DS1300: A Practical Introduction to Data Science#
Contact Information#
Instructor |
Phone |
Office |
Office Hours |
|
|---|---|---|---|---|
Dr. Robert Kalescky |
8-3070 |
Ford Hall 119 |
By Appointment |
|
Dr. Eric Godat |
8-4599 |
Ford Hall 119 |
By Appointment |
Students will be encouraged to use the course Teams channel for communication with instructors and their fellow classmates. This will be especially important when communicating with group members while working on the semester project.
Class information and assignments will be posted in Canvas.
Useful Links#
Dates and Times#
May Term, May 11 through 25 10:00 AM - 12:00 PM, 1:00 PM - 3:00 PM
Course Overview#
We live in a world driven by computers, algorithms, and data. This course seeks to equip students with the basic knowledge and skills to not only understand but to use the tools needed to model and make decisions about the world around them. Students will be introduced to basic programming in Python, high-performance computing using ManeFrame II, and data science. This course is not a programming course, though programming will be required to complete assignments. Prior knowledge of programming is neither expected nor required.
Learning Outcomes#
At the end of the course you will be able to:
Identify applications of data science in today’s world
Identify the privacy and ethical issues associated with the use of data
Clearly and concisely communicate data analyses via visualization and meaningful metrics
Have had exposure to computational thinking, programming, and high-performance computing
University Requirement Fulfillment#
UC Requirement (matriculation Fall 2016 to Spring 2020)#
This course meets a UC Breadth requirement for Technology and Mathematics
1b Students will demonstrate an ability to analyze complex mathematical problems that arise in a particular discipline or area
1c Students will demonstrate an understanding of how particular technologies work
CC Requirement (for students entering Fall 2020 or later)#
This course fulfills the CC Breadth requirement for Technological Advances and Society (TAS) by introducing students to the technology used in computational and data sciences, how this technology affects society by highlighting its use in different domains, and discussing issues like privacy and ownership of information.
Required Materials#
For this course, you will need a computer that has internet access. All programming will be done on through SMU’s OnDemand Portal so the specifications of your machine are minimal as long as you can connect to an internet browser.
The required reading for this class will be assigned out of:
Hello World: Being Human in the Age of Algorithms by Hannah Fry
Suggested readings will come from an assortment of sources including:
Introduction to Computation and Programming using Python by John V. Guttag
Course Structure#
This course will be lecture based with in-class activities designed to demonstrate and reinforce concepts. Given the accelerated nature of short semester courses, a given day will be divided into lecture, coding demonstrations, hands-on programming exercises, and collaborative work sessions. Students will be expected to complete homework assignments after class, though these assignments will follow closely from what is covered in class. Participation in class is expected and will be critical to student success.
This course is not a programming course, though programming will be required to complete assignments. Prior knowledge of programming is neither expected nor required. The instructors will equip students with the skills and tools needed to complete each assignment and be available to provide programming support should need arise.
The course also contains a group project that will span the length of the semester. Students will be divided into teams by the instructors. These groups will work together for the duration of the semester. Group work will be evaluated as a sum of individual efforts and, as such, each member is expected to contribute equally to the success of the project. Issues within teams should be brought to the instructors attention immediately so they can be resolved quickly.
Evaluation#
In-class coding assignments, homework sets, and a semester-long group project will be used to evaluate understanding and proficiency with the material.
Category |
Percentage |
|---|---|
In-class Participation |
20% |
Homework Problems |
40% |
Semester Project |
40% |
The semester project will culminate in a group presentation to the class during the time designated by the University Registrar for the final exam. This presentation will involve a question and answer session from peer students, attendees, and the instructors. Students will be graded on their presentation skills, content, ability to answer questions, and engagement in other student presentations.
Syllabus#
Each calendar date on the syllabus corresponds to 4 lecture hours, in line with the structure of a May Term semester.
Day 1
High-performance Computing
Using ManeFrame II
Using GitHub
Day 2
Data Science Overview
Using Jupyter
Python Programming I
Day 3
Ethics and Bias in Data
Python Programming II
Working with Data
Day 4
Exploring and Working with Data
Story Telling with Data
Day 5
Data and Justice and Medicine
Project Work Day
Day 6
Intermediate Project Presentations
Using Dask
Day 7
Data and Cars and Crime
Data Storage
Building Models and Making Predictions
Day 8
Visualizing Various Types of Data
Using Geopandas
Day 9
Data and Art
Text Mining
Customizing Analysis Functions
Metric Building
Day 10
Project Work Day
Day 11
Project Presentations
Semester-Long Project#
Each student will be assigned a semester-long project that will require the student to apply the methods and tools discussed in the class to a unique dataset. The goal of the project is to reinforce core data science concepts and techniques through application.
University Policies#
Disability Accommodations: Students needing academic accommodations for a disability must first register with Disability Accommodations & Success Strategies (DASS). Students can call 214- 768-1470 or visit: http://www.smu.edu/Provost/ALEC/DASS to begin the process. Once registered, students should then schedule an appointment with the professor as early in the semester as possible, present a DASS Accommodation Letter, and make appropriate arrangements. Please note that accommodations are not retroactive and require advance notice to implement.
Religious Observance: Religiously observant students wishing to be absent on holidays that require missing class should notify their professors in writing at the beginning of the semester, and should discuss with them, in advance, acceptable ways of making up any work missed because of the absence. (See University Policy No. 1.9.)
Excused Absences for University Extracurricular Activities: Students participating in an officially sanctioned, scheduled University extracurricular activity should be given the opportunity to make up class assignments or other graded assignments missed as a result of their participation. It is the responsibility of the student to make arrangements with the instructor prior to any missed scheduled examination or other missed assignment for making up the work. (University Undergraduate Catalogue)
Student Learning Outcomes: Please include in your syllabi all student learning outcomes, both those specific to your course, as well as those that satisfy major and general education requirements.
Final Exams: Final course examinations shall be given in all courses where they are appropriate, and some form of final assessment is essential. Final exams or final assessments must be administered as specified in the official examination schedule, and shall not be administered during the last week of classes or during the Reading Period. Please state clearly in the syllabus the date/time and form of the final exam or assessment.