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.

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.

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.

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.

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.

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.

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.

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.

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. the model residuals can be beneficial in model selection.

In this section, we’ll create a combination chart with a dual-axis. Our data centers around data science and Python programmers and comes from the annual KD Nuggets survey. We’ll get a chance to delve deeper into each of the four software, as we learn how to create combination charts.

We’ll work on real-life data and create a dashboard in Excel. Our dashboard will center on creating a report for a large company operating in the FMCG sector. The star of the report will be the two custom filters, which will slice the data based on the period we’re interested in.

In this section, we’ll rely on Tableau’s capabilities to create a dynamic report for a company based on real-world data. Our dashboard will include a custom time filter, as well as a brand filter and we’ll even devise our very own custom design template for the dashboard.