Special Topics – Programming in
Python for Data Science
INFO197P
– UMass Amherst – Spring 2020
Course
Information
|
Instructor |
Emma Anderson emmaanderson@cs.umass.edu |
|
Time |
W/F 9:05-9:55am, 3/20 – 4/26 |
|
Location |
Engineering Lab Room 325 |
|
Prerequisites |
CS121 |
|
Textbook |
None required |
|
Office Hours |
Fridays 10-11am, CS building room 228 |
Welcome
In this course, each voice in the classroom has something of
value to contribute. Please take care to respect the different experiences,
beliefs and values expressed by students and staff involved in this course. My
colleagues and I support UMass’s commitment to diversity, and welcome
individuals regardless of age, background, citizenship, disability, sex,
education, ethnicity, family status, gender, gender identity, geographical
origin, language, military experience, political views, race, religion, sexual
orientation, socioeconomic status, and work experience.
Course Description
A brief introduction to the Python programming language for
students with a working knowledge of basic programming concepts. This
course is geared towards introductory data science and analytics tasks, and is
intended for Informatics majors. Prerequisite:
COMPSCI 121. Runs for 6 weeks beginning
3/20.
Course
Goals and Objectives
The goal of this course is to provide hands-on experience with Python
programming, with an eye towards performing basic data analytics tasks. At the end of the course, you should be able
to use the Python Numpy and Pandas packages to
perform quantitative analysis on various datasets.
Accommodations
The University of Massachusetts Amherst is committed to
providing an equal educational opportunity for all students. If you have a
documented physical, psychological, or learning disability on file with
Disability Services (DS), you may be eligible for reasonable academic
accommodations to help you succeed in this course. If you have a documented disability
that requires an accommodation, please notify me within the first two weeks of
the semester so that we may make appropriate arrangements.
Grading
This is a 1-credit P/F class.
In order to pass, you must meet the following requirements:
-Attend at least 75% of the class meetings (8 out of 11 meetings;
exceptions can be made for extenuating circumstances on a case-by-case basis)
-Complete all 4 short assignments, assigned in weeks 1-4
-Complete a final project
No grades will be assessed in this course; instead, assignments will
be graded on completion and given written feedback.
As this is a skills-based course, class meetings will consist of
short lectures followed by hands-on work with code. As such, your attendance in class is
critical.
What To Expect
Classes will be a combination of lecture and hands-on code
workshop. Please bring your laptop to
class each day. Since this is a 1-credit
class, you should expect to spend 2-3 hours of time on this course outside of
class each week. This will be more
heavily weighted towards the latter half of the course, so expect to spend 1-2
hours per week at first, and 3-4 hours per week towards the end as you work on
a final project.
Schedule
3/20: Course Introduction, print statements, types, variables Lecture
3/22: Python Lists Lecture
3/27: String & List methods Lecture
3/29: Importing packages, Numpy, introduce
final project Lecture Code
Video Lecture
4/3: Dictionaries, introduction to Pandas and DataFrames
Pandas_Lecture.pdf
4/5: Charts and Graphs Pandas
Visualization Guide
4/10: Data Cleaning
4/12: Machine Learning Techniques
4/17: Machine Learning Techniques 2
4/19: Getting data from the web
4/24: Project sharing
Resources
and other fun stuff
March
Madness Prediction Analysis
Emma's GitHub
– data files used in class