CIS241 Intro to Data Science
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  • Course Schedule
  • Assignments
  • Course Policies
  • Ground Rules
  • JupyterHub
  1. Course Information
  2. 2  Course Schedule
  • 1  Homepage
  • Course Information
    • 2  Course Schedule
    • 3  Course Policies
    • 4  Ground Rules
  • Assignments
    • 5  Documentation Assignment
    • 6  Project Planning Document
    • 7  Presentation
    • 8  Final Project
  • Guides
    • 9  JupyterHub
    • 10  Python
    • 11  Pandas
    • 12  Altair
  • Other Key Info
    • 13  How to Explain in CIS241
    • 14  R.A.D. C.A.T.
    • 15  Work Acquistion and Gender in the Museum of Modern Art
    • 16  Advanced Jupyter Setup
  1. Course Information
  2. 2  Course Schedule

2  Course Schedule

Date Topics & Slides Workshops Assignments Supplemental Reading
26 & 28 Aug. What Is Data? & Responsible Data Collection Jupyter Bruce Ch. 1 (Sakai)
2 & 4 Sept. Python & Data Wrangling 0: Practice Python McKinney, Ch. 3, 5, and 8; Downey Ch. 7
9 & 11 Sept. Exploratory Data Analysis: Summary Statistics & Data Types 1: Movies 1 Documentation Assignment Due 11 Sept. Downey Ch. 2; McKinney Ch. 11
16 & 18 Sept. Exploratory Data Analysis: Visualization 1: Movies 2 UW Ch. 1, 2, 3, & 6
23 & 25 Sept. Hypothesis Testing: Comparison of Means 2: Sports 1 (Begin Test 1) Downey Ch. 11 and 13
30 Sept. & 2 Oct. NO CLASS TUESDAY; Hypothesis Testing: Correlation 2: Sports 2 Test 1 2 Oct. Downey Ch. 9
7 & 9 Oct. Supervised Learning: Regression 3: Business 1 Downey Ch. 10; McKinney Ch. 12.4
14 & 16 Oct. Supervised Learning: Regression 3: Business 2
23 Oct. NO CLASS TUESDAY (FALL BREAK); Ethical Data Science 3.5: Ethics
28 & 30 Oct. Supervised Learning: Classification 4: Health 1 Project Planning Assignment Due 30 Oct.
4 & 6 Nov. Supervised Learning: Classification 4: Health 2 (Begin Test 2)
11 & 13 Nov. Unsupervised Learning: Clustering 5: Literature 1 Test 2 13 Nov.
18 & 20 Nov. Unsupervised Learning: Dimension Reduction 5: Literature 2
25 Nov. Work on Final Projects; NO CLASS THURSDAY (THANKSGIVING) Video Presentations Due 25 Nov.
2 & 4 Dec. Discuss Projects & Videos, Sample Final Project
Saturday 13 Dec. 2-5pm Panel Discussions Final Project Due 13 Dec. at 2pm

2.1 Textbooks:

There are no required textbooks for this course. All textbooks are free online or made available to you on Sakai.

  • McKinney, Python for Data Analysis
  • UW, Visualization Curriculum for Altair
  • Downey, Elements of Data Science
  • Bruce, Bruce, and Gedeck, Practical Statistics for Data Scientists 2nd ed.

2.1.1 Note on the schedule

Keep in mind that some of this schedule could change throughout the semester. However, if anything changes I’ll update this page, and I’ll be sure to give you plenty of advance notice.

2.2 Software

All projects in this course will be scripted and analyzed using Python, an open source programming language and environment. Specifically, we will be using JupyterHub as our programming environment. No previous experience with Python, statistical software packages, or computer programming is required.

1  Homepage
3  Course Policies