Creative Coding for Data Visualizations: In this course, students will explore how to use Python to analyze and visualize data, creating interactive and visually engaging digital projects. The course introduces essential coding skills, focusing on practical applications of data analysis and visualization. Students will work with Python libraries like Pandas, Matplotlib, Seaborn, and Numpy to manipulate and present data in meaningful ways. By the end of the course, students will have completed their own data-driven visualization projects.
Students will learn:
- Introduction to Python programming in Jupyter Notebook.
- Key programming concepts like variables, functions, and loops, and begin using tools to explore and analyze data.
- How to use Numpy and other tools to calculate mean, median, mode, variance, and standard deviation. This will provide them with the foundation to understand and summarize data.
- How to explore datasets in the form of Pandas Data frams, NumPy arrays and learning to summarize them, identify patterns and deal with missing values.
- The basics of data visualization and experiment with different types of plots (e.g., histograms, scatter plots) to visualize trends and relationships in the data.
- Data manipulation with Pandas and NumPy, teaching students how to clean, filter, and transform data.
- Use Matplotlib and Seaborn to create more advanced visualizations, including line charts, bar graphs, and heatmaps, allowing them to represent complex data in a visually appealing way.
- How to generate synthetic data using different libraries such as Sk LEarn, Matplotlib and continue manipulating and visualizing them.
- Create a data visualization or interactive project relating to the topic of their choice (banking, health, sports, etc) and present their work, showcasing how they combined data analysis, manipulation, and creativity.
By the end of the week, students will have:
- A solid understanding of Python programming fundamentals.
- Practical experience in analyzing and manipulating data.
- Skills to create clear and effective data visualizations.
LUNCH: Students staying for full day or for both morning and afternoon sessions can bring or buy their lunch. All students will eat lunch on Campus in The Commons, a small food court.
