Data Visualization with Python, R, Tableau, and Excel

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. That’s precisely why 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. 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 toolset.

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Section 1

Introduction to The Course

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.

Premium course icon What Does the Course Cover
Premium course icon Why Learn Data Visualization
Premium course icon How to Choose the Right Visualization - Popular Approaches and Frameworks
Premium course icon Color Theory and Colors

Section 2

Setting Up the Working Environments

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.

Premium course icon Setting Up The Environments - Do Not Skip, Please!
Premium course icon Tableau - Downloading Tableau
Premium course icon Python - Why Python and Why Jupyter
Premium course icon Python - Installing Anaconda
Premium course icon Python - Jupyter Dashboard - Part 1
Premium course icon Python - Jupyter Dashboard - Part 2
Premium course icon Python - Installing the Seaborn Package
Premium course icon R - Installing R and RStudio
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Premium course icon R - Quick Guide to RStudio
Premium course icon R - Changing the Appearance in Rstudio
Premium course icon R - Installing Packages and Using Libraries
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Section 3

Bar Chart - A Brief Intro To Each Environment

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.

Premium course icon Bar Chart - Introduction - General Theory and Dataset
Premium course icon Bar Chart - Excel - How to Create a Bar Chart
Premium course icon Bar Chart - Tableau - How to Create a Bar Chart
Premium course icon Bar Chart - Python - How to Create a Bar Chart
Premium course icon Bar Chart - R - How to Create a Bar Chart
Premium course icon Bar Chart - Interpretation & What Makes a Good Bar Chart
Premium course icon Bar Chart - Homework

Section 4

Pie 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.

Premium course icon Pie Chart - Introduction - General Theory and Dataset
Premium course icon Pie Chart - Excel - How to Create a Pie Chart
Premium course icon Pie Chart - Tableau - How to Create a Pie Chart
Premium course icon Pie Chart - Python - How to Create a Pie Chart
Premium course icon Pie Chart - R - How to Create a Pie Chart
Premium course icon Pie Chart - Interpretation
Premium course icon Pie Chart - Why You Should Never Use a Pie Chart

Section 5

Stacked Area 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.

Premium course icon Stacked Area Chart - Introduction - General Theory and Dataset
Premium course icon Stacked Area Chart - Excel - How to Create an Stacked Area Chart
Premium course icon Stacked Area Chart - Tableau - How to Create an Stacked Area Chart
Premium course icon Stacked Area Chart - Python - How to Create an Stacked Area Chart
Premium course icon Stacked Area Chart - R - How to Create an Stacked Area Chart
Premium course icon Stacked Area Chart - Interpretation
Premium course icon Stacked Area Chart - What Makes a Good Stacked Area Chart

Section 6

Line Chart

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.

Premium course icon Line Chart - Introduction - General Theory and Dataset
Premium course icon Line Chart - Excel - How to Create a Line Chart
Premium course icon Line Chart - Tableau - How to Create a Line Chart
Premium course icon Line Chart - Python - How to Create a Line Chart
Premium course icon Line Chart - R - How to Create a Line Chart
Premium course icon Line Chart - Interpretation
Premium course icon Line Chart - What Makes a Good Line Chart

Section 7

Histogram

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.

Premium course icon Histogram - Introduction - General Theory and Dataset
Premium course icon Histogram - Excel - How to Create a Histogram Chart
Premium course icon Histogram - Tableau - How to Create a Histogram Chart
Premium course icon Histogram - Python - How to Create a Histogram Chart
Premium course icon Histogram - R - How to Create a Histogram Chart
Premium course icon Histogram - Interpretation
Premium course icon Histogram - How to Choose the Right Number of Bins
Premium course icon Histogram - What Makes a Good Histogram Chart

Section 8

Scatter Plot

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.

Premium course icon Scatter Plot - Introduction - General Theory and Dataset
Premium course icon Scatter Plot - Excel - How to Create a Scatter Plot
Premium course icon Scatter Plot - Tableau - How to Create a Scatter Plot
Premium course icon Scatter Plot - Python - How to Create a Scatter Plot
Premium course icon Scatter Plot - R - How to Create a Scatter Plot
Premium course icon Scatter Plot - Interpretation
Premium course icon Scatter Plot - What Makes a Good Scatter Plot

Section 9

Combo Plots Part 1 - Scatter and Trendline (Regression 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.

Premium course icon Regression Plot - Introduction - General Theory and Dataset
Premium course icon Regression Plot - Excel - How to Create a Regression Plot
Premium course icon Regression Plot - Tableau - How to Create a Regression Plot
Premium course icon Regression Plot - Python - How to Create a Regression Plot
Premium course icon Regression Plot - R - How to Create a Regression Plot
Premium course icon Regression Plot - Interpretation
Premium course icon Regression Plot - What Makes a Good Regression Plot
MODULE 2

Programming for Data Science

This course is part of Module 2 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. In contrast to the introductory nature of Module 1, Module 2 is designed to tackle all aspects of programming for data science. You will learn how to work with relational databases and SQL, as well as how to code in Python and R. By the end of this Module, you will have a versatile programming skill set.

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