This course is centered not in just one, but four different environments: Excel, Tableau, Python, and R. Each section is dedicated to a specific type of chart – bar charts, pie charts, area charts, line charts and many more.
The Data Visualization course is designed for everyone looking to deepen their understanding of creating meaningful and compelling visualizations. Whether you’re coming from a business or data science-related field, knowledge in data visualization is both important and advantageous.
In addition, there are lectures that specifically explore what to avoid when creating a certain graphic. You can stick with your preferred environment and follow each section. Or you could master all four environments and add indispensable skills to your data visualization tools.
SECTION 1:
In this section, you will learn about the importance of data visualization, as well as some theoretical foundations for creating charts.
We introduce popular frameworks for choosing an appropriate visualization for your data, discuss color theory, and show different approaches to selecting the colors for your graphic.
SECTION 2:
Here, we set up different environments for the course. First, we will guide you through the installation process for Tableau.
Then, you will get familiar with the step-by-step process of installing Anaconda and Jupyter and an introductory tour of the Jupyter Dashboard for Python.
Finally, you’ll learn how to install R and R studio, explore the latter’s main features and learn how to customize its appearance.
SECTION 3:
We dive straight into visualization with the bar chart! We will take a look at a data set for second-hand car advertisements and use it to create a bar chart in Excel, Tableau, Python, and R. We’ll also lift the curtain on the key elements to making an outstanding bar chart.
SECTION 4:
In this section, we explore pie charts, which, despite criticism, are among the most popular visualizations. You will learn how to create a pie chart of engine fuel types in Excel, Tableau, Python, and R, and discover what to avoid when making a pie chart.
SECTION 5:
Here, you will create your own stacked area chart. Once again, our data follows the automobile theme with one additional element - time series, as the chart follows the popularity of different engine fuel types across the years.
SECTION 6:
In this section, we continue discussing time series data. We will turn our attention to the financial world and explore the stock market returns for two major indices: S&P 500 and FTSE 100. In conclusion, you’ll find out the advantages of using a line chart and what you should be wary of when creating one.
SECTION 7:
This section centers around the histogram – an integral part of the data analysis process. We will create a histogram of the price of California's real estate. Here, we devote an extra lecture and explore how to choose the right number of bins for your histogram.
SECTION 8:
In this section, you will learn how to create a scatter plot of real estate data. First, we’ll observe the relationship between California's real estate pricing and the area of properties.
Then, you’ll make a scatter plot in Excel, Tableau Python, and R and finish the section with valuable tips on what makes a good scatter plot.
SECTION 9:
We’ll explore a combination chart of a scatter and a regression line by using marketing data and a regression line to quantify the relationship between a company’s advertising budget and its sales.
You will learn how to create a regression scatter in Excel, Tableau, Python, and R, and discover different types of relationships between features in data.
Note:
With the annual package, you will be paying $ 29.00/Month that will include the following benefits:
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