Related: Syllabus

Course Description

CSCI 127. Joy and Beauty of Data. 4 Credits. (3 Lec, 1 Lab) F,S

COREQUISITE: M 151Q Provides a gentle introduction to the exciting world of big data and data science. Students expand their ability to solve problems with Python by learning to deploy lists, files, dictionaries and object-oriented programming. Data science libraries are introduced that enable data to be manipulated and displayed.

To succeed in this course, it is recommended you have a good math background, some basic computer programming experience; if you are unsure about your readiness, our recommendation is to begin with CSCI 107, which provides a gentle introduction to Python and basic programming concepts.

Class Resources

  • A laptop computer is highly recommended. There will be time to work on it in class. Alternatively, the library has laptops they can lend out to students, and the lab rooms have computers you can use. 
  • The online textbook is free.
  • Latest Python and IDLE editor - download site.

Course Outcomes

By the end of this course, students should be be able to:

  • Utilize lists, files, dictionaries and arrays to solve problems in Python.
  • Utilize fundamental object oriented principles such as classes, objects, methods and inheritance to solve problems in Python.
  • Utilize data science libraries to solve data science problems in Python.
  • Understand the broad area of data science and its relevance.

Other Help

Class Policies

Course Materials: The syllabus, course lectures and presentations, and any course materialsprovided throughout this term are protected by U.S. copyright laws.  Students enrolled in the course may use them for their own research and educational purposes.  However, reproducing, selling or otherwise distributing these materials without written permission of the copyright owner is expressly prohibited, including providing materials to commercial platforms such as Chegg or CourseHero.  Doing so may constitute a violation of U.S. copyright law as well as MSU’s Code of Student Conduct.

No cheating - The work you submit to be graded must be your own. Unless it is expressly stated otherwise, out of class assignments (labs, projects, homework) should be done individually. Assignments can be discussed with other students, TAs, or instructors at a high level (verbally, whiteboard or paper, examples), but sharing and reusing written code or finished answers is prohibited. Exams and quizzes must be done individually with no sharing or discussion of solutions.

Late Policy - You will be given one late pass (valid for up to one week) that you may use at your discretion for a LAB assignment. This will be tracked by your TA. The the late pass cannot be used for a program -- there is approximatley one week to complete the programs so submit those EARLY and OFTEN (any subsquent submitals will replace the previous one.) There is no point penalty for using the late pass on a lab.

No rescheduling exams - Please check the syllabus early, and make certain that you will be able to take it at the required time. As a professional student, the only conflict with a priority matching a scheduled exam is another scheduled exam in another class.

Medical and other emergencies - If there is a medical emergency, tragedy or sudden hardship, you will need to provide written confirmation in order to have consideration for an exception to any grading schedules. 

Collaboration Policy

All students should read the MSU Student Conduct Code.

When it comes to Python assignments, you may

  • Work with the other people on your team if teams are allowed. Each assignment will specify the maximum number of people per team.
  • Share ideas with people in other teams.
  • Help other teams troubleshoot problems.

You may NOT

  • Share code you write with other teams.
  • Submit code that someone on your team did not write.
  • Modify another team's solution and claim it as your own.

Failure to abide by these rules will result in an "F" for the course and being reported to the Dean of Students.

Additional MSU Resources