 51²è¹Ý
Course Description: This introductory course offers a foundation in computational thinking as a powerful, structured approach to problem-solving. It is built around one uncompromising standard: correctness. Students will explore the core principles of computation and gain familiarity with key subdisciplines in the field. Working both with and without code, students will learn to design, analyze, and refine solutions with attention to correctness, clarity, and style. The course is anchored in two fundamental computational models: the functional model, which treats computation as the evaluation of mathematical functions, and the imperative model, which views it as a sequence of state-changing instructions. More details are available on the course webpage.
Pre-requisites: None
Cross listing: None
Grading Policy: 1. Short Assessments / Applied Work (Quizzes, In-class Writings, Presentations, Group Work, Viva or Lab Reports) (60%), 2. Final Assessment (30%), 3. Regular Course work (10%).
History: War: History, Politics, Society
Course code: HIS-2505/ SOA-2234/ POL-2107 / IR-2067
Faculty: Pratyay Nath, Associate Professor, 51²è¹Ý
Course Description: How has war shaped gender identities and political ideologies in our societies? In what ways do race, class, and religion figure in the experience of war? How have computer games, movies, and comics made war an object of popular consumption? How have war, animals, and ecology shaped each other? These are some of the questions that the present course addresses. It offers a global history of the inter-relationship between war, politics, and society. In the first week, we will look at war and politics, with respect to ideology, propaganda, and protest. In the second week, we will study war and gender, in terms of masculinity, femininity, sexuality, and violence. In the third week we will analyse war and identity, with respect to labour, class, religion, and race. In the fourth week, we will examine war and the environment, in terms of animals and ecology. In the fifth week, we will study war and entertainment with respect to computer games, movies, literature, and comics. In the final week, we will look at the politics of dissemination of information about war in posters, museums, and media. Drawing examples from across time and space, the present course unravels this rich history through a close reading of the latest scholarly literature on the subject. Alongside this, students will get hands-on experience of analysing modern cultural artifacts of war (like movies, graphic novels, posters, and games). By the end of the course, students will have an understanding of the social, cultural, and political lives of war in the past and present times.
Pre-requisites: None.
Cross-listing: Political Science, Sociology, and International Relations
Grading Policy: Class Participation (25%) Mid-Term Presentation (35%) Term Paper (40%)
English: Introduction to Classical Greek
Course code: ENG-3370
Faculty: Sarah Yona Zweig, Visiting Faculty,? 51²è¹Ý
Course Description: This course is an introduction to Classical Greek. We will use the textbook Reading Greek and cover the Greek alphabet, core vocabulary, and some basic rules of morphology and syntax. This course is designed to make learning Greek both rewarding and enjoyable. At the same time, mastering a classical language does take time and effort. You should plan on about two hours of preparation before each class session for readings, completing exercises, memorizing paradigms, and independent study. Because the course builds knowledge step by step, regular preparation is essential; repeated absences or too little time spent on the assignments will make it more difficult to keep pace.
Course Book Joint Association of Classical Teachers¡¯ Greek Course. Reading Greek. 2nd ed. Cambridge: Cambridge University Press, 2007. Volume 1: Text and Vocabulary, Volume 2: Grammar and Exercises
Pre-requisites: None
Cross-listing: None
Grading Policy: Grade Distribution:? Homework Exercises: 20%, In-class Quizzes: 20%, Participation: 10% ,Mid-term Exam: 20%, Final Exam: 30%.
GRADE BREAKDOWN
100¨C95 A
94¨C85 A-
84¨C80 B+
79¨C75 B
74¨C70 B-
69¨C65 C+
64¨C60 C
59¨C55 C-
54¨C50 D+
49¨C45 D
44¨C40 D-
<40 F
ASSESSMENT Submit all your assignments on time. If you turn in an assignment late, your grade will drop by one-third of a letter grade for each extra day. If you need an extension, make sure to ask at least 48 hours before the deadline. Extensions will only be granted in exceptional cases.
