This course explains how to perform data processing and analysis in Python, from basic syntax to visualization and a simple GUI. Built for beginners and non-IT backgrounds.
Language of course:
English
Format of the event:
Video lectures
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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.
Course Description
This course (English copy of "Python玩转数据" ) is primarily intended for non-computer majors. It starts with basic Python syntax, to getting Python data locally and from the web, to presenting the data, then to doing basic and advanced statistical analysis and data visualization, and finally to developing a simple graphical interface for presenting and processing data, progressing level by level.
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