Course

Introduction to Data Science (DAT540)

Facts

Course code DAT540

Credits (ECTS) 10

Semester tution start Autumn

Language of instruction English

Number of semesters 1

Exam semester Autumn

Time table View course schedule

Literature Search for literature in Leganto

Introduction

The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.

Content

The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors, and industries. In this course, students become familiar with basic tools and processes used in Data Science. Students work through the whole data lifecycle from loading, through cleaning and modeling, to storing the data. The work is performed using Python stack consisting ia of: IPython, NumPy, Pandas, Matplotlib, and Jupyter Notebooks. Students learn to structure their work using CRISP-DM and Data Science Process (Ask, Get, Explore, Model, Communicate and Visualize).

Learning outcome

Knowledge :

  • Execute/Develop tools to load, parse, clean, transform, merge, reshape, and store data.
  • Compare regular Python, NumPy, and Pandas data structures and choose one for the given problem. Use the IPython shell and Jupyter notebook for exploratory computing.
  • Execute/Develop simple machine learning or data mining algorithms.

Skills:

  • Organize data analysis following CRiSP-DM and Data Science Process
  • Build engaging visualizations of data analysis using matplotlib
  • Optimize data analysis applying available structure and methods
  • Evaluate, communicate and defend results of data analysis

General qualifications :

  • Solve real-world data analysis problems following a well-structured process

Required prerequisite knowledge

10 ECTS in Programming, Databases or Software Engineering related courses.

Recommended prerequisites

Programming fundamentals (DAT120), Probability and Statistics 2 (STA500)

Exam

Project work and written exam

Weight 1/1

Marks Letter grades

Project work in groups

Weight 3/5

Marks Letter grades

Written exam (Multiple Choice)

Weight 2/5

Duration 3 Hours

Marks Letter grades

Written exam (multiple choice) is digital.

Project Work in Groups

The project is completed in groups. Project work is to be performed in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the lecturer.

A project report, including source code, contributes to the grade.

If a student fails the project work, he/she has to take this part again the next time the subject is lectured.

Method of work

The work will consist of 6 hours of lecture, scheduled laboratory, supervised group work per week. Students are expected to spend an additional 6-8 hours a week on self-study, group discussions, and development work.

Open for

Admission to Single Courses at Master Level at the Faculty of Science and Technology
Data Science Computational Engineering Computer Science Industrial Economics Industrial Automation and Signal Processing - Master's Degree Programme - 5 year
Exchange programme at The Faculty of Science and Technology

Admission requirements

Must meet the admission requirements of one of the study programmes the course is open for.

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.

Contact

Head of Department:
Tom Ryen
Course coordinator:
Antorweep Chakravorty
Course teacher:
Aida Mehdipourpirbazari

Offered by

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1