English: Introduction to Classical Arabic
Course code: ENG-3060
Faculty: ? Sarah Yona Zweig, Visiting Faculty, 51²è¹Ý
Course Description: This course offers an introduction to Classical Arabic, designed to equip students with the foundations needed for reading premodern Arabic texts. We will work primarily with Mastering Arabic 1 and selected materials from Manuel d¡¯arabe moderne 1. Over the course of the semester, you will learn the Arabic script, acquire core vocabulary, and gain familiarity with the basic principles of Classical Arabic morphology and syntax. The course is reading-focused: it prepares students to approach sources from the pre-Islamic period, the Qur?an, exegetical literature, adab, and historical writing. It is not a course in conversational Arabic, and it does not cover modern Arabic dialects. However, a solid grounding in Classical Arabic provides an excellent foundation for later study of Modern Standard Arabic or regional dialects. Mastering a classical language does take time and effort, even if you already know the script. You should plan on about two hours of preparation before each class session for readings, completing exercises, memorizing paradigms, and independent study. Students willing to commit time and attention to the language will gain access to one of the major literary and intellectual traditions of the premodern world.
Pre-requisites: None
Cross-listing: None
Grading Policy: Grade Distribution:? Homework Exercises: 20%, In-class Quizzes: 20%, Participation: 10% ,Mid-term Exam: 20%, Final Exam: 30%.
GRADE BREAKDOWN
100¨C95 A
94¨C85 A-
84¨C80 B+
79¨C75 B
74¨C70 B-
69¨C65 C+
64¨C60 C
59¨C55 C-
54¨C50 D+
49¨C45 D
44¨C40 D-
<40 F
ASSESSMENT Submit all your assignments on time. If you turn in an assignment late, your grade will drop by one-third of a letter grade for each extra day. If you need an extension, make sure to ask at least 48 hours before the deadline. Extensions will only be granted in exceptional cases.
International Relations: The Rise of Populism in International Politics
Course code: IR-2013/ POL-2038
Faculty: Ananya Sharma, Assistant Professor, 51²è¹Ý
Course Description: Populism is one of the main political buzzwords of the 21st century. The rise of populist forces in recent years has generated new challenges in many long-established democracies, such as the US, UK, Germany, Italy, Greece, and France, as well as destabilizing states worldwide, such as in Venezuela, Brazil, Hungary, Turkey, the Philippines, Thailand, and India. What explains the rise of these forces? What are the consequences? And what can be done to mitigate the risks? The course aims at bringing together the conceptual analysis of populism with comparative case studies in different regions of the world. Given the highly contested nature of populism, we will look in depth to different theories of populism, including institutional, ideological, discursive and socio-cultural understandings of populism. The course will also explore the conditions of emergence of populism and the relations between populism and key political concepts, such as democracy, security, gender, international organizations and political communication. The course covers: (i) The conceptual foundations of populism, tracing its definitional debates and mapping the typologies of populism including ideational, socio-cultural, performative approaches. (ii) Competing explanations focused on ¡®demand-side¡¯ cultural value change, economic grievances, and patterns of immigration, and also ¡®supply-side¡¯ electoral rules and party competition.(iii) The broader implications of populism for civic political culture, democratic norms and policy agenda; and alternative strategic responses.
Pre-requisites: None
Cross-listing: Political Science
Grading Policy: Attendance and Class Participation (10%) ii. Reality Check: Behind the Headlines (25%) iii. Exploriments Around the Globe: E-Zine (25%) and iv. Final end term essay (40%)
Media Studies: Wisdom of the Documentary Film
Course code: MS-2091
Faculty: Natasha Badhwar, Visiting Faculty, 51²è¹Ý
Course Description: With a focus on South Asian non-fiction films, this is an experiential course on analyzing the affective impact of viewing documentary film on the self and diverse audiences. Through screenings and moderated discussions, students will develop the practice to observe, appreciate and articulate the form, content and craft of documentary, and connect it to film theory, visual culture and socio-political discourse. Students will participate in discussions and write personalized reviews on themes, theory, aesthetics and impact of a range of documentary work.
Pre-requisites: None
Cross-listing: Visual Arts
Grading Policy: Grading Rubric includes mid-term essay, final video submission, weekly film essays and class participation.
Media Studies: Writing Memoir
Course code: MS-3121/CW-2121
Faculty: Natasha Badhwar, Visiting Faculty, 51²è¹Ý
Course Description: Memoir is an expansive genre that borrows from the craft of fiction and poetry to create compelling, honest writing that resonates with readers and expands their empathy. The best personal essays are a location to identify questions, interrogate ideas and explore vulnerability and dissonant realities through deep research, interviews and reflection on lived experiences. As one sifts through memories, emotions and personal experience to connect the personal to the universal, writing about one’s life can be a healing and transformative journey, both for the writer as well as readers. This course offers an empathetic, nurturing space with creative prompts, writing exercises, readings, analysis and feedback to develop one’s unique voice and write compelling essays that are intimate, thought-provoking and deeply honest.
