The class web site is available at https://ycp.powersoftwo.org . Current office hours are at that site. Send email to rsnyder9@ycp.edu

An introduction to data science from a computer science perspective. Includes an introduction to the programming language used in the course, numerical computation, essential probability and Bayesian statistics concepts, structured and unstructured text and data processing, data and information visualization, machine learning using examples such as Naive Bayes, regression, decision trees, clustering, mixture models and topic modeling.

Completed at least two 300 level courses in computer science with at least 89.0 credit hours.

Python Data Science Handbook: Essential tools for working with data. Jake VanderPlas. O'Reilly. ISBN-13: 978-1491912058.

This class will cover some of the important concepts underlying data science. In general, the course will include programming (Python), data acquisition and preparation, some probability and statistics, data and information visualization, and various aspects of machine learning.

I expect that you start the course with a solid grasp of programming.

The course will consist primarily of lecture and discussion, with occasional in-class lab activities.

There will be a number of assignments, of which some will be dropped.

There will be a number of quizzes, of which some will be dropped.

There will be some in-class midterm exams.

A required final exam will be given, as scheduled by the Registrar.

The learning outcomes for this course include, but are not limited, to the following.

• Understanding of data science in general, including business and decision aspects, and of computer science aspects of data science in particular.
• Understanding of programming in general and for data science in particular, using Python.
• Understanding of needed aspects of probability and statistics to understand and use data science.
• Understanding aspects of data representation, acquisition, manipulation, preparation and use for data science.
• Understanding aspects of some application areas of data science in machine learning for data science.

There will be a number of assignments, of which some will be dropped.

There will be a number of quizzes, of which some will be dropped.

There will be some in-class midterm exams.

A required final exam will be given, as scheduled by the Registrar.

Grades are assigned on a 1000 point scale.

At the end of the semester, the scale may be curved (upwards) if appropriate.

Your overall grade for the course will be determined as follows where percentages are approximate and may be adjusted as needed.

• Assignments: 30%
• Quizzes: 20%
• Midterm exams: 30%
• Final exam: 20%

No make-up quizzes or exams will be given. In the case of quizzes, there are dropped quiz scores to account for not being able to make a quiz. There may be unannounced quizzes to provide additional quiz scores from which to drop. In the case of exams, the final exam is cumulative and required. Any exam score average that is below the final exam average is raised to the average of the final exam score average. So a missed exam, for any reason, will have the same average as the final exam average.

There will be some drops of lowest quizzes and assignments.

Assignments are due on the date specified on the calendar. After that date, there is a 20% penalty for being late if that assignment is submitted before the next class. If the solution is covered in that next class, no further submissions of that assignment are permitted. Otherwise, there is a 20% penalty per week, not to extend beyond the last day of class. Assignments will be posted on the course web page.

Assignments are submitted via the class web site. Assignments can be submitted multiple times. However, the on-time submission nearest to the submission date is the one graded. Otherwise, the late assignment closest to the submission date is graded.

Please check the course web page, https://ycp.powersoftwo.org , regularly for important announcements.

Any reading assignments posted in the schedule on the course web page should be read and studied before class and then again after class. You are required to read the scheduled material before coming to class.

Class notes should be read and studied before and after class. Major changes to notes for a given class can happen until after a class meeting. Minor changes to those notes can happen after that class meeting.

Most of the material covered builds on previous material covered, so do not fall behind.

You must make a legitimate attempt to complete every assignment and/or lab requirements. I reserve the right to fail any student who does not make a good faith effort to complete all of the homework assignments.

Assignments and labs will be available for download no later than the day after the previous assignment or lab was due.

Assignments will be submitted from the class web site. You will be immediately able to see your submission status and can download that submission to insure it was submitted correctly.

I expect you to attend class and participate regularly in class activities. If you miss a class, please notify me in advance. You are responsible for all material covered in class, regardless of whether or not you were present. In addition to missing class material coverage, missed class may result in missed quizzes, etc.

Attendance is taken and categorized as follows.

• present all of class
• arrived late
• departed early
• not present for class

In the case of ambiguous cases (e.g., arrived late and departed early) the professor will make a judgment on into which category the attendance is recorded.

You are responsible for keeping up with the reading assignments as described in the course schedule.

Because the individual assignments are essential for working towards and demonstrating the achievement of the course outcomes, you must solve them on your own (unless group work is explicitly permitted).

Quizzes, exams, and the final exam must be completed individually, in the classroom.

Unless group work is permitted for an assignment (or lab), all work for assignments (and labs) should be individual (i.e., by yourself). Any help received should be added as comments near the top of the program or work. Help from the professor does not need to be noted.

You may discuss the problem and high-level (pseudo-code) approaches to solving the problem with other students. You may not, under any circumstances, discuss or share concrete implementation techniques or code. Examples of forbidden types of collaboration include, but are not limited to: looking at another student's code, allowing another student to see your code, viewing and/or using code from an external source such as a web page, discussing the use of specific API functions to solve a problem, giving or receiving help debugging specific code, etc.

Note that since submissions are electronic, that submitted work can be electronically compared with other student's work to determine academic integrity violations (in a manner similar to many plagiarism systems for written work).

Any violation of the course's academic integrity policy will be referred to the Dean of Academic Affairs, and could have consequences ranging from a 0 on an assignment to dismissal from the college.

If you had an IEP or 504 plan in high school or if you have a disability or health condition that impacts you in the classroom, please contact Linda Miller, Director of Disability Support Services, at 815-1785 or lmille18@ycp.edu to discuss obtaining the accommodations for which you may be eligible. If you already have an accommodation memo and wish to access your accommodations in this class, please see me confidentially to discuss.

While York College recognizes students’ need for educational and emergency-related technological devices such as laptops, PDAs, cellular phones, etc., using them unethically or recreationally during class time is never appropriate. The college recognizes and supports faculty members’ authority to regulate in their classrooms student use of all electronic devices.

York College recognizes the importance of effective communication in all disciplines and careers. Therefore, students are expected to competently analyze, synthesize, organize, and articulate course material in papers, examinations and presentations. In addition, students should know and use communication skills current to their field of study, recognize the need for revision as part of their writing process, and employ standard conventions of English usage in both writing and speaking. Students may be asked to further revise assignments that do not demonstrate effective communication.