About DS2500

Provides intermediate to advanced python programming for data science with the aim of preparing students for more advanced courses in data science and to enable practical contributions to software development and data science projects in a commercial setting. Covers object-oriented design patterns using Python, including encapsulation, composition, and inheritance. Through case studies, the course will survey key concepts in data science with an emphasis on machine-learning (classification, clustering, deep learning), data visualization, and natural language processing.

Accompanied by DS2501: Lab for DS2500 (1.000 credits) in which students will practice the programming techniques discussed in lecture through hands-on experimentation.

Topics	Data Science techniques: data visualization, fundamental data statistics, linear regression, correlation and covariance, scaling and normalization, k-means clustering, k-nearest neighbor classification, cross-validation, decision trees, polynomial regression. Intermediate Programming: loops, functions, files, Python data structures, classes and objects, command-line, code design and quality, unit testing. Python Libraries and Tools: Jupyter Notebooks, Pandas, NumPy, Scikit, Seaborn, Geopandas.
Syllabus	Download
Piazza	https://piazza.com/northeastern/spring2024/ds2500
Lecture Questions	https://bit.ly/ds2500_lecture_q
Grading Guidelines	Code quality & Visualizations
Gradescope	https://www.gradescope.com/courses/679045
TA Appreciation	Feedback form

Professor & Office Hours

Laney's Office Hours begin Tuesday, January 9th. We do not hold office hours on university holidays. (TA office hours begin on Tuesday, January 16th -- details on the TA page.)

Laney Strange (she/her)

E-mail	laneys@northeastern.edu
Web	https://northeastern.edu/home/laney
Zoom (for OH)	https://northeastern.zoom.us/my/laney
Office Hours	T 3:30-6pm, F 12-1pm (schedule time during OH or just drop in. Meserve 313, or my Zoom. Appointments take priority, though!) If the Tues and Fri office hours get booked quickly, or if they don't work for you, please just email me and we'll set up something else.

Lecture Schedule

DS2500 lectures introduce key concepts and dive into examples and applications. Lectures are in-person and attendance is expected. We don't want or expect anyone to come to class when they're sick, though, so we'll post notes and code after class every day, which you can use to catch up on any missed material. We'll alternate weeks focusing on Python programming, and focusing on data science techniques.
Python Practice Problems will be assigned towards the end of most Tuesday lectures. You'll put together code that we'll all use in the following lecture that Friday. We'll have time set aside to do these in class, or you can submit on your own.
DS2501 labs will have a graded assignment each week, due at 9pm on Monday. Most of these assignments are auto-graded, so we need you to follow the specs exactly (function names, files names, etc.). There are generally 5 problems per lab, and 3 of them need to pass the autograder for full credit.

DS2500-01 (Lecture)	TF 9:50-11:30am CG 097
DS2500-02 (Lecture)	TF 1:35-3:15pm CG 097
DS2501-01 & -03 (Lab)	M 8:00-9:40am WVH 210A (both sections meet in 210A) TAs: Ivan, Vidhi, Roland
DS2501-04 (Lab)	M 9:50-11:30am WVH 210A TAs: Shivani, Matthew
DS2501-05 (Lab)	M 9:50-11:30am WVH 210B TAs: Laura, Matthew
DS2501-06 (Lab)	M 11:45am-1:25pm WVH 210A TAs: Seamus, Naman
DS2501-07 (Lab)	M 11:45am-1:25pm WVH 210B TAs: Varun, Naman
DS2501-08 (Lab)	M 1:35-3:15pm WVH 210A TAs: Keegan, Alina
DS2501-09 (Lab)	M 1:35-3:15pm WVH 210B TAs: Kenichi, Alina
DS2501-10 (Lab)	M 3:25-5:05pm WVH 210A TAs: Pratyush, Neha, Daniel
DS2501-13 (Lab)	M 3:25-5:05pm WVH 210B TAs: Harsh, Neha

New to Python or feeling a little rusty?

Below we've collected helpful videos from DS2000 and some other useful links.

DS2500 Videos - Python Review	DS2000 in 2000 Seconds Installing Anaconda
DS2000 Videos - Basic Python	Variables & Arithmetic Working with Files Data Viz Conditionals While Loops Data Sources Lists List Operations Strings
DS2000 Videos - Functions, Scope, Data Structures	Functions Calling functions 2D Lists 2D Lists Variables & Scope Dictionaries Dictionaries Wordcount
DS2000 Videos - Classes & Objects; Data Science Techniques	Classes and Objects Classes Cont'd Recommendation Systems CSV to Dictionary
Additional Python Resources	Python 3 Documentation W3 Schools Tutorial Python for Everybody Learn Python.org Geeks 4 Geeks MIT Open Courseware Google's Python Class