Bundle Course - Data Visualization with Python and R

Uplatz offers this comprehensive bundle course on Data Visualization with Python and R consisting of a combo of video courses on all topics that are associated with Data Visualization using Python programming and Data Visualization using R programming. You will be awarded Course Completion Certificate at the end of the course.

Courses included in Bundle Course Data Visualization with Python and R Bundle Course:

1. Data Visualization in Python
2. Data Visualization in R

Data visualization with Python involves using libraries such as Matplotlib, Seaborn, Plotly, and others to create graphical representations of data. These visualizations help in understanding patterns, trends, and relationships within the data.

A brief overview of which Python modules and libraries are used for data visualisation purposes:

1. Matplotlib: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. It offers a wide range of plot types, including line plots, scatter plots, bar plots, histograms, heatmaps, and more. Matplotlib provides fine-grained control over every aspect of the plot, allowing customization of colors, markers, labels, titles, axes, and legends.

2. Seaborn: Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive statistical visualizations. It simplifies the process of creating complex plots, such as categorical plots, distribution plots, regression plots, and heatmap visualizations. Seaborn also offers built-in themes and color palettes to enhance the aesthetics of the plots.

3. Plotly: Plotly is a library that supports interactive and web-based visualizations in Python. It allows users to create interactive plots with zooming, panning, and hover effects directly within Jupyter Notebooks or web applications. Plotly supports a wide range of plot types, including line plots, scatter plots, bar plots, 3D plots, and choropleth maps.

4. Bokeh: Bokeh is another library for creating interactive visualizations in Python. It focuses on providing tools for creating web-ready plots with interactive features such as tooltips, hover effects, and linked brushing. Bokeh supports a variety of plot types and can be easily integrated with web frameworks like Flask and Django for building interactive web applications.

5. Altair: Altair is a declarative statistical visualization library in Python that allows users to create concise, expressive visualizations using a simple and intuitive syntax. It is built on top of the Vega and Vega-Lite visualization grammars and provides seamless integration with Pandas data structures for easy data manipulation and visualization.

Python offers a rich ecosystem of libraries and tools for data visualization, catering to a wide range of needs and preferences for creating informative and visually appealing plots.

Data visualization in R is facilitated by several powerful libraries, with ggplot2 being one of the most popular. Here's a description:

1. ggplot2: ggplot2 is a powerful and flexible R package for creating a wide variety of static visualizations. It follows a grammar of graphics approach, which means that you can build visualizations by combining different layers and aesthetics. ggplot2 offers a consistent and intuitive syntax for creating plots, making it easy to customize colors, shapes, sizes, and labels. It supports various plot types, including scatter plots, line plots, bar plots, histograms, box plots, and more.

Other notable packages for data visualization in R include:

1. ggvis: ggvis is an interactive data visualization package that is based on the principles of ggplot2. It allows you to create interactive plots with tooltips, zooming, and brushing capabilities directly in R Markdown documents or Shiny web applications.

2. plotly: plotly is an R package for creating interactive web-based visualizations using the Plotly JavaScript library. It enables you to create interactive plots with zooming, panning, and hover effects directly within R code. plotly supports a wide range of plot types, including scatter plots, line plots, bar plots, 3D plots, and choropleth maps.

3. gganimate: gganimate is an extension of ggplot2 that allows you to create animated plots in R. It enables you to add transitions, animations, and interactivity to your ggplot2 visualizations, making it easier to convey dynamic trends and patterns in the data.

4. Shiny: Shiny is an R package for building interactive web applications directly from R code. It allows you to create interactive dashboards, data visualization tools, and web-based applications with minimal coding. Shiny apps can be deployed on the web or hosted locally, making them accessible to users across different platforms.

R offers a rich ecosystem of packages and tools for data visualization, catering to a wide range of needs and preferences for creating informative and visually appealing plots. Whether you're creating static visualizations for reports and presentations or interactive web-based applications for data exploration and analysis, R provides the flexibility and versatility to meet your data visualization needs.

Everyone

Passion and determination to achieve your goals!

• Data Visualization Specialist
• Data Scientist
• Data Analyst
• Data Engineer
• Business Intelligence Analyst
• BI Developer
• Data Visualization Developer
• Data Visualization Analyst
• Python Developer
• R Developer
• Software Engineer
• Data Journalist
• Research Analyst
• Data Visualization Consultant
• Machine Learning Engineer
• Data Consultant
• Data Science Manager
• Financial Analyst
• Marketing Analyst
• IT Consultant

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Uplatz Certificate of Completion

Digital certificate - Included