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.
6 hours 
64 exercises 
2 assignments 
50 downloadables
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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.
What Does the Course Cover
Why Learn Data Visualization
How to Choose the Right Visualization - Popular Approaches and Frameworks
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.
Setting Up The Environments - Do Not Skip, Please!
Tableau - Downloading Tableau
Python - Why Python and Why Jupyter
Python - Installing Anaconda
Python - Jupyter Dashboard - Part 1
Python - Jupyter Dashboard - Part 2
Python - Installing the Seaborn Package
R - Installing R and RStudio
R - Quick Guide to RStudio
R - Changing the Appearance in Rstudio
R - Installing Packages and Using Libraries
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.
Bar Chart - Introduction - General Theory and Dataset
Bar Chart - Excel - How to Create a Bar Chart
Bar Chart - Tableau - How to Create a Bar Chart
Bar Chart - Python - How to Create a Bar Chart
Bar Chart - R - How to Create a Bar Chart
Bar Chart - Interpretation & What Makes a Good Bar Chart
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.
Pie Chart - Introduction - General Theory and Dataset
Pie Chart - Excel - How to Create a Pie Chart
Pie Chart - Tableau - How to Create a Pie Chart
Pie Chart - Python - How to Create a Pie Chart
Pie Chart - R - How to Create a Pie Chart
Pie Chart - Interpretation
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.
Stacked Area Chart - Introduction - General Theory and Dataset
Stacked Area Chart - Excel - How to Create an Stacked Area Chart
Stacked Area Chart - Tableau - How to Create an Stacked Area Chart
Stacked Area Chart - Python - How to Create an Stacked Area Chart
Stacked Area Chart - R - How to Create an Stacked Area Chart
Stacked Area Chart - Interpretation
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.
Line Chart - Introduction - General Theory and Dataset
Line Chart - Excel - How to Create a Line Chart
Line Chart - Tableau - How to Create a Line Chart
Line Chart - Python - How to Create a Line Chart
Line Chart - R - How to Create a Line Chart
Line Chart - Interpretation
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.
Histogram - Introduction - General Theory and Dataset
Histogram - Excel - How to Create a Histogram Chart
Histogram - Tableau - How to Create a Histogram Chart
Histogram - Python - How to Create a Histogram Chart
Histogram - R - How to Create a Histogram Chart
Histogram - Interpretation
Histogram - How to Choose the Right Number of Bins
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.
Scatter Plot - Introduction - General Theory and Dataset
Scatter Plot - Excel - How to Create a Scatter Plot
Scatter Plot - Tableau - How to Create a Scatter Plot
Scatter Plot - Python - How to Create a Scatter Plot
Scatter Plot - R - How to Create a Scatter Plot
Scatter Plot - Interpretation
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.
Regression Plot - Introduction - General Theory and Dataset
Regression Plot - Excel - How to Create a Regression Plot
Regression Plot - Tableau - How to Create a Regression Plot
Regression Plot - Python - How to Create a Regression Plot
Regression Plot - R - How to Create a Regression Plot
Regression Plot - Interpretation
Regression Plot - What Makes a Good Regression Plot
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.
See All Modules
Why Choose the 365 Data Science Program?
Practice
Real-life project and data. Solve them on your own computer as you would in the office.
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Trust the other 500,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense Đ°of how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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