Python and SQL: intro / SQL platforms 2400-DS1SQL
The main aim of the course is to make students familiar with the Python programming language and SQL for data management and analysis. The course develops both programming and analytical skills:
i) coding basics, logic, and data structures in Python
ii) data wrangling and visualization using Pandas, NumPy, and Seaborn/Matplotlib
iii) database querying with SQL and its integration with Python
iv) building simple applications (Streamlit dashboards, Django web apps)
Students will gain practical coding experience, learn to debug and optimize code, and present their results through interactive applications.
Topics include:
• Python basics – installation, Jupyter, VS Code, data types, libraries, file handling
• Control structures – if statements, loops, errors, functions
• Data management – Pandas DataFrames, NumPy arrays
• Object-oriented programming (OOP) in Python
• Visualization – Seaborn, Matplotlib
• Streamlit – interactive dashboards and apps
• SQL – queries, filtering, joins, aggregations
• SQL + Python – connecting to databases, using Pandas
• Django – basics of models, views, and templates
• Final project presentations (individual)
Estimated student workload:
Activity Contact (K) Self-study (S)
Lecture/lab 30 0
Consultations 3 0
Work with additional materials 0 10
Preparation for lectures 0 10
Preparation for mid-semester tasks 0 10
Preparation for exam 0 10
Exam 2 0
Project preparation 0 15
Total 35 55 = 90
|
Term 2024Z:
1. Relational model for database management. Szacunkowy nakład pracy studenta: |
Type of course
Course coordinators
Term 2025Z: | Term 2024Z: |
Learning outcomes
Learning outcomes:
- Is familiar with the Python programming environment and SQL basics
- Can manage, clean, and analyze different datasets
- Is able to visualize data and present findings using Python libraries
- Can query relational databases with SQL and integrate them with Python
- Is able to design and implement a simple data-driven app (Streamlit or Django)
- Can independently troubleshoot errors and optimize code (K_U02, K_U05)
Assessment criteria
The final grade includes:
• Final individual project presentation (50 points)
• Written exam – multiple choice (25 questions) (50 points)
Grades:
Points Grade
0–50 ndst (2)
51–60 dst (3)
61–70 dst+ (3+)
71–80 db (4)
81–90 db+ (4+)
91–100 bdb (5)
>100 bdb! (5!)
Bibliography
Compulsory literature:
• VanderPlas, J. (2016). Python Data Science Handbook. O’Reilly.
• McKinney, W. (2017). Python for Data Analysis. O’Reilly.
• Beaulieu, A. (2009). Learning SQL. O’Reilly.
|
Term 2024Z:
1. Lutz, M. (2013) ,”Learning Python”, 5th Edition, O’Reilly |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: