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This is the study programme for 2019/2020. It is subject to change.


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

Learning outcome

Knowledge:
  • Execute 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 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
  • Evalute, communicate and defend results of data analysis

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

Contents

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 i.a. 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).

Required prerequisite knowledge

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

Exam

Weight Duration Marks Aid
Project work and oral presentation1/1 A - F
Project is completed in groups. Project work is to be performed at the times and 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.
Both project and oral examination must be done before final grade is given. Each group member can receive a different grade based on their performance during the oral examination.
If a student fails the projectwork , he/she have to take this part again next time the subject is lectured.

Coursework requirements

Mandatory assignments
The course starts with 4 mandatory assignments completed individually. All mandatory assignments must be approved within a given deadline so that that student has the right to start with the project.

Course teacher(s)

Course coordinator
Antorweep Chakravorty
Programme coordinator
Nina Egeland
Head of Department
Tom Ryen

Method of work

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

Open to

Computer Science - Master's Degree Programme

Course assessment

Form and/or discussions.

Literature

(Required) Python for Data Analysis 2nd Edition, McKinney, O'Reilly ISBN: 978-1491957660
(Optional) Python Data Science Handbook, VanderPlas, O'Reilly ISBN: 978-1491912058
(Optional) Building Machine Learning Systems with Python 2nd Edition, Coelho and Richert, PACKT ISBN: 978-1784392772


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 27.06.2019