Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. [source]
Desired Learning Outcomes:
Students will be able to manipulate data to extract subsets, sort, and compute derived quantities.
Students will be able to manipulate data to produce visualizations.
Students will be able to compute statistical quantities from a real-world dataset.
Students will be able to sample from a dataset and construct empirical confidence intervals from it.
Students will be able to perform bootstrap resampling from a dataset and construct empirical confidence intervals.
Students will be able to perform linear regression and inference from it.
Students will be able to perform classification.
Students will be able to state the privacy, ownership, and ethical issues arising in data science.
(MATH 1060, 1090, 1101, 1200, 1250, 1260, 1322, or 1500) or (Math placement 2 or higher)
We **will** have a class Teams team.
The main activities there are in channels:
As questions here so that others get the benefit of your questions too.
Respond to others' questions to help them figure it out. (Posting solutions to homework questions is not being helpful.)
I will answer in the channel so that everyone gets the benefit.
Along with in person office hours, I will run them as Teams meetings.
Computational and Inferential Thinking: The Foundations of Data Science.,
Ani Adhikari and John DeNero. Available (for free) at
You are allowed 5 absences (out of 41 classes) without penalty; these include university excused absences for illness, death in the immediate family, religious observance, jury duty, or involvement in University-sponsored activities. Each additional absence will reduce your final average by 0.5%. Your attendance record will be available in Blackboard.
There is a homework assignment due in most weeks. Late homework is penalized 10% per day (or part
thereof) late. Your lowest two scores are dropped.
In 10 of the weeks, the Tuesday lab meeting will be used for (computer) laboratories, to be completed during that time. Missed labs cannot be made up, but your lowest score is dropped.
There are three substantial, multi-week projects during the semester. Some time in the Tuesday lab meetings will be used to work on projects. You can do your project alone or with one partner.
Tests and Exam:
There will be one mid-term test. The final exam is on **TBD**.
Your grade is based on the labs 10%, the homework 20%, the projects 25%, the midterm test 15%, and the final exam 30%.
An average of 90% guarantees you at least an A-,
80% a B-, 70% a C-, and 60% a D-.
You are allowed to use most resources, but there are some limitations.
Unlimited use, without specific acknowledgment:
Discussions with me.
Your partner, for the project.
Broad use, with acknowledgment:
Websites on statistics, data science, etc.
Explanations by other students in this class.
Explanations by friends, roommates etc.
Acknowledge and describe this help in writing on the
problem where it was used. For example, you might write
"[Name] explained to me how to do [some part] of this
problem" or "I found an explanation of [concept] at the
The work or programs from students who took this class (in any of its versions at any university).
Websites that claim to have homework, lab, or project solutions for this class.
If you are not sure if something is allowed, then ask me first.
A minor, first-time violation of
this policy will receive a warning and discussion and
clarification of the rules.
Serious or second violations will
result in a grade penalty on the assignment. Very serious or
repeated violations will result in failure in the class and be
reported to the Office of
Community Standards and Student Responsibility, which may
impose additional sanctions. You may appeal any sanctions through
the grade appeal process.
If you have specific physical,
psychiatric, or learning disabilities and require
accommodations, please let me know as soon as possible so that
your learning needs may be appropriately met. You should also
register with Student Accessibility
Services to obtain written documentation and to learn about
the resources they have available.
Responsible Employee Reporting Obligation:
If I learn of any instances of sexual harassment, sexual
violence, and/or other forms of prohibited discrimination, I
am required to report them. If you wish to share such
information in confidence, then use
of Equity and Civil Rights Compliance.
Schedule (Subject to change)
DRAFT!! Subject to change. Textbook links are active now; other links will become
active nearer their date.