Pre-requisites: None
Cross-listing: Creative Writing
Grading Policy: Grades will be distributed over class participation, weekly essays, mid-term and final submission
Media Studies: How to read a film
Course code: MS-2410
Faculty: Aakshi Magazine, Visiting Faculty, 51²è¹Ý
Course Description: Memoir is an expansive genre that borrows from the craft of fiction and poetry to create compelling, honest writing that resonates with readers and expands their empathy. The best personal essays are a location to identify questions, interrogate ideas and explore vulnerability and dissonant realities through deep research, interviews and reflection on lived experiences. As one sifts through memories, emotions and personal experience to connect the personal to the universal, writing about one’s life can be a healing and transformative journey, both for the writer as well as readers. This course offers an empathetic, nurturing space with creative prompts, writing exercises, readings, analysis and feedback to develop one’s unique voice and write compelling essays that are intimate, thought-provoking and deeply honest.?
Pre-requisites: None
Cross-listing: Creative Writing
Grading Policy: Grades will be distributed over class participation, weekly essays, mid-term and final submission
Media Studies: Listeners to Practitioners: Recording and Post Production Workflow
Course code: MS-2301
Faculty: Gaurav Chintamani, Visiting Faculty, 51²è¹Ý
Course Description: Every piece of audio represents countless micro-decisions made during its recording and post-production. The gap between what the ear hears and how it is captured and finessed for presentation is massive¡ªthis course bridges that gap through understanding the deliberate craft that brings recordings into being. This course deconstructs fundamental audio capture and production workflow, taking students through technical and creative decisions that transform raw sound into polished recordings. We will explore essential tools¡ªmicrophones, digital audio workstations, and signal processing¡ªas interconnected elements in coherent production methodology. Students will develop proficiency in microphone selection and placement, DAW operation, take compilation, and basic mixing procedures using core signal processing concepts. Through practical exercises and comparative analysis, students will identify subtle choices distinguishing amateur from professional recordings across genres and production styles. The course cultivates operational competency and informed decision-making serving both technical accuracy and creative intent. Designed as an entry point to audio production, this course provides essential groundwork for further study in advanced recording techniques and sound design while developing critical listening abilities.
Pre-requisites: None
Cross-listing: None
Grading Policy: Attendance: 25% Exercise(s): 25% Final Project: 50%
Biology: Python for Research in Life Sciences
Course code: BIO-3636
Faculty: Sudipta Tung, Faculty, 51²è¹Ý
Course Description: This is an introductory course to Python to use this versatile programming language for aiding research in Life sciences. This course does not assume any prior knowledge in programming, starts with the basic coding lessons, and builds up upon them. The course will nudge you to think intuitively in terms writing an algorithm. This skill, once mastered, is transferable to any programming language in future. In addition, after first reviewing the basics of Python 3, we shall learn how to use Python scripts to import, organize, analyze, and visualize experimental data, and run own simulations to generate new in silico research data. Using a combination of a lectures, and guided hands-on sessions, students will be exposed to a variety of different Python features across various topics in Life sciences. We shall explore examples and case studies with data, inter alia, behavioral experiments, genomics, epidemiology, and biostatistics. Students will also be introduced to the rapidly developing field of image processing and machine learning. Students will get a chance to hone their new Python skills by solving take-home assignments on their own. More details can be found on this webpage: https://sites.google.com/ashoka.edu.in/summercourse-python/home
Pre-requisites: None
Cross-listing: None
Grading Policy: Assignments 30%, Exam 30%, DIY project 30%, Classroom participation 10%
Entrepreneurship: Business Applications of Data Science
Course code: ENT-2045
Faculty: Tushar Jaruhar, Visiting Faculty, 51²è¹Ý
Course Description: This course is designed to equip entrepreneurs with essential skills and knowledge in AI and technology, enabling them to leverage these tools to drive innovation and growth in their businesses. Through a combination of theoretical learning and hands-on practical exercises, participants will gain proficiency in key technologies such as Excel, KNIME, Tableau, GPT, AI driven Videos and no-code app development platforms. Participants will begin by mastering Excel, learning how to manipulate and analyze data effectively using advanced functions and formulas. They will then delve into KNIME, a powerful data analytics platform, to explore data preprocessing, analysis, and visualization techniques. With Tableau, participants will learn to create dynamic and interactive data visualizations to gain actionable insights from their data. The course will provide participants with the skills to track and analyze website tra?c, measure campaign effectiveness, and make data-driven decisions for their digital marketing strategies. Participants will explore the capabilities for natural language processing and image generation, unlocking opportunities for creative content creation and automation. Additionally, participants will learn about no-code app development platforms, empowering them to build and deploy applications without writing a single line of code. By the end of the course, participants will have the knowledge and skills to harness the power of AI and technology to drive innovation, streamline operations, and create value in their entrepreneurial ventures.
