This course will introduce students to the methods and tools used in data science to obtain insights from data. Students will learn how to analyze data arising from real-world phenomena while mastering critical concepts and skills in computer programming and statistical inference. The course will involve hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. This class is ideal for students looking to increase their digital literacy and expand their use and understanding of computation and data analysis across disciplines. No prior programming or math background is required.
Modes of Thinking Requirement: Thinking Quantitatively and Empirically, Thinking Technologically and Digitally
Enrollment Cap: 40 students
Office hours: TBA
Hours will be posted here. More information can be found at https://cs.barnard.edu/cs-help-room
The course contains the following components:
By the end of this course, students will:
There are no textbooks required for this course. The course will follow a modified version of the popular online textbook Computational and Inferential Thinking: The Foundations of Data Science. The modified version will be tailored for this specific Barnard course and will be available online. All additional course readings will be made available on Canvas.
The schedule will be broken into the following units:
During an immersive semester, the course will meet 4 times a week and have lab once a week. During a full semester, the course will meet twice a week and have lab once a week. Deadlines and labs below assume a full semester schedule.
|1||Introduction||Lab 1: Python & Jupyter Introduction|
|2||Causality & Experiments|
|3||Python Introduction, Data Types||Lab 2: Data Types|
|4||Tables||HW 1 due
Causality and Expressions
|5||Visualization||Lab 3: Tables|
|6||Functions & Tables||HW 2 due
Arrays and Tables
|7||Programming Catch up||Lab 4:
Functions & Visualizations
|9||Monty Hall & Probabilities||Lab 5: Randomization||HW 3 due
Tables and Charts
|10||Sampling & Empirical Distributions I||Project 1 due
|11||Sampling & Empirical Distributions II||Lab 6: Sampling|
|12||Hypothesis Testing||HW 4 due
Probability and Sampling
|13||A/B Testing||Lab 7: Assessing Models / Review|
|15||Midterm Review (Catch-up)||Lab catch-up||HW 5 due
|17||Estimation I||Lab 8:
Resampling and Bootstrapping
|19||The Normal Distribution||Project 2 due
|20||Central Limit Theorem||HW 6 due
Confidence Intervals & Sample Size
|21||Prediction I||Lab 9: Correlation, Variance of Sample Means|
|23||Regression Inference||Lab 10: Regression Analysis||HW 7 due
Correlation, Regression, & Least Squares
|25||Classification II||Lab 11: Classification||HW 8 due
Regression Inference, Diagnostics, and Classification
|26||Final Review||Project 3 due
|Grade Criteria||Grade Scale|
|Lab||10%||A||93 - 96|
|Weekly HW||20%||A-||90 - 92|
|Projects||25%||B+||87 - 89|
|Midterm||15%||B||83 - 86|
|Final||25%||B-||80 - 82|
|C+||77 - 79|
|C||73 - 76|
|C-||70 - 72|
Weekly labs will be graded based on attendance and students will receive full credit by working on the lab assignment until finished or until the end of lab period. Students may opt out of attending the lab in person but must complete and submit it by the end of the week (Friday 11:59pm).
This course is based on the popular Data 8: The Foundations of Data Science at Berkeley. Variations of this course have been taught at:
Cornell University, UIUC, Boise State University, University of Maryland, University of Virginia, and others
Approved by the student body in 1912 and updated in 2016, the Code states:
We, the students of Barnard College, resolve to uphold the honor of the College by engaging with integrity in all of our academic pursuits. We affirm that academic integrity is the honorable creation and presentation of our own work. We acknowledge that it is our responsibility to seek clarification of proper forms of collaboration and use of academic resources in all assignments or exams. We consider academic integrity to include the proper use and care for all print, electronic, or other academic resources. We will respect the rights of others to engage in pursuit of learning in order to uphold our commitment to honor. We pledge to do all that is in our power to create a spirit of honesty and honor for its own sake.
More information about the honor code can be found at https://barnard.edu/honor-code
It is important for undergraduates to recognize and identify the different pressures, burdens, and stressors you may be facing, whether personal, emotional, physical, financial, mental, or academic. We as a community urge you to make yourself--your own health, sanity, and wellness--your priority throughout this term and your career here. Sleep, exercise, and eating well can all be a part of a healthy regimen to cope with stress. Resources exist to support you in several sectors of your life, and we encourage you to make use of them. Should you have any questions about navigating these resources, please visit these sites:
If you believe you may encounter barriers to the academic environment due to a documented disability or emerging health challenges, please feel free to contact me and/or the Center for Accessibility Resources & Disability Services (CARDS). Any student with approved academic accommodations is encouraged to contact me during office hours or via email. If you have questions regarding registering a disability or receiving accommodations for the semester, please contact CARDS at (212) 854-4634, email@example.com, or learn more at barnard.edu/disabilityservices. CARDS is located in 101 Altschul Hall.
All students deserve to be able to study and make use of course texts and materials regardless of cost. Barnard librarians have partnered with students, faculty, and staff to find ways to increase student access to textbooks. By the first day of advance registration for each term, faculty will have provided information about required texts for each course on CourseWorks (including ISBN or author, title, publisher, copyright date, and price), which can be viewed by students. A number of cost-free or low-cost methods for accessing some types of courses texts are detailed on the Barnard Library Textbook Affordability guide (library.barnard.edu/textbook-affordability). Undergraduate students who identify as first-generation and/or low-income students may check out items from the FLIP lending libraries in the Barnard Library (library.barnard.edu/flip) and in Butler Library for an entire semester. Students may also consult with their professors, the Dean of Studies, and the Financial Aid Office about additional affordable alternatives for having access to course texts. Visit the guide and talk to your professors and your librarian for more details."