Course detail

Python Programming – Data Science

FSI-VPD Acad. year: 2027/2028 Summer semester

Students will use the Python programming language and its libraries to solve problems in Data Science.
Students will be introduced to the ecosystem of applications and development tools in Python for various Data Science tasks.

Language of instruction

Czech

Number of ECTS credits

4

Supervisor

Ing. Jiří Kovář, Ph.D.

Department

Institute of Automation and Computer Science

Entry knowledge

Fundamental level of programming in course VP0 (Python programming).

Rules for evaluation and completion of the course

The active participation and mastering the assigned task.
Education runs according to week schedules. Attendance at the seminars is required. The form of compensation of missed seminars is fully in the competence of a tutor.

Aims

Understand the use of Python and its libraries (pandas, numpy, matplotlib, etc.) for Data Science. Advanced Python programming.
Upon successful completion of this course, students will be able to use knowledge in practical areas of Data Science. The main goal of data specialists is to clean and analyze large data.

Study aids

https://www.kaggle.com/

VANDERPLAS, J., Python Data Science Handbook: Essential Tools for Working with Data, 978-1098121228, 2023

https://jupyter.org/

GÉRON, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2022, 978-1098125974

The study programmes with the given course

Programme N-AIŘ-P: Applied Computer Science and Control, Master's, compulsory

Programme N-MAI-P: Mathematical Engineering, Master's, elective

Type of course unit

 

Lecture

26 hours, optionally

Syllabus


  1. Introduction to Data Science and the Python Ecosystem

  2. Data – Concepts, Types, Industrial Data, and Data Sources (Sensors, PLC, SCADA, Historians)

  3. Python for Data Processing – Review and Consolidation of Fundamentals

  4. Data Formats and Storage – From Files to Data Lakes and Time-Series Databases

  5. NumPy and pandas – Libraries for Data Analysis

  6. Time Series – Working with Time and Integrating Data Sources

  7. Data Preparation and Cleaning

  8. Descriptive Statistics and Correlation

  9. Inferential Statistics, Hypothesis Testing, and Statistical Process Control

  10. Principles of Data Visualization – Chart Selection, Perception, Color, and Data Storytelling

  11. Interactive Data Visualization – Plotly and Other Libraries

  12. Dashboard Development – Streamlit, Dash, Layout Design, and Key Performance Indicators (KPIs)

  13. Data Processing Automation and Operational Dashboards

Computer-assisted exercise

26 hours, compulsory

Syllabus


  1. Environment Setup and First Import of Measurement Data

  2. Analysis of Historian Data Exports – Variable Types and Metadata

  3. Processing Machine Logs Using Pure Python

  4. Data Format Conversion and Building a Mini Data Lake Using Parquet Files

  5. Production Line Data Analysis Using pandas

  6. Resampling and Synchronization of Time Series from Two Sensors

  7. Cleaning Noisy Time-Series Data

  8. Statistical Profiling of Process Variables and Correlation Analysis

  9. Hypothesis Testing for Machine Comparison and Control Charts

  10. Static Data Visualization with matplotlib and seaborn – Improving Poorly Designed Charts

  11. Interactive Visualization of Time Series and Performance Indicators Using Plotly

  12. Production Line Dashboard Development Using Streamlit

  13. Automated Data Processing Pipeline with a Regularly Updated Dashboard