Pre-requisites: None.
Cross-listing: None
Grading Policy: Relative.
Entrepreneurship: Artificial Intelligence and Technology for Entrepreneurs
Course code: ENT-2041
Faculty: Tushar Jaruhar, Visiting Faculty, 51²è¹Ý
Course Description: This course is designed to equip entrepreneurs with essential skills and knowledge in AI and technology, enabling them to leverage these tools to drive innovation and growth in their businesses. Through a combination of theoretical learning and hands-on practical exercises, participants will gain proficiency in key technologies such as Excel, KNIME, Tableau, GPT, AI driven Videos and no-code app development platforms. Participants will begin by mastering Excel, learning how to manipulate and analyze data effectively using advanced functions and formulas. They will then delve into KNIME, a powerful data analytics platform, to explore data preprocessing, analysis, and visualization techniques. With Tableau, participants will learn to create dynamic and interactive data visualizations to gain actionable insights from their data. The course will provide participants with the skills to track and analyze website tra?c, measure campaign effectiveness, and make data-driven decisions for their digital marketing strategies. Participants will explore the capabilities for natural language processing and image generation, unlocking opportunities for creative content creation and automation. Additionally, participants will learn about no-code app development platforms, empowering them to build and deploy applications without writing a single line of code. By the end of the course, participants will have the knowledge and skills to harness the power of AI and technology to drive innovation, streamline operations, and create value in their entrepreneurial ventures.
Pre-requisites: None.
Cross-listing: None
Grading Policy: Relative.
Psychology: Psychology of Health and Illness ??
Course code: PSY-3083
Faculty: Annie Baxi, Visiting Faculty, 51²è¹Ý
Course Description: Course Objectives The paper is a blend of Critical and Cultural Health Psychology and intends to detail the various theoretical perspectives on health and illness and strategies that promote healing and wellbeing. Health is defined as ¡®a way of being¡¯ which is not limited by the absence of malfunction or disease but an experience that is grounded in one¡¯s body and is shaped largely by individual and collective attributions around it. The designed course attempts to address questions like how do we identify and operationalise markers of a healthy living in a context? What are the various ways in which illness(es) can be experienced? What is the symbiotic relationship of the individual reality and social processes in understanding health and illness? Learning outcomes After completing the course, the student will be able to: Analyse and critically evaluate existing theories on health and illness Understand individual ¡®symptoms¡¯ as interactive and constituted by macro systems. Enhance skills associated with health and illness research. Apply concepts in designing health-related interventions for communities.
Pre-requisites: Introduction to Psychology (PSY-1001)
Cross-listing: None
Grading Policy: Grade Cut Offs
85 and above = A
75-84= A-
70-74= B+
65-69= B
60-64= B-
55- 59= C+
50-55= C
Below 50 = D/F
Psychology: Violence as a Human Behavior
Course Code: PSY-3045
Faculty: Simantini Ghosh, Assistant Professor, 51²è¹Ý
Course Description: What enables human beings to perpetrate acts of cruelty and aggression? What effects do these events have on survivors? Can we predict violent behaviors accurately for some groups? Can we prevent violence from happening? Violence is a widespread and complex issue that has been part of human behavior through time. In this class, we will tease violence apart along multiple axes, but usually in a data driven fashion. In the first half of the course we will break violence down to its elemental blocks using concepts from neurobiology, biochemistry, genetics, psychology, evolution and epigenetics. The second half of this course will reassemble fundamental types of violence based on religion, politics, gender and socioeconomic structures using the concepts discussed. We will also spend some time discussing technology facilitated violence and the role of fake news in inciting mass violence. Prospective students are encouraged to approach the material as part of a journey to understand violence. Each member of the class might arrive at a different conclusion about violence at the end of the course, but the goal of the class is to provide them with different frameworks to interpret and analyze data about violence to reach their conclusion. This class is MOSTLY taught as an advanced seminar with a flipped classroom style. The instructor will play the role of a faculty moderator of student led discussion. Each week a few research articles, reviews, book chapters or articles from the media will be discussed by students, with the entire class being an active participant in discussion. All students will be expected to participate in class discussion as they come to class having done the readings for the day.
