Welcome to DS-1 A!
In this course, we will provide you with a strong foundation in data analysis, starting from the basics of cleaning data, to conducting exploratory data analysis and creating effective visualizations.
By the end of this module, you will develop skills in pre-modeling and post-modeling exploratory data analysis and visualization, which will help you gain a deep understanding of your data and identify key trends and patterns.
This page introduces you to the team, the basic instructions, the schedule, and various elements of our class.
Dr. Pavlos Protopapas
Dr. Ignacio Becker
- Astronomer currently pursuing a Ph.D. in Computer Science at Pontificia Universidad Católica in Chile.
- His main area of research is applied AI to astrophysical problems.
- Nowadays, he focuses on developing models to process the real-time data of the next generation of telescopes.
Click on avatars of the TAs to know more about them.
We have very carefully designed the coursework to give you, the student, a wholesome learning experience. Each week shall include:
- 2 Sessions
- 2 Labs
- 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 a few 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 1.5 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 further elucidate concepts you learned in lecture.
- After discussing exercises, we will have a semi-formal Q/A session. This part of the lab is free-for-all, where you can ask any doubts that lingered over from lecture.
You are expected to have programming experience and basic machine learning concepts such as model fitting, test-validation, regularization, etc.
- Programming Experience:
- Pandas - Specific topics: Introducing Pandas Objects Data, Indexing and Selection
- NumPy - Specific topics: Understanding Data Types in Python, The Basics of NumPy Arrays, Computation on Arrays: Broadcasting, Comparisons, Masks, and Boolean Logic
- Matplotlib - Specific topics: Simple Line Plots, Simple Scatter Plots, Visualizing Errors, Density and Contour Plots, Histograms, Binnings, and Density
- Sklearn API
- Machine Learning Experience:
- Loss functions
- Overfitting and regularization
- Regression and classification
You can also use, MLPrep and PyPrep provided by Univ.AI to learn the topics mentioned above.
In addition to this, you can use the book - Introduction to Statistical Learning to refresh the basic mathematical concepts and Python Data Science Handbook to refresh machine learning concepts.
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.
 K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.