Introduction to Data Science

CMSC320 – Spring 2023

Introduction to Data Science

Header image to data science course website showing a multi-colored cube or matrix

Instructor: Maksym Morawski (morawski@umd.edu)
TAs: Lectures [0101]: IRB 0324, Monday & Wednesday & Friday 10:00–10:50 PM

Description of Course

Data science encapsulates the interdisciplinary activities required to create data-centric products and applications that address specific scientific, socio-political or business questions. It has drawn tremendous attention from both academia and industry and is making deep inroads in industry, government, health and journalism.

This course focuses on (i) data management systems, (i) exploratory and statistical data analysis, (ii) data and information visualization, and (iv) the presentation and communication of analysis results. It will be centered around case studies drawing extensively from applications, and will yield a publicly-available final project that will strengthen course participants' data science portfolios.

This course will consist primarily of sets of self-contained lectures and assignments that leverage real-world data science platforms when needed; as such, there is no assigned textbook. Each lecture will come with links to required reading, which should be done before that lecture, and (when appropriate) a list of links to other resources on the web.

Requirements

Students enrolled in the course should be comfortable with programming (for those at UMD, having passed CMSC216 will be good enough!) and be reasonably mathematically mature. The course itself will make heavy use of the Python scripting language by way of Jupyter Notebooks, leaning on the Anaconda package manager; we'll give some Python-for-data-science primer lectures early on, so don't worry if you haven't used Python before. Later lectures will delve into statistics and machine learning and may make use of basic calculus and basic linear algebra; light mathematical maturity is preferred at roughly the level of a junior CS student.

There will be two written midterm examinations. There will not be a final examination; rather, in the interest of building students' public portfolios, and in the spirit of "learning by doing", students will create a self-contained online tutorial to be posted publicly. This tutorial can be created individually or in a small group. As described here (TBA), the tutorial will be a publicly-accessible website that provides an end-to-end walkthrough of identifying and scraping a specific data source, performing some exploratory analysis, and providing some sort of managerial or operational insight from that data.

Final grades will be calculated as:

  • 40% homeworks
  • 15% midterm #1
  • 15% midterm #2
  • 15% final
  • 15% final project

This course is aimed at junior- and senior-level Computer Science majors, but should be accessible to any student of life with some degree of mathematical and statistical maturity, reasonable experience with programming, and an interest in the topic area. If in doubt, e-mail me: morawski@umd.edu!

Office Hours & Communication

We are going to use a combination of in-person office hours, as long as those are a thing, and the Piazza forum (sign up here: https://piazza.com/umd/spring2023/cmsc3200201) for Q&A. This means that it's appropriate to use Piazza for asynchronous communication with the course instructors and other students, and also for short high-bandwidth discussions that could usually take place before/after class. Note that Piazza is not appropriate for things like asking for accommodation or other such issues/concerns, please email your instructor (morawski@umd.edu) with [CMSC320] in the email subject line for those things.

As mentioned above: for private correspondence or special situations (e.g., excused absences, DDS accomodations, etc), please email your instructor (morawski@umd.edu) with [CMSC320] in the email subject line.

* All hours are EDT

Instructor Time Location
Maksym Morawski IRBE 2232, M/W 1:00-3:00pm; please email (morawski@umd.edu) with [CMSC320] in the email subject line. TBA
You can find TA office hours locations for all your courses here. Unless otherwise stated, CMSC320's assigned space is AVW 4122. If TAs are hosting hours virtually, you can find the zoom link on their names.
Do keep an eye on Piazza, though; TAs will sometimes swap hours, shift hours, host hours on Zoom, and so on! Additionally, we have at least one TA explicitly covering Piazza on each weekday; all course staff will float around Piazza in general, too!
Day Time TAs
Name Hours Location

Weekly View of in-person and online office hours:

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property. The following policies (about masks, projects, group chats, and so on) are largely directly copied and pasted from standard campus and CMSC-specific guidance, so they should not come as a surprise to anyone.

Projects/Labs: On any graded project or lab, you are NOT allowed to exchange code. We compare each student's code with every other student's code to check for similarities. Every semester, we catch an embarrassingly high number of students that engage in cheating and we have to take them to the Honor Council.

GroupMe/Other Group Chats: We encourage students to talk about course material and help each other out in group chats. However, this does NOT include graded assignments. There have been a couple instances in the past where students have posted pictures/source files of their code, or earlier sections have given away exam questions to later sections. Not only did this lower the curve for the earlier section because the later one will do better, the WHOLE group chat had to pay a visit to the Honor Council. It was an extremely ugly business. Remember that in a group of 200+, someone or the other will blow the whistle. If you happen to be an innocent person in an innocent groupchat and someone starts cheating out of the blue, leave it immediately (and better yet, say you are leaving and say you will report it).