Pre-requisites: Statistics and Research Methods I (PSY-2001) or Statistics for Economics (ECO-1400) and Introduction to Psychology (PSY-1001)
Cross-listing: None
Grading Policy: https://docs.google.com/document/d/1SxDfcZszcIVuJpGORsoDiph1PGNmFZLcKIav8RyFaQg/edit?usp=sharing
Psychology: Clinical Psychology
Course Code: PSY-2041
Faculty: Simantini Ghosh, Assistant Professor, 51²è¹Ý
Course Description: This course focuses on understanding the phenomenology (description), etiology (causes), and treatment of abnormal behavior. Major psychological syndromes will be discussed along with the current APA classification system (DSM-5) and other classification systems. Genetic, biological, social, and psychological parameters implicated in the etiology of these syndromes will be introduced. Students will learn the principles of clinical assessment and the 5P model of psychological assessment and case formulation
Pre-requisites: Introduction to Psychology ( PSY-1001) or Thinking like a Psychologist ( PSY-1003)
and Statistics and Research Methods I (PSY-2001) or Statistics for Economics (ECO-1400)
Cross-listing: None
Grading Policy:https://docs.google.com/document/d/14ecCW7xa9uiiJ8EvvGKStngWqaCOjw3WxPuBMdmjHKI/editusp=sharing
Psychology: Computational Modelling of Behaviour
Course Code: PSY-3102
Faculty: Supriya Ray, Associate Professor, 51²è¹Ý
Course Description: Computational models in neuroscience aim to mimic brain mechanisms to understand behaviour, including abnormal states in mental disorders, using modern computational tools. Computational models of behaviour are powerful tools used to simulate and understand human behaviour through mathematical and computational techniques. They aim to replicate human cognitive processes, allowing for predictions about human behaviour in various situations. These models are used in various fields such as education, healthcare, advertising, defence, and software engineering. Successful implementation involves defining clear objectives, building the model based on relevant psychological theories and empirical evidence, testing and validating the model, and refining it based on test results. There are various types of computational models, including connectionist models, agent-based models, and neural models, each with its own strengths and applications. Recent advancements include combining computational models with neuroimaging to link social computations to the brain and using agent-based models to simulate economic and social systems. In summary, computational models of behaviour are essential tools for understanding, predicting, and augmenting human performance by combining insights from multiple disciplines.
Pre-requisites: None
Cross-listing:? None
Grading Policy: Mid-sem exam (20%), Two quizzes (15% each), Final Exam (50%)
Psychology: Cognitive Neuroscience
Course Code: 3021
Faculty: Supriya Ray, Associate Professor, 51²è¹Ý
Course Description: Cognitive neuroscience bridges the gap between biological structures and the magic of thought. By mapping neural circuits to cognitive processes like memory, emotion, perception, action and consciousness, you¡¯ll discover how a three-pound organ inside the skull creates your entire reality. Your brain receives information of your environment in complete darkness. It has never seen light, heard a sound or touched an object – it simply interprets electrical pulses generated by your sensory organs. Everything you feel and do is contingent on a high-fidelity illusion constructed by billions of tiny cells called neurons. Join me to decode the biological blueprints of the self and master the science behind how we think, feel, perceive, and act.
