51茶馆

About the course

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Embark on a transformative journey into the world of data science and learn the mechanics of how data is a building block game changer for all tech giants – from Amazon to Apple to Google. Applied Data Science with ML & AI is a hands-on live course that will enable you to build a solid foundation in data science and analysis techniques with various types of datasets such as tabular, textual, and images, that can be structured or unstructured. This course will teach you how to collect, analyze, apply suitable algorithms, inspect with visualization, build models and draw predictions to transform raw data into insights.听

You will deep dive into both structured and unstructured data. In managing various types of data, you will learn new tools and techniques to analyze, visualize and present the key insights from data, enhancing your ability to communicate data-driven narratives. By the end of this course, you will have a well-rounded understanding of data science, ML/AI and practical skills, empowering you to tackle data-driven challenges.听

You will kick off your data science journey with a hands-on capstone project. The project will engage you in thematic problem-solving with data. You will have three themes to choose from: data and economics, data and climate, data and healthcare and apply your learnings from each week to solve a problem under any of these themes or an intersection such as data science, machine learning, healthcare and climate change (e.g. )

What鈥檚 more? You will get an opportunity to contribute to projects ongoing at 51茶馆 that are applying data science to solve real-world problems.听

Unlock the power of data now and develop your skills for effective analysis and problem-solving!

“The course turned out to be much more than I expected. I learned how to approach data-driven problems, use github and data bases like Kaggle, even google collab, and implement machine learning algorithms like linear regression, logistic regression, and clustering. I also developed a deeper appreciation for statistical thinking, critical analysis, and working with real datasets under time pressure.”

– Bhrigu, Grade 10, Don Bosco School

Eligibility

• High school students from India and across the world are eligible to apply.
• Students should be entering or in grades 9-12 as in summer 2025.
• Students should be comfortable with written & spoken English, which will be the medium of instruction for the courses.
• Students should have consistent access to the Internet and a computer that aligns with the
• Students should have good and consistent academic record.

Who is this course for?

This course is for high schoolers looking to gain knowledge in data science, machine learning, artificial intelligence and develop data analytical skills or for those who are considering to study these subjects for higher education. It is for students aspiring to be data analysts, data scientists and anyone who is a data enthusiast.听

Prerequisites: High proficiency in written & spoken English. You will be required to submit your latest mark sheet in the application form.听

What do I take away?

By the end of the programme, you will:

1. Gain in-depth knowledge of what data science involves and learn about its applications in various contexts
2. Practice data manipulation and visualization techniques to uncover trends and insights
3. Understand data types, focusing on the characteristics of tabular, textual, and image data
4. Discover the art of visual storytelling and presentation through data.
5. Solve a problem with the use of tools and platforms for data analysis.

Programme Details

Week	Lecture Module	Project Module
Week 1	Decoding data science: What, why, when & how? Gain foundational understanding of data sets and techniques for effective data analysis Types of data Attributes of data Applications of data Open source scientific computing languages (Python, R, Julia) GitHub, Notebooks, Colab etc	Coding to decode data: Data types & attributes Practice working with various data types and their attributes and set up a data science environment Unix based operating system – Windows Subsystem for Linux听 Conda environments and basics of Python GitHub, Notebooks and Colab
Week 2	Going beyond data: Machine learning and artificial intelligence Understand data as the building block for AI and ML and connect the dots to understand its applications Introduction to probability and statistics Basics of machine learning algorithms and applications Foundation of artificial intelligence and applications	Practice algorithms: Deep learning and machine learning Do hands-on practice on key algorithms, statistics and models Computing probabilities & summary statistics for a dataset Using existing machine learning algorithms for unsupervised pattern recognition (such as K-Mean and PCA) and supervised algorithms Deep learning, AI, NLP, and language models
Week 3	Visualizing data: Creative coding & insights Transform data into visual representation and insights to implement various types of data visualizations Simple to complex data Static to dynamic data Introduction to creative coding	Draw insights: Variety of visualizations Play with various types of data to understand how to create and interpret a variety of data visualizations. Color theory to increase accessibility Geometry – 2D to 3D Creative coding for dynamic visualization using P5JS
Week 4	Building prediction models: What, why, how? Understand the steps in building a machine learning model and learn about different validation techniques Data preprocessing & training听 Data testing听 Validation techniques & performance metrics	Deploy models: Real-world scenarios听 Practice building a machine learning model and training pipeline and deploy models in real-world scenarios Train & test Internal & external validation Server and API building, Docker Image
Week 5	颁辞耻苍蝉别濒濒颈苍驳:听 Get a chance to ask questions to the faculty and the mentor and get their answers and perspective.听 You are encouraged to ask questions to the faculty around the following aspects: What are some related fields that can be explored based on this course topic? What are the future trends in the related fields? How can the course learning help the student enhance their college applications?	Mentoring: You are encouraged to ask questions to the mentor around the following aspects: What are some sub-topics to learn/practice beyond the course? What is the scope of the course topic if the student wants to pursue/study it further? What are some personal projects that can be taken up outside of the course based on the knowledge gained in the course?

Capstone Project

You will choose from three themes: data and economics, data and climate, data and healthcare and solve a problem in your chosen theme using data. For your chosen theme and problem, you will analyze the three distinct types of datasets: tabular, textual, and image. By the end of the project, you will collate your learnings to present a solution to the identified problem within the chosen theme.听

Faculty

Rintu Kutum is a Faculty Fellow/Data Scientist at the Department of Computer Science, 51茶馆. He was the project coordinator (postdoctoral fellow) of 鈥淎L/ML for Healthcare鈥� theme under the 鈥淐ity Knowledge Innovation Cluster 鈥� Delhi Research Implementation and Innovation鈥� project at the Center for Excellence in Healthcare, IIIT-Delhi. His Ph.D. was in the area of Machine Learning, Genomics, and Gene regulatory Networks from CSIR-Institute of Genomics & Integrative Biology, New Delhi. He uses open source scientific computing languages such as R, Python, Julia etc., to accelerate his scientific research and promote via training. During his Ph.D. tenure, he has trained 22 scholars from various institutions across India. He has also been an eLife Ambassador during 2018-2019 and 2019-2020, and mainly worked on 鈥淩eproducibility research through Open Source Scientific Computing鈥�. During this tenure, he organized 3 workshops with the help of various institutions; and has been actively working towards enhancing computer science literacy amongst life science scholars in India. He is one of the members, and instructor for Reproducibility for Everyone (R4E) Initiative. His long term interest is to train & nurture transdisciplinary scholars for doing collective research, and continues to work on the area of 鈥淐omputational Health Sciences: Cellular Ecosystem to Public Health鈥�.

Grading, assessments, certification and much more

All Ashoka Horizons courses offer a certificate on satisfactory completion of the programme.听

• Weekly project stages – 30%
• Final project presentation & submission – 40%
• In-class participation and attendance – 30%

Class participation will be assessed based on your active engagement in live sessions, contributions to discussion forums, and involvement in Teaching Fellow-led activities.听

Achieve More鈥�with Horizons:

• Successful Course Completion Certificate
• Letters of Academic Achievement to top 3 students based on exceptional performance听
• Potential opportunity to contribute to an ongoing data science project at 51茶馆, intended to solve a real-world problem*.

听*For select students, subject to discretion of the faculty

IT Requirements

This programme听 is administered through an online platform. Students are expected to have a foundational understanding of computer usage, including but not limited to sending emails and conducting Internet searches. Consistent access to the Internet and a computer that aligns with the are also requisite for participation in the programme.

Have a question about Ashoka Horizons Achievers Programme? Write to us on horizons@ashoka.edu.in