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    Data Visualisation using Python

    Python Programming Course
    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.

    Data Visualisation using Python 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

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    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Data Visualisation using Python Training Prerequisites

      arget Audience

      Data Visualisation with Python is designed for developers and scientists, who want to get into data science or want to use data visualisations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualisation, however, it'll help you to have some knowledge of Python and familiarity with mathematics. Even though this is a beginner level course on data visualisation, experienced developers will be able to improve their Python skills by working with real-world data.

    • Data Visualisation using Python Training Format

      In-Person

      Online

    • Data Visualisation using Python 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
      Bar chart
      Radar chart
      Activity 1: Discussion round about comparison plots
      · Topic 3: Relation plots

      Scatter plot
      Bubble plot
      Heatmap
      Correlogram
      Activity 1: Discussion round about relation plots
      · Topic 4: Composition plots

      Pie chart
      Stacked bar chart
      Stacked area chart
      Venn diagram
      Activity 1: Discussion round about composition plots
      · Topic 5: Distribution plots

      Histogram
      Density plot
      Box plot
      Violin plot
      Activity 1: Discussion round about distribution plots
      · 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
      Topic 2: Matplotlib
      Topic 3: Seaborn
      Topic 4: Geo plots with geoplotlib
      Topic 5: Interactive plots with bokeh

      Lesson 4: Deep Dive into Data Wrangling with Python

      Topic 1: Matplotlib
      Topic 2: Pyplot basics
      Topic 3: Basic plots
      Activity 1: Comparison plots: Line, bar, and radar chart
      Activity 2: Distribution plots: Histogram, density, and box plot
      Activity 3: Relation plots: Scatter and bubble plot
      Activity 4: Composition plots: Pie chart, stacked bar chart, stacked area chart, and Venn diagram
      Topic 4: Legends
      Activity 1: Adding a legend to your plot
      Topic 5: Layouts
      Activity 1: Displaying multiple plots in one figure
      Topic 6: Images
      Activity 1: Displaying a single and multiple images
      Topic 7: Writing mathematical expressions

      Lesson 5: Simplification through Seaborn

      Topic 1: From Matplotlib to Seaborn
      Topic 2: Controlling figure aesthetics
      Activity 1: Line plots with custom aesthetics
      Activity 2: Violin plots
      Topic 3: Color palettes
      Activity 1: Heatmaps with custom color palettes
      Topic 4: Multi-plot grids
      Activity 1: Scatter multi-plot
      Activity 2: Correlogram

      Lesson 6: Plotting geospatial data

      Topic 1: Geoplotlib basics
      Activity: Plotting geospatial data on a map
      Activity: Choropleth plot
      Topic 2: Tiles providers
      Topic 3: Custom layers
      Activity: Working with custom layers

      Lesson 7: Making things interactive with Bokeh

      Topic 1: Bokeh basics
      Topic 2: Adding Widgets
      Activity 1: Extending plots with widgets
      Topic 3: Animated Plots
      Activity 1: Animating information

      Lesson 8: Combining what we've learned

      Topic 1: Recap
      Topic 2: Free exercise
      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.
      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
      An Extension to Data Wrangling