Pre-requisites: None
Cross-listing:? None
Grading Policy: Mid-sem exam (20%), Two quizzes (15% each), Final Exam (50%)
Psychology: Statistics for Advanced Research (STAR) in Psychology
Course Code: PSY-3001
Faculty: Naseer Ahmad Bhat, Assistant Professor, 51²è¹Ý
Course Description: Statistics for Advanced Research (STAR) is a six-week intensive summer course designed for psychology students who want to move beyond introductory statistics and develop real analytical confidence. If you have completed SRM-1 (Statistics and Research Methodology-1) and still feel that research articles are difficult to understand, the methods section feels overwhelming, or statistical tables look like a foreign language-this course is for you. In SRM-1, you learned the foundations: what data are, types of variables, distributions, measures of central tendency and dispersion, correlation, t-tests, basic ANOVA, and introductory regression. STAR builds directly on this base, revisiting these concepts at a deeper and more applied level, while also introducing you to advanced statistical techniques that are widely used in contemporary psychological research. The goal is simple: to help you shift from ¡°I avoid statistics¡± to ¡°I can understand, evaluate, and conduct data analysis independently.¡± STAR is especially relevant if you aspire to conduct rigorous research. Many students have strong research ideas but feel uncertain about how to translate those ideas into sound methods and appropriate analyses. This course will help you develop the skills needed to design studies with greater clarity, choose the right statistical tools, and interpret results with confidence. You will learn how to evaluate research evidence critically, understand assumptions behind different methods, and communicate findings effectively. Over six weeks, the course will cover key advanced approaches used across clinical, developmental, social, and cognitive psychology. Topics include normality testing and model diagnostics, exploratory and confirmatory factor analysis(for scale development and advanced validation), measurement invariance testing, missing value analysis (a crucial but often neglected skill in real-world datasets), factor analysis (to explore and validate psychological constructs), moderation and mediation (to test ¡°when¡± and ¡°how¡± effects occur).. You will also revisit ANOVA and regression at a more advanced level, including multiple regression and logistic regression, with a focus on interpretation, model building, and reporting. In addition, STAR provides an introduction to Structural Equation Modelling (SEM) , a powerful framework for testing measurement models and complex theoretical pathways. Finally, the course introduces longitudinal and panel data methods, helping you understand how psychologists analyze change over time, developmental trajectories, and within-person patterns. STAR is ideal for students planning future graduate education (Master¡¯s, MPhil, or PhD) and those who want to build a strong quantitative foundation early. Advanced research programs increasingly expect students to engage with data analytically, not just conceptually. By strengthening your statistical thinking now, you will enter graduate school with a clear advantage-better prepared for coursework, thesis work, independent research, and reading high-quality journal articles without fear. At the same time, STAR is not only for ¡°future academics.¡± Data skills are increasingly transferable across careers. Whether you plan to work in psychology, public health, education, policy, or industry, the ability to manage data, test hypotheses, interpret models, and make evidence-based decisions is a valuable asset. STAR helps expand your career horizons beyond traditional pathways by giving you practical analytical competence that is relevant in many sectors. The course is designed to be accessible, structured, and supportive-without being simplistic. The emphasis will be on understanding concepts clearly, applying techniques thoughtfully, and developing the confidence to work with research data independently. By the end of the course, you should be able to read research papers with sharper insight, design stronger studies, choose analyses responsibly, and move forward in your academic journey with genuine statistical authority.
Pre-requisites: Statistics and Research Methods I (PSY-2001)
Cross listing: Economics
Grading Policy: The course will have usual grading components that include quizzes, projects, assignments, attendance, and participation. The grading will be absolute grading
Psychology: Statistics and Research Methodology II
Course Code: PSY-2002
Faculty: Naseer Ahmad Bhat, Assistant Professor, 51²è¹Ý
Course Description: PSY-2002: Statistics and Research Methodology II is an intermediate-to-advanced course designed for students who have completed foundational training in psychological statistics and are ready to engage with the full research cycle as practicing scientists. The course moves decisively beyond the mechanics of individual statistical tests, directing students toward a critical and integrative understanding of how quantitative evidence is generated, evaluated, and communicated in psychological science. The course is structured around two substantive pillars ¡ª survey research and experimental research ¡ª each of which students develop from the ground up: from initial conceptualisation and design, through data collection and statistical analysis, to the production of a complete, APA- formatted empirical report. This structure ensures that statistical knowledge is always anchored in the realities of actual research practice rather than treated as abstract computation. Across the semester, the course will cover the process of research, scale development, basic psychometric testing- reliability and validity assessment, exploratory factor analysis, use of surveys and report writing. Experimental designs include basic concepts, elements of experimental designs, internal and external validity, types of designs- within group, between group and mixed designs, single-factor, factorial, and quasi-experimental paradigms, issues of research ethics, scientific publishing and communication with in depth discussion on scientific journals, journal metrices, publishing houses, and critical evaluation of published literature. Instruction will be delivered through weekly lectures, hands-on statistical workshops using JAMOVI during the class, and structured discussion sessions that consolidate learning through guided exercises and group project support.
Pre-requisites: Statistics and Research Methods I (PSY-2001)
Cross listing: –
Grading Policy: TBA
Economics: Introduction to Data Analytics and Machine Learning?
