Study Guide

Objective

Student knows Basic tools for data analysis
Student can implement data analytics in edge computing
Student knows Basic solutions for big data analysis in cloud

Content

Data at rest and data in motion
Process for data analysis
Data preparation
Basics of descriptive statistics
Data visualization
Machine learning basics
Big data architectures

Materials

Lecture material
Labwork exercises

Teaching methods

Lab works 7 x 3h, mandatory
Lectures 7 x 2h, mandatory
Self study

Exam schedules

One exam at the end of the course (late March).

Completion alternatives

-

Student workload

lab works 7x3h = 21h
lectures 7x2h = 14h
exam = 2h
self study = 73h
exam preparation 25h
TOTAL 135h

Content scheduling

1 Data at rest data in motion
2 Process of data analysis
3 Data preparation
4 Basics of descriptive statistics
5 Data visualization
6 Machine learning basics
7 Big data architectures

Further information

Course Itslearningn pages.

Evaluation scale

H-5

Assessment methods and criteria

Assessment is based on Labwork exercises and course exam. Labwork exercises are evaluated and every exercise need to returned. Half of the grade comes from exercises and other half from the course exam. Minimum reguirement to pass the course is to return all the exercises and to get 50% of the points in Course exam.

Assessment criteria, fail (0)

One or more labwork exercises missing or less than 50% of the points in course exam.

Assessment criteria, satisfactory (1-2)

The quality of the submitted exercises are poor and it is visible that the student has not put required effort in the exercises.

and

poor result from the course exam.

Assessment criteria, good (3-4)

The quality of the submitted exercises are good and it is visible that the student has spent the required time with the exercises but the student has not challenged his/her skills or the exercises lacks the final effort to improve it.

and

good result from the course exam.

Assessment criteria, excellent (5)

The quality of the submitted exercises are excellent and it is visible that the student has spent the required time or more with the exercises. The student has challenged his/her skills and exercises more about the topic to improve the end result

and

exelent result from the course exam

Qualifications

Basic skills in using both Windows and Linux systems
Basic networking skills (Cisco CCNA1 or similar)
Basic programming skills with some high level programming language (for example Python, Java, C# or similar)
Basic programming skills include (but are not limited to): output formatting, conditional execution, loops, functions/procedures, function parameters and return values, arrays, error handling, testing and good programming policies
Sufficient logical-mathematical thinking skills
Sufficient skills in English language (lectures and all materials are in English)