Getting Started with Data Analysis Using Python 2
Build practical Python 2 data analysis skills
Getting Started with Data Analysis Using Python 2 introduces Python-based workflows for gathering, cleaning, persisting, and visualizing data. The course uses practical demonstrations with CSV and pickle files, SQLite databases, and the pygal library to teach these techniques through stock-market examples.
This demo-driven course focuses on task-oriented skills so learners can programmatically collect datasets, persist and query them, apply lists, dictionaries, tuples, and sets for analysis, and generate readable charts with pygal. It is well suited to beginners seeking a compact, hands-on introduction and to developers maintaining legacy Python 2 environments who want concise, applicable guidance for small analytics projects.
At a Glance
Getting Started with Data Analysis Using Python 2 is an introductory Pluralsight course that demonstrates how to use Python to collect, clean, analyze, and visualize data.
It is taught by Terry Toy and covers core topics such as Python data structures, persisting data with CSV and SQLite, basic SQL usage, and creating simple charts with pygal.
| Level | Beginner |
| Rating | 4.5 out of 5 |
| Duration | 2+ hours |
| Languages | English |
| Certificate | Certificate of completion |
| Access | Access for as long as your subscription is active |
| Course includes |
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| Price | Subscription-based, monthly or annual plans |
What This Course Teaches
This course frames outcomes as practical, measurable competencies you can apply immediately to real datasets.
By course end you will collect data programmatically, persist and query it with CSV/pickle/SQLite, apply core Python data structures to analyze stock-market datasets, and build visualizations with pygal.
How the Course Is Structured
The course is organized into eight compact modules that move learners through setup, data collection, persistence, analysis, and visualization in a linear, demo-driven progression. In total the syllabus lists 8 modules and about 2 hours 23 minutes of video content.
Curriculum overview
01Course Overview▾
Orientation to course goals, structure, and how to approach the material.
02Introduction to Python Data Analysis▾
Covers Python version checks, installing and running scripts, and initial setup for the course.
03Collecting Stock Market Data▾
Introduces sources for market data, demonstrates fetching data programmatically, and writing data to files.
04Understanding Python Data Structures▾
Explains and demonstrates lists, dictionaries, tuples, and sets and how to use them for analysis tasks.
05Persisting Data in Databases and Files▾
Shows file-based persistence, using pickle, basic SQL and SQLite workflows, and tools like DB Browser for SQLite.
06Analyzing Stock Market Data▾
Walks through analysis techniques on stock-market datasets with a demo to compute insights and summaries.
07Building Simple Data Visualizations▾
Demonstrates chart basics and creating stock-market visualizations using the pygal library.
08Course Summary and Next Steps▾
Final review of key concepts and suggested next steps for continued learning.
Audience & Requirements
This beginner Pluralsight course is aimed at learners seeking a practical, hands-on introduction to using Python for collecting, persisting, analyzing, and visualizing real-world datasets.
It targets beginners who want to add concrete data-handling and charting skills (CSV, SQLite, pygal) that are immediately applicable to small analytics projects and stock-market examples.
- Aspiring data analysts and business analysts adding Python-based workflows to their toolkit.
- Developers or analysts who need practical data-persistence and simple visualization skills for small projects.
- Students and hobbyists exploring stock-market datasets and basic analytics.
- Professionals automating simple data collection and reporting tasks.
- A Python 2 environment installed and configured to run scripts (course title specifies Python 2).
- DB Browser for SQLite (or equivalent) to examine and manage local SQLite databases.
- Basic comfort running Python scripts from the command line or terminal.
- A code editor (Visual Studio Code or similar) for viewing and editing examples.
Final Verdict
This course is a practical, demo-driven primer that delivers concrete, task-oriented skills for learners wanting a compact introduction to data collection, persistence, analysis, and basic visualization in Python.
Given its focused syllabus and generally positive rating, it’s a worthwhile, low-friction option for beginners—especially those who need to work with or understand legacy Python 2 code—provided you already use or are willing to subscribe to the host platform for access and the included certificate.
