Course Information
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DS 256 - Data Science Programming Course Information |
The data scientist applies methods from statistics, data
analysis, computer science, and machine learning in order to gain insight from
data. In Data Science Programming,
we focus on developing the programming and machine learning skills necessary to
gain such insight. After first
exploring and cleaning raw data, we iteratively improve upon the choice and
engineering of data features for the machine learning of models from such data
features. Our machine learning
models enable us to predict from future data and/or help us discern patterns in
our data. Throughout our data science workflow, we perform scientific
visualization to aid in discerning improvements to and insights from our
modeling. Through experiential
learning, we equip students with the fundamental computer problem-solving skills
and tools of the data scientist.
You are responsible to know the material from each lecture and reading assignment before the start of the next class. Homework is due at the beginning of lecture on the due date. Late homework will not necessarily be accepted. Code must be a legal program in the relevant language in order to be graded. (It need not be free from logic errors.)
Because of the tight grading turnaround of peer grading and the cumulative nature of the material, there is no policy for accepting late work. When work is due, it will be collected and graded as-is.
Class attendance and participation is required. If you attend all classes and are willing to participate, you'll get 100% for this part of your grade. Even if you know enough to give a particular lecture, please consider the value of helping your peers during in-class exercises.
Woody Allen is quoted as saying "80% of success is just showing up." While our class attendance/participation grade is not 80% of the final grade, it is critical that late arrivals and unexcused absences are not excessive. Missing more than half of class unexcused is considered being absent. An unexcused late arrival is counted as a half absence. If the total number of absences counted this way exceeds 20% of class meetings, i.e. 6 absences or more, the student will have failed the course.
You are expected to work an average of 8 hours per week beyond class time. Gettysburg College policy, in accordance with federal and state standards, equates 1 credit unit with an average of 12 hours of work per week with 50 minute classes counting as 1 full hour of work. During these remaining 8 hours beyond class, a student is expected to learn from assigned readings, complete exercises related to such readings, attend required colloquia, and complete assignments.
Think of your college studies as a more-than-full-time job, and engage in it with passion. After all, you get out of it what you put into it, and it is my hope that you'll gain much from your investment in this course. If you'd like to learn more about how to better track tasks and manage time as a student, consider watching my short tutorial on getting things done.
What is permitted:
What is not permitted:
Put simply, students may discuss assignments at an abstract level (e.g. specifications, algorithm pseudocode), but must actually implement solutions independently or in permitted groups. Credit should be given where credit is due. Let your conscience be your guide. Do not merely focus on the result; learn from the process.