Intro. Comp. for Data Science
Learning by doing is the key
Learning by doing is the key
Dr. Nono Saha Cyrille Merleau - nonosaha@mis.mpg.de
Lecture
Seminars
The goal of both the lectures and the seminars is for you to be as interactive as possible. My role as instructor is to introduce you new tools and techniques, but it is up to you to take them and make use of them. Programming is a skill that is best learned by practicing, so as much as possible you will be working on a variety of tasks and activities throughout each lecture/seminar. Attendance will not be taken during class but you are expected to attend all lecture and seminar sessions and meaningfully contribute to in-class exercises and homework assignments.
You will be assigned larger programming tasks throughout the semester (roughly every two weeks). These assignments will be completed either in a team or individually.
Students are expected to make use of the provided git repository on the course's github page as their central collaborative platform. Commits to this repository will be used as a metric (one of several) of each team member's relative contribution for each homework.
There will be a two midterms that you are expected to complete individually. Each project will ask you to complete a number of small programming tasks related to the material presented in the class. The exact structure and content of the projects will be discussed in more detail before they are assigned. You must attempt *both* projects in order to pass this class.
We will form team of 3-5 students together. The teams will be responsible for the completion of an open ended final project for this course, the goal of which is to tackle an "interesting" problem using the tools and techniques covered in this class. Additional details on the project will be provided as the course progresses. You must complete a final project in order to pass this course.
For all of the team based assignments in this class you will be randomly assigned to teams of 3 to 5 students - these teams will change after each assignment. You will work in these teams during your scheduled labs. For team based assignments, all team members are expected to contribute equally to the completion of each assignment and you will be asked to evaluate your team members after each assignment is due. Failure to adequately contribute to an assignment will result in a penalty to your mark relative to the team's overall mark.
We will regularly send course announcements via email and Sakai, make sure to check one or the other of these regularly.
The University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Cheating on exams or plagiarism on homework assignments, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Community Standard, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved. Additionally, there may be penalties to your final class grade along with being reported to the University Conduct Board.
Please, review the course policies that follows:
A note on sharing / reusing code - I am well aware that a huge volume of code is available on the web to solve any number of problems. Unless I explicitly tell you not to use something the course's policy is that you may make use of any online resources (e.g. StackOverflow) but you must explicitly cite where you obtained any code you directly use (or use as inspiration). Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism. The one exception to this rule is that you may not directly share code with another team in this class, you are welcome to discuss the problems together and ask for advice, but you may not send or make use of code from another team.
Students who miss a class due to a scheduled varsity trip, religious holiday or short-term illness should simply sent me an email. Note that these excused absences do not excuse you from assigned homework, it is your responsibility to make alternative arrangements to turn in any assignments in a timely fashion. After three consecutive absences without excuses, I will consider that the student gave up on the course.
Those with a personal emergency or bereavement should speak with the director or the academic dean, or the faculty member in charge of the DS Master students
Your final mark will be comprised of the following
| Assignment | Value |
|---|---|
| Homeworks | 60% |
| Group projects | 40% |
| Course participation | 0% |
Your homeworks will be comprised of the following:
| Assignment | Value |
|---|---|
| Instructions | 20% |
| Coding | 40% |
| Testing | 20% |
| Results presentation | 20% |
Your groupworks will be comprised of the following:
| Assignment | Value |
|---|---|
| Git structure and repository | 20% |
| Slide organisation | 20% |
| Content | 20% |
| Oral presentation | 20% |
| Answers to questions | 20% |
The exact ranges for letter grades will be curved and cutoffs will be determined at the end of the semester. The more evidence there is that the class has mastered the material, the more generous the curve will be.
The course prepares Master's students for statistical computing for Data Science and ML. It provides them with the computational tools and basis of python programming language to tackle machine learning and data science problems. The materials are taken from Duke university and adapted to the University of Leipzig curriculum.