Data Processing Using Python: from acquisition to visualization

Course overview

Description generated based on course syllabus and open data.

The course systematically covers data processing using Python: basic syntax, acquiring data from local files and the web, presenting information, fundamental and advanced statistical analysis, visualization, and building a simple graphical interface.

Data processing in Python: scope and learning format

• Python 3: variables, collections, functions, modules.
• Data acquisition: CSV/JSON files, HTTP requests (Requests), HTML parsing (Beautiful Soup), simple Web APIs.
• Preparation and presentation: cleaning, structuring, tabular formats, saving outputs.
• Statistics: descriptive metrics, correlations, basics of hypothesis testing.
• Visualization: core plots and charts for data overview.
• Simple GUI: presenting and interacting with data in a windowed interface.

Level: beginner. Format: self-paced with practical examples across domains.

Who benefits from Python-based data processing, and who may not

Suitable for

• Students and professionals without a CS background who work with data.
• Entry-level analysts, researchers, data journalists, and business-domain specialists.
• Those needing basic Python tools for preparation and visualization.

Not ideal for

• Learners seeking in-depth machine learning or big data engineering.
• Users who want purely no-code tools.

Data processing challenges → expected outcomes with Python

• Scattered files → structured tables and consistent formats.
• Ad-hoc collection → reproducible scripts for local and web sources.
• Unclean data → basic cleaning, missing-value handling, validation.
• Dry statistics → clear visuals for exploration and communication.
• Manual steps → automation of repetitive tasks with Python scripts.

Comparison with alternatives for data processing

• Excel/spreadsheets: quick start but limited reproducibility; Python offers scriptable workflows.
• R: strong statistics; Python is broader for web integration, automation, and GUI.
• SQL: great for database queries; Python spans collection, processing, analysis, and visualization beyond DBMS.

Outcome overview after mastering data processing using Python

• Understanding Python 3 basics and libraries such as SciPy, Requests, and Beautiful Soup.
• Ability to acquire data from files and the web and perform basic cleaning.
• Skills in descriptive statistics and simple visualizations.
• Building a simple GUI to present and interact with data.
• Establishing reproducible workflows for everyday data tasks.