Teaching Assistants

Please see Piazza for any scheduling updates and connection information. All times are ET/Boston.

Name	Office Hours	Zoom
Mahek Aggarwal	Mon 8p-10p, Wed 8a-9a	zoom
Colbe Chang	Tue/Wed 5:30p-7:30p	zoom
Sofie Cook	TBD	zoom
Ashiwn Kasargode	Mon/Tue 10:15a-12:15p	zoom
Andre Kirby	Tue/Wed 1p-3p	zoom
Emily Liu	Mon/Tue 11a-1p	zoom
Khushi Morparia	TBD	zoom
David Pogrebitskiy	Mon/Wed 12n-2p	zoom
Rishita Shroff	Mon/Wed 11a-1p	zoom
Abigail Sodergren	Thu 10:30-12:30	zoom
Sri Sreepada	TBD	zoom
Prerna Chander	Mon/Wed 9:30a-11:30a	zoom
Vinesh Kumar Gande	Thu/Fri 9:30a-11:30a	zoom
Franc O	Tue 11a-2p	zoom

About the course

Catalog Description

DS3000: Introduces core modern data science technologies and methods that provide a foundation for subsequent Data Science classes. Covers: working with tensors and applied linear algebra in standard numerical computing libraries (e.g., NumPy); processing and integrating data from a variety of structured and unstructured sources; introductory concepts in probability, statistics, and machine learning; basic data visualization techniques; and now standard data science tools such as Jupyter notebooks.

DS5110: Introduces students to the core tasks in data science, including data collection, storage, tidying, transformation, processing, management, and modeling for the purpose of extracting knowledge from raw observations. Programming is a cross-cutting aspect of the course. Offers students an opportunity to gain experience with data science tasks and tools through short assignments. Includes a term project based on real-world data.

4.000 Credit Hours
Prerequisites: Assumes an Intermediate-level understanding of Python Programming (DS5010 or DS2500 or the equivalent).

My vision for these two courses

This semester, at my suggestion, DS3000 (Undergraduate) and DS5110 (Graduate) are being cross-listed. This means that the content of these two courses are being merged. Both sections will attend the live lectures MTWR 3:20p-5:00p ET/Boston. Why is this being done? As the co-director of the Master in Data Science program here at Northeastern, one of my functions is to help define and refine the course curriculum of our MS/DS core courses and elective offerings. I very much view both DS5110 and DS3000 as bringing together Mathematical and Programming foundations to explore key concepts, algorithms, models, and applications in Data Science. From here you will continue your studies in Data Science to explore Machine Learning, Natural Language Processing, Database Systems, Visualization, and other topics relevant to Data Science. A course in Data Science Foundations sets the stage for these subsequent topics. My goal for this course is to explore a number of interesting topics in Data Science without overlapping too much with material that you have already covered, or will cover in the future. In addition you will expand your programming skills for data minimulation, visualization, analysis, and algorithmic development.

This class is not recorded

Other than the second-week of class, this class is not recorded. I strongly you recommend you come to every live session with your cameras on and your microphone muted. In the event you need to miss class, I will be posting lecture notes and coding examples that evening. (Materials should be available by 8pm.) I very much welcome active discussion in my classrooms. To ask a question, feel free to virtually raise your hand or post your question on chat.

In week 2 (May 15th-18th), I will be in Boston for faculty meetings. Pre-recorded lectures will be made available and we will temporarily run the class asynchronously. In week 3 and for the rest of the semester, we will return to live / synchronous lectures on Zoom.

Readings

There is no singular textbook that I know of on Data Science Foundations. The subject is still very new and contantly evolving. Below I list a number of books that I have found interesting and well-written and readings will be assigned from selected chapters. Most of these books are available through O'Reilly E-Books. As Northeastern students, you have free access!