Requirements

• Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Data Visualization with R course by Uplatz.

Data Visualization refers to quantitative analysis that allows us to explore data and communicate our findings. In data science, analyzing your data is only half the battle - communicating your data and results to share knowledge and facilitate decision making is also essential. Data visualization is a powerful tool that can allow people across all ranges of statistical know-how to understand complex patterns and findings.

Not only the R programming language was specifically designed for statistical computing, it was also developed with a focus on graphics. As well as the standard plotting functions available in base R, additional functionality is also available through add-on packages of code. The ggplot2 package was developed by Hadley Wickham as part of the tidyverse (a collection of packages designed with data science in mind) and is considered one of the best tools for plotting graphs. It combines high levels of customization with clean and visually pleasing graphics, often with minimal effort put in on the part of the programmer. Thus ggplot2 is basically a set of packages that aim to make data management, analysis and visualization more user-friendly. It contains a wide range of options to customize plots and can be used for all types of data.

Uplatz provides this in-depth training on Data Visualization using R. This Data Visualization with R course helps you to effectively create figures based on your quantitative data. If you want to understand your data better and add impact to your publications, Data Visualization in R is the right course for you. Understand the art of visual communication and practical implementation of data visualization. This is done using the R statistical programming environment with your own data.

Course Objectives

• The purpose of data visualization and how to use it to communicate effectively

• A quick introduction to base R graphics

• Different plot types in R

• Data Visualization tools in R

• Which is the most appropriate plot type for your data

• Adding details to plots

• ggplot2 package

• Implementing the "Grammar of Graphics" in ggplot2, such as scales, coordinate systems, position adjustments, and faceting

• Creating complex visualizations and investigating the correlations between variables

• Designing and implementing a visualization from scratch

• How much is too much

• The science of perception and apply design principles

• To distinguish between explanatory graphics for publication and for data exploration

• Advanced plot customization and beyond

Data Visualization with R - Course Curriculum

1. Data Visualization in R

• What is data visualization?

• Selecting right chart type

• Importance of data visualization & its benefits

• Applications of DATA Visualization

• R Programs for Scatterplot, Histogram, Bar & Stacked bar chart, boxplot, heatmap, line chart, density plot, pie chart

2. Data Visualization with ggplot2 package

• What is ggplot2

• Plotting with ggplot2

• Building your plots iteratively

• Univariate distributions & bar plot

• Annotation with ggplot2

• Axis manipulation

• Density plot

3. More Data Visualization tools in R

• Text mining and word cloud

• Radar chart

• Waffle chart

• Area Chart

• Correlogram

4. Project on Data Visualization

• "Visualizing Covid-19" comprehensive project - create from scratch

By learning data visualization with R, you gain not only technical skills but also the ability to communicate insights effectively through visually appealing and meaningful charts and dashboards. Some of the key benefits include:

1. Powerful Visualization Libraries: R provides a rich set of libraries like ggplot2, plotly, and Shiny that allow you to create complex and interactive visualizations with ease.

2. Customizability: R allows for highly customizable and detailed visualizations. You can fine-tune every aspect of your charts, from colors and shapes to annotations and themes.

3. Integration with Data Analysis: As a data analysis language, R seamlessly integrates data manipulation and visualization. This reduces the need for switching between different tools or languages, streamlining the workflow.

4. Support for Complex Data: R excels at handling large datasets, complex statistical analysis, and visualizing results in meaningful ways, making it ideal for scientific research, finance, and business analytics.

5. Reproducibility: Visualizations created with R can be easily documented and reproduced through scripts, enabling better transparency and repeatability in analysis.

6. Interactivity: R's interactive features (via packages like plotly and Shiny) allow users to explore data visualizations dynamically, which is valuable for exploring trends and gaining deeper insights.

7. Wide Industry Adoption: R is widely used in fields like finance, healthcare, academia, and research. Mastering it can open doors to various analytical and data-driven roles.

8. Collaboration: R integrates well with other tools like Tableau, Power BI, and databases, making it easier to collaborate in teams working on larger projects.

Who this course is for:

• Data Visualization Analysts & Consultants
• Data Analytics Specialists
• Data Visualization Designers
• Reporting Analysts & Business Intelligence (BI) Analysts
• Data Analytics and Visualization Managers
• Beginners and newbies interested in BI
• Analytics
• Visualization
• Data Scientists
• Data Engineers
• Data Integration Experts
• R Programmers & Developers
• Full Stack Data Visualization Developers
• Senior BI & Data Visualization Analysts
• Anyone aspiring for a career in Data Visualization
• Business Analysts & Consultants
• Marketing & Finance Analysts