Course evaluations

Course evaluations are important and the department and faculty take student feedback seriously. Near the end of the semester, students can go to http://www.courseevalum.umd.edu to complete their evaluations.


Schedule

(Schedule subject to change as the semester progresses!)
Topic Date Topic Reading Slides Notes
1 1/25
  • Introduction
  • What is Data Science
Sign up on Piazza!
2 1/27 Background Alternate Bayes Rule video My notes Topics covered:
  • Correlation / Covariance
  • Bayes Rule
  • Expected Value
  • Uncertainty
  • Distributions
3 Experimental Design
4 What is Data?
5 Introduction to Python, Git, SQL and Pandas Homework 1, Homework 2, and Homework 3 out
6 Data Visualization
7 Summary Stats
8 Data Exploration
9 Hypothesis Testing
10 Feature Engineering
11 Review
12 Midterm 1
13 Introduction to Modeling
14 Classification
15 Regression
16 Image Processing
17 Natural Language Processing
18 Time Series
19 Review
20 Recommendation Engines
21 Big Data
22 Graphs
23 Communication
24 Ethics
25 Final Review

Homeworks

(Assignments will appear over the course of the semester.)
# Description Date Released Date Due Project Link
Homework 0 Jan 30th Feb 6th link (inactive)

Late Policy

Each students is provided with two 48-hour extensions for the mini-projects (i.e. not the quizzes, midterm exams, or final project). These extensions will be applied automatically to the first two late submissions (i.e. students do not choose which assignments use up their late allowance). No request (via email or otherwise) is necessary. After two late submissions, no late work will be accepted (i.e. any late work will be given a 0% and will not be graded).


Final Project

We will have one final project, posted as a page on github. Stay tuned!


Additional Administrative Information

Excused Absences

Missing an exam for reasons such as illness, religious observance, participation in required university activities, or family or personal emergency (such as a serious automobile accident or close relative’s funeral) will be excused so long as the absence is requested in writing at least 2 days in advance and the student includes documentation that shows the absence qualifies as excused; a self-signed note is not sufficient as exams are Major Scheduled Grading Events. For this class, such events are the final project assessment and midterms, which will be due on the dates listed in the schedule above. The final exam is scheduled according to the University Registrar.

For medical absences, you must furnish documentation from the health care professional who treated you. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. In addition, it must contain the name and phone number of the medical service provider to be used if verification is needed. No diagnostic information will ever be requested. Note that simply being seen by a health care professional does not constitute an excused absence; it must be clear that you were unable to perform your academic duties.

It is the University’s policy to provide accommodations for students with religious observances conflicting with exams, but it is the your responsibility to inform the instructor in advance of intended religious observances. If you have a conflict with a planned exam, you must inform the instructor prior to the end of the first two weeks of the class.

The policies for excused absences do not apply to project assignments. Projects will be assigned with sufficient time to be completed by students who have a reasonable understanding of the necessary material and begin promptly. In cases of extremely serious documented illness of lengthy duration or other protracted, severe emergency situations, the instructor may consider extensions on project assignments, depending upon the specific circumstances.

Besides the policies in this syllabus, the University’s policies apply during the semester. Various policies that may be relevant appear in the Undergraduate Catalog.

If you experience difficulty during the semester keeping up with the academic demands of your courses, you may consider contacting the Learning Assistance Service in 2201 Shoemaker Building at (301) 314-7693. Their educational counselors can help with time management issues, reading, note-taking, and exam preparation skills.

Right to Change Information

Although every effort has been made to be complete and accurate, unforeseen circumstances arising during the semester could require the adjustment of any material given here. Consequently, given due notice to students, the instructors reserve the right to change any information on this syllabus or in other course materials. Such changes will be announced and prominently displayed at the top of the syllabus.

University of Maryland Policies for Undergraduate Students

Please read the university’s guide on Course Related Policies, which provides you with resources and information relevant to your participation in a UMD course.

Miscellaneous Resources

As we go through the course sometimes I will mention additional resources or next steps. None of this is required for the course, but students have asked for me to keep a record of which texts/websites I mention.

  • Python for Data Analysis covers some of the same topics that we cover in this class, but in textbook form.
  • Data Science From Scratch this book is meant to give you a grounding in how some of the statistics and math could be implemented. Mostly, data scientists use off-the-shelf libraries for their mathematical routines/functionality, this books works through how you would implement some of those libraries. The idea is that this might give you a deeper understanding of what is going on. The implementations in this book are not meant to be high-performance, or industrial strength, but illustrative.
  • Web Scraping 101 is a good overview to how web scraping works.
  • Recent iterations of this course can be found for the Fall 2019 (Dickerson), Fall 2020 (Dickerson), Spring 2021 (Calderón Trilla), and Summer 2021 (Calderón Trilla) semesters.