Course code: ECO-3401
Faculty: Parush Arora, Assistant Professor, 51²è¹Ý
Course Description: The course discusses why data analysis is a required skill among social scientists. The course not only focus on theory but aims to teach students how to analyze data and
apply techniques. The core topics will include prediction, supervised-unsupervised learning, bias-variance trade-off, cross-validation, regularization, how linear regression is used from the perspective of prediction Vs causation etc. The course will also introduce some of the machine learning techniques like lasso, k-nearest neighbours, decision tree etc. The course aims to teach students to analyze data on RStudio andR.
Pre-requisites: Statistics for Economics (ECO-1400) and Econometrics (ECO-2400)
Cross-listing: None
Grading Policy: Student grades have 4 components: midterm exam (20% weightage), Final exam (30% weightage), Assignments (20% weightage) and final presentations (30% weightage). The syllabus for the midterm exam will be announced in class and on AMS/Moodle/Google Classroom. Final exam will be cumulative.
Mathematics: Calculus
Course code: MAT-1000
Faculty: Kumarjit Saha, Associate Professor, 51²è¹Ý
Course Description: Calculus is a foundational subject for all areas of mathematics, science and even modern social sciences. This course is a fast-paced but thorough coverage of the subject. We will see many elementary applications of the subject in Physics, Optimisation, Biology, Economics etc. so that students who have covered Calculus before will not feel the entire course to be repetitive. This course is not about applying formulas and solving problems. It goes to the real reasoning behind them .It is expected that you will solve lot of problems in this course. We will follow the following texts (not exactly) in this course: Textbooks: 1) James Stuart, Calculus. (Cengage). 2) Apostol, Calculus, vol 1.
Pre-requisites: None
Cross-listing: None
Grading Policy: Grading will be a combination of class quizzes, assignments and one final in-class exam (to be held in the last week of the course)
China Studies: Introduction to Mandarin Intensive
Course code: CHI-1101 and CHI-1102
Faculty: Shao Jhe Chin, 51²è¹Ý
Course Description: Introduction to Mandarin is a beginner¡¯s language course, specifically designed for students with no exposure to Modern Standard Chinese (Putonghua ÆÕͨԒ). The course will introduce students to the Chinese language and culture and focus on basic speaking, listening, reading, and writing skills. This includes an introduction to phonetics, basic vocabulary, and expressions of daily use. The course will prepare the students to attain a certain level of understanding and proficiency in Chinese language through interpretive, interpersonal, and presentational modes. It will integrate Chinese culture into language learning through both written and audio-visual means to familiarize students with the culture and language. At the end of the class, students will be able to read and comprehend Chinese texts on various topics moderately without the aid of pinyin and tone marks. They will be able to give presentations in Chinese on a prepared topic and write short paragraphs and compositions on a limited range of topics and situations. This course consists of a total of 90 hours, including two semesters of Mandarin language classes and cultural lessons. By the end of the program, students are expected to reach the TOCFL Novice 2 level (equivalent to HSK 2).
Pre-requisites: None
Cross-listing: None
Grading Policy: Attendance(15%),Homework assignments (20%) Multiple Quizzes(20%) Oral Presentations (25%),Cultural Activities(10%) TOCFL mock test (10%).
Note: Students are required to register for both CHI-1101 and CHI-1102 simultaneously in Summer 2026.
Political Science: Qualitative Research Methods in Political Science
Course code: POL-1030
Faculty: Uday Chandra, Visiting Faculty, 51²è¹Ý
Course Description: This is a required methods course in the political science major, but it can also be taken by other students interested in learning qualitative research methods (interviews, ethnography, case studies, archival methods, and discourse analysis) in a unique hands-on format.
Pre-requisites: None
Cross-listing: None
Grading Policy: Standard A-F with regular assignments and final summative assessment
Political Science: Quantitative Research Methods
Course code: POL-1007
Faculty: Aashna Khanna, 51²è¹Ý
Course Description: This course focuses on developing the analytical skills required to engage with academic research in the social sciences. It provides a foundational overview of working with quantitative data and the tools required to analyse it, including basic statistical concepts and computational analysis using statistical software. The course covers topics such as causal inference, research design, exploratory and descriptive analysis and linear regression. No prior knowledge of mathematics or programming skills is required.
Pre-requisites: None
Cross-listing: None
Grading Policy: Problem sets (40%), midterm exam (25%), final exam (25%) and remaining 10% for participation.
