Description
Description
In a landscape where an abundance of data is constantly being generated, developers proficient in data analytics and visualization remain highly sought after. Through “Data Visualization with Python,“ you’ll acquire the skills to harness Python alongside NumPy, Pandas, Matplotlib, and Seaborn to craft compelling data visualisations using real-world, publicly available data.
Data Visualization with Python adopts a practical, hands-on approach towards creating impactful data visuals using Python. Through numerous activities embedded within real-life business scenarios, you’ll have ample opportunities to practice and implement your newfound skills in a highly relevant context.
Data Visualisation with Python caters to developers and scientists aiming to venture into data science or leverage data visualisations for enhancing their personal and professional projects. While prior experience in data analytics and visualisation is not necessary, a basic understanding of Python and familiarity with high school-level mathematics can be beneficial. Although this course is designed for beginners in data visualisation, seasoned developers can enhance their Python proficiency through hands-on engagement with real-world data.
Training Objectives
- Understand and use various plot types with Python
- Explore and work with different plotting libraries
- Understand and create effective visualizations
- Improve your Python data wrangling skills
- Work with industry-standard tools like Matplotlib
- Seaborn
- and Bokeh
- Understand different data formats and representations
Course Outline
- Lesson One: Importance of data visualization and data exploration<
- · Topic 1: Introduction to data visualization and its importance<
- · Topic 2: Overview of statistics<
- o Activity 1: Compute mean, median, and variance for the following numbers and explain the difference between mean and median<
- · Topic 3: A quick way to get a good feeling for your data<
- · Topic 4: NumPy<
- o Activity 1: Use NumPy to solve the previous activity<
- o Activity 2: Indexing, slicing, and iterating<
- o Activity 3: Filtering, sorting, and grouping<
- · Topic 5: Pandas<
- o Activity 1: Repeat the NumPy activities using pandas, what are the advantages and disadvantages of pandas?<
- Lesson Two: All you need to know about plots<
- · Topic 1: Choosing the best visualization<
- · Topic 2: Comparison plots<
- Line chart<br />
- Bar chart<br />
- Radar chart<br />
- Activity 1: Discussion round about comparison plots<br />
- · Topic 3: Relation plots<
- Scatter plot<br />
- Bubble plot<br />
- Heatmap<br />
- Correlogram<br />
- Activity 1: Discussion round about relation plots<br />
- · Topic 4: Composition plots<
- Pie chart<br />
- Stacked bar chart<br />
- Stacked area chart<br />
- Venn diagram<br />
- Activity 1: Discussion round about composition plots<br />
- · Topic 5: Distribution plots<
- Histogram<br />
- Density plot<br />
- Box plot<br />
- Violin plot<br />
- Activity 1: Discussion round about distribution plots<br />
- · Topic 6: Geo plots<
- · Topic 7: What makes a good plot?<
- Activity 1: Given a small dataset and a plot, reason about the choice of visualization and presentation and how to improve it<
- Lesson 3: Introduction to NumPy, Pandas, and Matplotlib<
- Topic 1: Overview and differences of libraries<br />
- Topic 2: Matplotlib<br />
- Topic 3: Seaborn<br />
- Topic 4: Geo plots with geoplotlib<br />
- Topic 5: Interactive plots with bokeh<
- Lesson 4: Deep Dive into Data Wrangling with Python<
- Topic 1: Matplotlib<br />
- Topic 2: Pyplot basics<br />
- Topic 3: Basic plots<br />
- Activity 1: Comparison plots: Line, bar, and radar chart<br />
- Activity 2: Distribution plots: Histogram, density, and box plot<br />
- Activity 3: Relation plots: Scatter and bubble plot<br />
- Activity 4: Composition plots: Pie chart, stacked bar chart, stacked area chart, and Venn diagram<br />
- Topic 4: Legends<br />
- Activity 1: Adding a legend to your plot<br />
- Topic 5: Layouts<br />
- Activity 1: Displaying multiple plots in one figure<br />
- Topic 6: Images<br />
- Activity 1: Displaying a single and multiple images<br />
- Topic 7: Writing mathematical expressions<
- Lesson 5: Simplification through Seaborn<
- Topic 1: From Matplotlib to Seaborn<br />
- Topic 2: Controlling figure aesthetics<br />
- Activity 1: Line plots with custom aesthetics<br />
- Activity 2: Violin plots<br />
- Topic 3: Color palettes<br />
- Activity 1: Heatmaps with custom color palettes<br />
- Topic 4: Multi-plot grids<br />
- Activity 1: Scatter multi-plot<br />
- Activity 2: Correlogram<
- Lesson 6: Plotting geospatial data<
- Topic 1: Geoplotlib basics<br />
- Activity: Plotting geospatial data on a map<br />
- Activity: Choropleth plot<br />
- Topic 2: Tiles providers<br />
- Topic 3: Custom layers<br />
- Activity: Working with custom layers<
- Lesson 7: Making things interactive with Bokeh<
- Topic 1: Bokeh basics<br />
- Topic 2: Adding Widgets<br />
- Activity 1: Extending plots with widgets<br />
- Topic 3: Animated Plots<br />
- Activity 1: Animating information<
- Lesson 8: Combining what we've learned<
- Topic 1: Recap<br />
- Topic 2: Free exercise<br />
- Activity 1: Given a new dataset, the students have to decide in small groups which data they want to visualize and which plot is best for the task.<br />
- Activity 2: Each group gives a quick presentation about their visualizations.<
- Lesson 9: Application in real life and Conclusion of course<
- Applying Your Knowledge to a Real-life Data Wrangling Task<br />
- An Extension to Data Wrangling
