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).
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.
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 Credits in Programming, Databases or Software Engineering related courses.
DAT120 Introduction to Programming, STA500 Probability and Statistics 2
Project work and written exam
Form of assessment
Project work in groups
Written exam (Multiple Choice)
Project Work in GroupsThe 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.
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.
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.