Python Fundamentals Training

Duration: 8 hours

Alpinum’s Python Fundamentals Training gives engineers practical experience in Python programming for data processing, automation, API interaction and scalable application development. The course is designed for engineers who need to use Python in software, systems, AI/ML, networking or automation workflows.

The training combines core Python programming concepts with hands-on labs that reflect real engineering use cases.

Who Should Attend?

This course is suitable for:

• • Graduate engineers learning Python for engineering workflows

• • Software engineers moving into automation, data processing or API development

• • Systems engineers using Python for scripting and tooling

• • Technical teams needing a shared Python foundation

What the Course Covers

The course introduces the main Python concepts required for practical software development. Participants learn how to structure Python applications, work with data, build reusable modules and connect to external services using REST APIs.

Key topics include:

• • Python data structures

• • Object-oriented programming

• • Python modules

• • Asyncio

• • REST APIs

• • Data processing

Practical Labs

Participants work through practical Python labs including a student data analysis system, a weather information REST API client and an asynchronous stock market data aggregator. These labs help engineers practise Python data handling, API integration, asynchronous programming and reporting.

Lab 1: Student Data Analysis System

Learning Objectives: Data structures, OOP, Modules, Data processing

Description: Develop a student performance management system that loads, processes, and analyzes student assessment data.

Tasks

• • Create classes: Student, Course, Assessment

• • Use Python data structures: Lists, Dictionaries, Sets, Tuples

• • Organize code into modules: models.py, data_loader.py, analytics.py, main.py

• • Implement: Load student data from CSV, calculate averages, identify top performers, and generate summary reports

Example Dataset

StudentID, Name, Module, Mark 
1001, Alice, Maths,85 
1002, Bob, Physics,72 
1001, Alice, Physics,91

Extension Tasks

• • Export reports to JSON.

• • Create grade distributions.

• • Generate charts using Matplotlib.

Topics Covered

Data Structures, OOP, Modules, Data Processing

Open Source Recommendation: The Pandas ecosystem is standard here. For education-specific tooling, nbgrader (a system for assigning and grading Jupyter notebooks) is highly relevant.

Lab 2: Weather Information REST API Client

Learning Objectives: REST APIs, OOP, Modules, Data processing

Description: Build a Python application that retrieves and analyzes weather information from a public REST API.

Tasks

• • Create classes: WeatherClient, Location, WeatherReport

• • Use REST API calls with requests

• • Retrieve: Current weather, Forecasts, Temperature trends

• • Process returned JSON data.

• • Display: Highest temperature, Lowest temperature, Average daily temperature

Extension Tasks

• • Cache API responses.

• • Support multiple locations.

• • Add command-line arguments.

Topics Covered

REST APIs, OOP, Modules, Data Processing, Data Structures

Open Source Recommendation: PyOWM (an open-source Python wrapper for the OpenWeatherMap API) or wttr. in for terminal-based weather reporting.

Lab 3: Asynchronous Stock Market Data Aggregator

Learning Objectives: Asyncio, REST APIs, Data processing, OOP, Modules

Description: Create a high-performance application that collects financial market data from multiple online services concurrently.

Tasks

• • Build classes: MarketDataClient, Stock, Portfolio

• • Use: asyncio, aiohttp

• • Fetch stock information for multiple symbols simultaneously: AAPL, MSFT, GOOGL, NVDA, AMZN

• • Process received data: Daily change, Percentage gain/loss, Portfolio value

• • Generate a summary report.

Extension Tasks

• • Add portfolio tracking.

• • Store results in SQLite.

• • Create real-time updates.

• • Implement retry and timeout handling.

Topics Covered

Asyncio, REST APIs, Data Processing, OOP, Modules, Data Structures

Open Source Recommendation: yfinance (Yahoo! Finance market data downloader) or the OpenBB Terminal (an open-source investment research platform).

Coverage Matrix

These labs provide a natural progression: Local data processing and OOP fundamentals, consuming and analyzing external REST API data, and building scalable asynchronous applications using modern Python techniques.

Project:

The course can include a REST API service project to consolidate Python application design, API handling and structured data processing.

Assessment

• • Hands-on Exercises: Directed development blocks focused on track-specific implementation challenges.

• • Quizzes: Milestone-based knowledge checks to ensure students remain aligned with architectural best practices throughout the project lifecycle.

Learning Outcomes

By the end of the course, participants will be able to build structured Python applications, use common data structures, organise code into modules, process data, call REST APIs and understand asynchronous Python workflows.

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