14.02.2025 – 07.03.2025
3 weeks
English
Online
TU Berlin Certificate of Professional Education
Online
LECTURER
Dongrui Jiang
3898,50 €
Recognized as Bildungszeit
PRACTICAL PYTHON APPLICATIONS
This course is tailored for individuals who have a basic understanding of Python but struggle to connect it with their work or projects. It’s designed to bridge that gap, equipping participants with the skills to apply Python in solving real-world challenges. The course goes beyond theory, offering a deep dive into essential techniques for data collection, processing, and visualization. Participants will learn how to use Python to scrape data from the web, process complex datasets – including geo-data and time-series – and visualize this data in a meaningful way.
By the end of the course, participants will be able to create interactive dashboards that not only display their visualizations but also connect to databases for real-time data updates. This hands-on experience will enable learners to turn raw data into actionable insights that can drive decisions and impact their field. Whether you’re looking to integrate Python more effectively into your work or seeking to advance your data skills, this course provides a practical, step-by-step approach to mastering data collection, processing, and visualization with Python. The course culminates in a final project that incorporates all the skills and knowledge gained throughout the course.
Learning goals
Gain practical experience for the following fields:- Web Scraping: develop the skills to automate data collection from websites using Python, enabling participants to efficiently gather and prepare data from various online sources for analysis.
- Data Processing: Learn how to manipulate and analyze different types of table-structured data, including geo-data and time-series, using Python packages like Pandas and GeoPandas. Also gain experience in performing basic database operations to manage and query data effectively.
- Data Visualization: Transform processed data into compelling visual representations. Learn how to use Python’s visualization libraries to craft graphs, charts, and plots that clearly communicate insights and trends.
- Build Interactive Dashboards: Integrate data processing and visualization skills to develop dynamic, interactive dashboards. These dashboards will not only present the learners’ visualizations but also connect to databases, allowing for real-time data updates and more interactive user experiences.
Content
1. Python Programming Review- Rapid review of fundamental Python concepts
- Hands-on exercises with advanced data types for real-world scenarios
- Introduction to web scraping: concepts, ethics, HTTP, and HTML basics
- Static scraping using Requests and BeautifulSoup
- Dynamic scraping using Selenium for interacting with dynamic pages
- Data storage and processing techniques for scraped data
- Hands-on practice with static and dynamic website scraping
- Handling time series data with Pandas
- Processing geo-referenced data using GeoPandas
- Pandas vs. Excel: Comparative analysis for practical applications
- Real-world data manipulation with Pandas
- Hands-on practice with time series and geo-data
- Overview of database types and structures
- Interacting with SQL databases using Python
- Working with NoSQL databases like MongoDB
- Hands-on practice integrating scraped data into databases
- Fundamentals of creating interactive dashboards
- Introduction to Docker for dashboard deployment and scalability
- Hands-on practice building and deploying a dashboard using Docker
Target group
This course is perfect for working professionals with some basic Python knowledge and want to apply their knowledge to real-world data challenges. Ideal for data analysts, business intelligence specialists, and professionals in fields like finance and marketing to gain hands-on experience in data collection, processing, and visualization.Prerequisites
A basic understanding of Python is required (no advanced programming or data science experience is required) and an enthusiasm for applying it to practical data challenges will help to succeed in this course.As this is an online course, participants will need to ensure access to a laptop or PC, a headset with a microphone and a reliable internet connection in order to participate effectively in virtual classroom sessions and collaborative discussions. Participants are encouraged to bring their real-world challenges to the course, fostering a dynamic learning environment where the acquired skills directly address professional needs.
Dates
Course schedule:- Virtual classroom session on February 14, 2025 from 15:00 - 21:00 (CET)
- Self-Study from February 15 -21, 2025 (20-30 hours) including 6 hours of open consultation in a group or individually
- Virtual classroom session on February 22, 2025 from 09:00 - 17:00 (CET)
- Self-Study from February 23, 2025 – March, 06, 2025 (20-30 hours) including 10 hours of open consultation in a group or individually
- Virtual classroom session on March 07, 2025 from 09:00 - 17:00 (CET) incl. final project presentation and results
Following the conclusion of the first two virtual classroom sessions, participants will undertake a period of a self-study of 20-30 hours, e.g. with homework and exercises. This independent study phase will be accompanied by the lecturer, up to the date of the final virtual classroom session. The lecturer will support participants for approx. 8 hours online, in the form of an open consultation, a small group workshop (or similar), by arrangement.
LECTURER
Dongrui Jiang is a scientific researcher in the chair of Energy and Resource Management at TU Berlin. Her research primarily focuses on energy system analysis, with a significant emphasis on data analysis in the energy field. Since 2020, she has also been a lecturer for the "Introduction to Python Programming" course at the TU Berlin Summer and Winter University. With over 5 years of experience utilizing Python for data processing, she brings extensive practical knowledge and expertise to the field. Passionate about teaching and sharing insights, she conducts training sessions on both basic Python grammar and advanced topics such as web scraping and data analysis.
