Early Bird Application Deadline: April 15th, 2024
Regular Application Deadline: May 15th, 2024
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About Discover Summer School in Data Science
Discover’s Summer School in Data Science is an immersive six-day program, meticulously crafted to introduce young scholars to the world of data science. Through a blend of interactive assessments, group activities, and expert-led sessions, participants will delve into the forefront of machine learning and data analysis across diverse domains such as healthcare, marketing, public policy, and psychology.
In our virtual setting, students have the opportunity to connect with leading academics from around the globe, gaining insights that transcend geographical boundaries. From engaging lectures to hands-on workshops, the Discover Summer School curriculum is designed to be inclusive and accessible to students of all backgrounds and levels of expertise.
At Discover, we believe in fostering a holistic and intersectional understanding of data science, empowering students to develop a critical lens that extends beyond the classroom into their academic, personal, and future professional endeavors.
Questions You May Have
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Each batch of the summer school will span 6 days. Here are the batch details:
Batch 1: June 3rd to June 8th
Batch 2: June 10th to June 15th
You're welcome to opt for whichever batch suits you best!
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The summer school is open to young scholars aged 15 and above.
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To apply, simply fill out our Interest Form, and we will promptly revert to you. Please note that there are only limited seats in the program and your application will be selected based on your response.
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The regular application deadline for the course is May 15th. However, for those interested in early bird registration, the deadline is April 15th. Please ensure to submit your application by the respective deadlines for consideration.
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The fee for Discover Summer School is INR 10,000. However, for early bird registrants, the cost is discounted to INR 8,000. Please note that there are only limited slots available for early bird registration.
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Upon successfully completing the program, participants will receive certificates in acknowledgment of their achievement.
Meet the Faculty
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Dr. Utkarsh Agrawal
is a Research Fellow at the University of Oxford. He holds a PhD in Computer Science from the University of Nottingham and an M.Tech. in Information Technology from the Indian Institute of Information Technology, Allahabad. Utkarsh's research interests revolve around Electronic Health Records, Health Data Science, and Epidemiology, with a focus on Applied Machine Learning. His expertise extends to Clustering Classification Algorithms, Ensemble methods, and Data Fusion.
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Feryl Badiani
is a DPhil student in Psychology at the Victoria University of Wellington and holds an MSc in Social and Cultural Psychology from the London School of Economics and Political Science. Her master's thesis delved into the intricate dynamics of individual agency amidst conflicting knowledge systems. Feryl's PhD research explores the evolution of Hinduism, positing that its contemporary vitality stems not solely from historical large-group coordination, but from its nuanced impact on the fitness of diverse Indian linguistic communities. While her primary approach involves qualitative inquiry, she aspires to integrate quantitative methodologies for broader, large-scale investigations and applicability.
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Dr. Archana BA
has a PhD from the Indian Institute of Science, Bangalore, and an MBA from the Indian Institute of Technology, Kharagpur. She had worked in academia as an Assistant Professor at T A Pai Management Institute, Manipal, and also worked in the corporate as a Project Manager at IBM. Her core area of research is in marketing, viral marketing, and consumer behavior. She has published research articles in top-tier academic journals and case studies published in Ivey/Harvard and Emerald publications.
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Dr. Cledwyn Fernandez
is currently a policy consultant at ICRIER, a public policy think tank based out of Delhi. He has a Ph.D. in Economics from XLRI Jamshedpur. He was an Assistant Professor of Economics at TA Pai Management Institute, Manipal. His areas of interest are microeconomics, macroeconomics, development economics, public-private partnership & infrastructure development, public policy, and industrial economics.
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Session by: Feryl Badiani
Psychology is grouped as a science because the methods it uses are scientific. Conclusions are based on quantitative data that is often derived from a large sample and is significant. In this workshop, we will go over the basics of statistics- including descriptive and inferential statistics, such as mean, t-test, and regression. We will also use how to calculate this using R and R Studio.
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Session by: Dr. Utkarsh Agrawal
Machine learning has revolutionized our world, becoming ingrained in various daily applications such as finance (credit scoring, trading), biometrics (facial recognition, motion tracking, object detection, voice recognition), medicine (DNA sequencing, brain tumor detection, drug discovery), and manufacturing (predictive maintenance). Through this workshop, we aim to emphasize the profound impact of machine learning, with a particular focus on its research and application during the COVID-19 pandemic to understand the effectiveness of vaccines.
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Session by: Dr. Archana BA
The talk titled 'Applications of Data Science in Marketing' will cover the importance of data science tools in understanding consumer behaviour, enabling market segmentations, analyzing channel distributions, and deeper aspects of product pricing. The talk will begin with giving students a foray into the concepts of both, marketing and data science independently, followed by how marketing firms are adopting data science to scale and improve market performance. Through this talk, students will be able to understand the principles of marketing and will be adept at integrating the learnings of data science applications into marketing practice.
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Session by: Dr. Cledwyn Fernandez
Data science has ample applications in government and public policy. With the use of causal analytics, data science can help inform economic and public policy in various sectors such as health, education, and climate change. The talk will focus on how data science as a discipline has evolved and will then focus on how economic and public policy has leveraged data science for better decision-making. The talk will also discuss best practices across countries and the way forward.
About the Course
Contact Us
In case of escalations, please contact q@essai.in
If you are an academic who is interested in mentoring students with us, please refer to the recruitment page here.