Welcome to the First Edition of DS-2.
This is the second foundational course in data science. It follows DS 1 and builds on them. It develops your ability to use generative models and clustering. We introduce you to tree based models, ensemble models and boosting. The course will introduce you to bayesian models and culminates with decision theory and model interrogation, and how to deal with imbalanced data situations.After you finish this module, you will be ready to run complex data science analyses on all kinds of data on your own, dealing with data imbalance, and finally being able to make decisions with the models.
This page introduces you to the team, the basic instructions, the schedule, and various elements of our class.
Dr. Rahul Dave
Dr. Rahul Dave, former Data Science faculty at Harvard University, will be your instructor for Data Science 2. He was on the original team for Harvard’s famous Data Science course, cs109, and has taught machine learning, statistics, and AI courses, both at Harvard and at multiple conferences and workshops. You can read more about him here.
The teaching assistants for the duration of this course are:
- Anusha is working as a Research and Teaching fellow with Univ.ai, she has previously worked with Robert Bosch as a software engineer in the automotive domain.
- She is an incoming student at TU Delft.
- She is a big believer in sustainability and has been working on applications of AI in sustainable energy for the last 3 years.
- Arya is currently a data analyst at Schneider Electric and a Teaching Assistant at Univ.AI.
- She is passionate about image processing and is currently researching the application of computer vision in the detection of deep fake videos.
We have very carefully designed the coursework to give you, the student, a wholesome learning experience. Each week shall include:
- 2 Sessions
- 1 Lab
- Office hours
Session: What to expect
Before the session begins, students are expected to complete a pre-class reading assignment and attempt a quiz based on the same.
A session will have the following pedagogy layout which will be repeated three times:
- Approx. 10-15 minutes of live online instruction followed by a quiz
- Some sessions will have hands-on coding exercises or group activities
- Sessions will help students develop the intuition for the core concepts, provide the necessary mathematical background, and provide guidance on technical details.
- Sessions will be accompanied by relevant examples to clarify key concepts and techniques.
After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class and optional post-class reading will be provided.
Lab - What to expect
A lab is a TA driven one-hour session that is divided into 3 major parts.
- Each lab begins by solving parts of a complete problem. This problem is designed to help you with your homework and further elucidate concepts you learned in lecture.
- After discussing exercises, we will have a semi-formal Q/A session. The first part of this session is limited to homework questions, but the second part is more free-for-all, where you can ask any doubts that lingered over from lecture.
Welcome Session - Preparing for this class
There will be a Welcome Session scheduled on
8th March 2021 at
9:00 PM IST for all registered students. Please check your mail for more information.
High level course schedule
NOTE: Below timings are in IST
- Tuesday Series: 9:00 PM - 11:00 PM
- Friday Series: 9:00 PM - 11:00 PM
- Saturday Series: 7:30 PM - 8:30 PM
- Mondays: 9:00 PM - 10:00 PM
- Wednesdays: 9:00 PM - 10:00 PM
This is a part of ML-X Course, hence you should have completed ML-1 before this.
Diversity & Inclusion
We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions .We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:Offensive comments related to age, race, religion, creed, color, gender (including transgender/gender identity/gender expression), sexual orientation, medical condition, physical or intellectual disability, pregnancy, or medical conditions, national origin or ancestry.Intimidation, personal attacks, harassment, unnecessary disruption of talks during any of the learning activities.
Logistics: What do you need to begin?
- You MUST connect your courses.univ.ai account to Github. Here (install instructions) is how to do it!
- You MUST log on to discourse.univ.ai forum software. Here (install instructions) is how to do it.
- Before the first class, you will want to install Anaconda Python and play with Python a little bit. You will learn a bit about Jupyter notebooks, and a bit about Python coding. We’ll talk about Anaconda in class.
Education software we use
- Our lectures and labs are carried out via Zoom (install instructions).
- Quizzes & exercises will be conducted on the digital learning platform Ed.
- All exercises and homeworks in this course will be done in jupyter notebooks. This link will help you setup jupyter lab and get you acquianted with jupyter notebooks.
- We have an innovative platform for our courses. Here is some usage information for it. We will talk about the platform in class.
- The Slides will be available on Univ.AI Platform.
- For forum discussion we will be using Discourse.
- And for homework submissions and lab work we will be using github.