CMSC320 – Fall 2018

Introduction to Data Science

Data Science!?

Instructors: John P. Dickerson and Prem Saggar
TAs: Eleftheria Briakou, Hao Chen, Susmija Jabbireddy, Harihara Muralidharan, Renkun Ni, Nischal Reddy (UGTA), Yanchao Sun
Lectures: Monday and Wednesday, 2:00–3:15 PM, ESJ 0224

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—just ask Nate Silver!

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.


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 one written, in-class midterm examination. 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 (subject to change!), 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:

You can earn full credit for class participation in three ways:

  1. Lecture participation, asking questions and answering your peers questions;
  2. Piazza participation, asking and answering questions on Piazza; and
  3. Regular attendance at office hours.
To earn full credit you should aim to ask or answer a question at least once every two weeks in lecture or on Piazza; or attend office hours at least once a month (this can include just going to my office hours to chat about computer science, data, science, software engineering, etc.).

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:!

Office Hours & Communication

For course-related questions, please use Piazza to communicate with your fellow students, the TAs, and the course instructors. For private correspondance or special situations (e.g., excused absences, DDS accomodations, etc), please email John with [CMSC320] in the email subject line.

Office Hours
Human Time Location
Eleftheria Briakou Mondays, 11:45am–1:45pm AVW 1120
Hao Chen Fridays, 3pm–5pm AVW 1120
John Dickerson TBD 1hr/wk. Also by appointment; please email John with [CMSC320] in the email subject line. AVW 3217
Susmija Jabbireddy Thursdays, 3pm–5pm AVW 1120
Harihara Muralidharan Mondays, 10am–12pm AVW 1120
Renkun Ni Wednesdays, 11:00am–1:00pm AVW 1120
Nischal Reddy Thursdays, 9am–10am AVW 1120
Prem Saggar By appointment; please email Prem with [CMSC320] in the email subject line.
Yanchao Sun Tuesdays, 10am–12pm AVW 1120

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: 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.

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 to complete their evaluations.


(Schedule subject to change as the semester progresses!)
# Date Topic Reading Slides Lecturer Notes
1 8/27 Introduction What the Fox Knows. pdf, pptx Dickerson Sign up on Piazza!
Part I: Data Collection, Storage, & Management
2 8/29 Scraping Data with Python Anaconda's Test Drive. pdf, pptx Dickerson PDF download script from class: link
3 9/5 NumPy, SciPy, & DataFrames Introduction to pandas. pdf, pptx Dickerson Pandas tutorials: link
4 9/10 Best practices & version control pdf, pptx Dickerson Workflows for git: link
5 9/12 Data Wrangling I: Pandas & Tidy Data Hadley Wickham. "Tidy Data." pdf, pptx Dickerson Hould's Tidy Data for Python
6 9/17 Data Wrangling II: Tidy data & SQL Derman & Wilmott's "Financial Modelers' Manifesto." pdf, pptx Dickerson SQLite: link; pandasql library: link
7 9/19 Quality Assurance (QA) ipynb Saggar "High Availability" & 9's rule
8 9/24 Missing Data (Theory) Pandas tutorial on working with missing data. pdf, pptx Dickerson Scikit-learn's imputation functionality: link
9 9/26 Missing Data (Practice) ipynb Saggar We'll try to have the .ipynb up before lecture; if so, please feel free to download it and bring a laptop to class!
10 10/1 Data Wrangling Wrap-Up: Data Integration, Data Warehousing, Entity Resolution Data Cleaning: Problems and Current Approaches (Note: this is a reference piece; please don't read the whole thing!) pdf, pptx Dickerson Wikipdia article on outliers
11 10/3 Exploratory Data Analysis: Summary Statistics, Transformations, & Visualization John W. Tukey: His Life and Professional Contributions. pdf, pptx Dickerson Seaborn visualization library for Python: link
12 10/8 Basic Probability, Causation & Correlation, Hypothesis Testing pdf, pptx Saggar
13 10/10 Basic Probability, Causation & Correlation, Hypothesis Testing pdf, pptx Saggar Excel spreadsheet covering hypothesis testing example from class: link
14 10/15 Data Wrangling Wrap-Up: Graphs Introduction to GraphQL: link pdf, pptx Dickerson NetworkX: link
15 10/17 Basic Probability & Bayes' Theorem pdf, pptx Saggar Excel spreadsheet (second one!) covering hypothesis testing example from class: link
16 10/22 Midterm Review & Raw Text/NLP Intro pdf, pptx Dickerson Sample midterm from Spring 2017 was also posted on Piazza earlier.
17 10/24 Midterm Dickerson Bring a pen!
18 10/29 NLP I NLTK Book. pdf, pptx Dickerson Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link
19 10/31 NLP II Continued from last class ... pdf, pptx Dickerson Continued from last class ...
20 11/5 Decision Trees pdf, pptx Saggar
21 11/7 Decision Trees pdf, pptx Saggar Excel file from class: link
22 11/12 Introduction to Machine Learning, & Linear Regression Hal Daumé III. A Course in Machine Learning. pdf, pptx Dickerson Scikit-learn cheat sheet: link
23 11/14 Gradient Descent pdf, pptx Saggar Gradient descent example from class: xlsx file
24 11/19 Random Forests, SVM pdf, pptx Dickerson Tensorflow: link
11/21 Thanksgiving Break
25 11/26 Neural Nets I pdf, pptx Saggar
26 11/28 Neural Nets II pdf, pptx Saggar
27 12/3 Debugging Data Science pdf, pptx Dickerson
28 12/5 Data Science In Industry ipynb, pdf, pptx Saggar
29 12/10 Course Wrap Up & Research pdf, pptx Dickerson
Final 12/15 Final Exam Date Final versions of tuturials must be posted by 1:30PM, the exam time. Instructions & rubric: link

Mini-Projects né Homework

In addition to the tutorial to be posted publicly at the end of the semester, there will be four "mini-projects" assigned over the course of the semester (plus one simple setup assignment that will walk you through using git, Docker, and Jupyter). The best way to learn is by doing, so these will largely be applied assignments that provide hands-on experience with the basic skills a data scientist needs in industry.

Posting solutions publicly online without the staff's express consent is a direct violation of our academic integrity policy. Late assignments will not be accepted.

(Assignments will appear over the course of the semester.)
# Description Date Released Date Due Project Link
0 Setting Things Up August 27 September 7 link
1 Fly Me To The Moon September 13 September 28 October 1 link
2 Moneyball October 1 October 19 October 21 link
3 Fact Tank October 28 November 19 November 28 link
4 Baltimore Crime November 26 December 7 link