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, Introduction to DOPP (5.10.2021)

2. Data wrangling on the command line, Text stream processing (12.10.2021)

3. SciPy, NumPy, vectorisation, visualisation, benchmarking (19.10.2021)

4. Preprocessing, Pandas (9.11.2021)

5. Data suitability, Data biases (16.11.2021)

6. Intro to Machine Learning (23.11.2021)

7. Network Analysis (30.11.2021)
   

Exercise-related sessions

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

• 14.12.2021, 9:00-16:00
• 15.12.2021, 9:00-16:00

Project presentation: 25.1.2022, 9:00-18:00

The effort breakdown is:

Python tutorial: 4h
Lectures: 7 sessions @ 2h: 14h
Exercises:
    EX1 (data wrangling): 8h
    EX2 (pandas + sklearn): 12h
    EX3 (project): 37h [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.