After successful completion of the course, students are able to program in Python in a data-oriented way, using SciPy, NumPy and Pandas; explain the fundamentals of machine learning and network analysis, and implement a Data Science project.

The following topics are covered in the lectures:

• Introduction to Data-Oriented Programming Paradigms
• Python
• SciPy, NumPy, vectorisation, execution performance measurement
• Data preparation, structuring, fusion with Pandas
• Data Science solution approaches and case studies
• Introduction to machine learning
• Introduction to network analysis

lectures about the fundamentals

3 practical exercises (Exercises 1 and 2 are done individually, Exercise 3 is done in a group)

The link to the online lectures is on TUWEL.

Syllabus

All Lectures on Tuesday 12:00 c.t.-13:45.

1. Kickoff-Session, data science process, community, solution examples, Python introduction [Hanbury] (6.10.2020)

2. Introduction to DOPP [Hanbury] (13.10.2020)

3. SciPy, NumPy, vectorisation, visualisation, benchmarking [Piroi] (27.10.2020)

4. Preprocessing, Pandas [Piroi] (3.11.2020)

5. Intro to Machine Learning [Hanbury] (17.11.2020)

6. Network Analysis [Hanbury] (1.12.2020)
   

Exercise-related sessions

Review meetings for exercise 3 (15 minutes for each group):

• 15.12.2020, 9:00-13:00
• 16.12.2020, 9:00-11:00 and 13:00-15:00

Project presentation: 27.1.2020, 9:00-16:00

The effort breakdown is:

Python tutorial: 4h
Lectures: 7 sessions @ 2h: 14h
Exercises:
    EX1 (data wrangling): 5h
    EX2 (pandas + sklearn): 10h
    EX3 (project): 42h [includes review meeting (topic + questions + work plan)]
SUM: 75h

Three practical exercises. The third exercise requires a report, Jupyter Notebook, and presentation of the results.