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DIT852 Introduction to Data Science (2019, Period 1)
Master’s Program in Applied Data Science, Gothenburg University
Instructor: Alexander Schliep (alexander.schliep@cse.gu.se)
Department of Computer Science and Engineering (CSE), Gothenburg University | Chalmers

 

Organization:

  • Classes meet Tuesdays and Thursdays from 10:00-11:45. Check time edit for rooms. 
  • Labs are 13:15-15:00 on Thursdays in ED3582 (EDIT building).
  • Office hours: (to be announced)
  • Course website: https://canvas.gu.se/courses/25009
  • Best way to contact instructor is via Email to alexander.schliep@cse.gu.se using “DIT852” as the beginning of the subject line.

 

Course content:

The course gives an introduction to applied data science using case studies from different application domains and a primer in using Python for data science. In particular the following topics will be covered:

  • revision of mathematical and statistical concepts useful in data science, such as basic set theory, logic, and probability theory;
  • case studies of data science applications displaying a range of application areas and fundamental types of analysis problems;
  • a brief introduction to working in a Unix/Linux environment and programming in Python to implement basic transformations, visualizations and analyses;
  • use of a machine learning library from Python to analyze data;
  • demonstration of inherent limitations of computational analyzes with examples;
  • implications on privacy and ethical considerations.

These three large streams—data science fundamentals and methods, data science in Python, and case studies for uses, impact and societal impact of data science will be followed (mostly) concurrently throughout the course.

 

Learning Outcomes:

On successful completion of the course the student will be able to:

  • describe fundamental types of analysis problems arising in data science;
  • give examples of data science applications from different contexts;
  • use Python to implement basic transformations, visualizations and analyses of example data;
  • apply simple machine learning methods implemented in a standard library;
  • identify appropriate types of analysis problems for concrete applications;
  • reflect on inherent limitations of results of data science methods and
  • critically analyze data science applications with respect to ethics and privacy.


Formalia:

  • Credits:The total number of higher education credits (HEC) for the course is 7.5. The course has two sub-courses
    • Written examination (Skriftlig tentamen), 4 higher education credits 
Grading scale: Pass (G) and Fail (U)
    • Written assignments (Skriftliga inlämningsuppgifter), 3.5 higher education credits Grading scale: Pass (G) and Fail (U)
  • Assessment:The course is examined by an individual written exam carried out in an examination hall, as well as mandatory written assignments submitted as written reports, some of which will be carried out individually and others in groups of normally 2-4 students.

    To get a pass (G) in the written assignments a student must get a pass both in the regular assignments and the "Python Programming" assignment individually. In other words good performance in the regular assignments cannot compensate insufficient performance in the programming assignments and vice versa.

    There will be non-obligatory individual assignments (multiple choice questions to be answered with an iClicker device) which grant bonus points for the written exam. These bonus points are only valid in the academic year in which the class was taken. Students are responsible for having the iClicker device loaned to them available during class. It is not possible to make up bonus points.

    If a student who has failed the same examined component twice wishes to change examiner before the next examination, a written application shall be sent to the department responsible for the course and shall be granted unless there are special reasons to the contrary (Chapter 6, Section 22 of Higher Education Ordinance).

    In cases where a course has been discontinued or has undergone major changes, the student shall normally be guaranteed at least three examination occasions (including the ordinary examination) during a period of at least one year from the last time the course was given.
  • Assignments: Note that cell phone pictures of hand-written exercises will not be graded and counted as not submitted. Please typeset your assignment, oder upload scanned versions of handwritten assignments.

    If a student fails the written assignment sub-course: It will only be possible to retake all assignments the next time the course is offered. It is necessary that a request is sent to examiner and instructor four weeks before the start of the period the course is offered in.  
  • Course Evaluation: The course is evaluated through meetings both during and after the course between teachers and student representatives. Further, an anonymous questionnaire is used to ensure written information. The outcome of the evaluations serves to improve the course by indicating which parts could be added, improved, changed or removed.

 

General Rules and Policies

  • Exercise deadlines are firm. Exemptions from deadlines must be requested before the deadline. Unannounced late submissions will not be considered. 

    If late submissions are allowed, 25% of the points are taken off per day late (2 days late = 50% off). No points are taken off per instructor's discretion.
  • It is allowed, even encouraged, to discuss the exercises during the course. Also, do not hesitate to ask if you have difficulties with the exercises, or if something is unclear.
  • You must write your final solutions on your own, using your own words, and expressing them in the way you understood them yourself.
  • Submitting others' work in your own name is cheating! It can lead to severe consequences, in very bad cases even suspension from studies.
  • Specifically, it is prohibited to copy (with or without modifications) from each other, from books, articles, web pages, etc., and to submit solutions that you got from other persons, unless you explicitly acknowledge the sources and add your own explanations. We will be particularly watchful if exercises appear as (alleged) innocent questions in internet forums.
  • You are also responsible for not giving others the opportunity to copy from your work. We will not investigate who copied from whom.

 

Modules:

The course moves through the following modules covering different aspects of the contents and the learning goals.

The labs are particularly focused on Implementing Data Science Solutions by applying concepts from the lectures.


Resources:

Kurssammanfattning:

Datum Information Sista inlämningsdatum