Best Courses in Data Visualization to Transform your Data Science Journey

Are you looking to get hands-on data visualization? We have curated a series of top courses in data visualization for you to get started.

Data visualization is considered one of the powerful tools in data analytics. It plays a vital role by converting the massive amount of intangible data into pictures and graphics that can be easily understood.

In this technological revolution, every industry is getting a massive amount of data. This massive data can be utilized for data visualization, where it can take all the raw data, model it, and compile all the essential data into visualizations that can be easily understood and interpreted. Suppose you are running a business and using a data visualization tool. In that case, you can quickly identify the areas to be improved, factors affecting customer satisfaction and dissatisfaction, and how to move with the specific products.

Data visualization will help all the business owners to make a better prediction on sales, volume, and future growth of the business. It is a tool for every business as it will drive success as your decisions will be based on the facts and not intuition.

So, to be an expert in the field, one must be well versed in both the data analysis approaches, namely the quantitative and qualitative data analysis. The quantitative data analysis works with the numerical variables that will determine the value of the business, while the qualitative data analysis mainly involves non-numerical information, this approach majorly asks questions like ‘what,’ ‘where,’ and ‘how.’ Such analysis is usually in the form of narratives or text which might also include audio and video depictions. The information provided through this data analysis tool can be used to improve the final product.

However, getting information out of the data is not always easy, as experts know how one can easily get lost in data. The more you accumulate, examine, and evaluate, the more likely you will want to explore it; here comes into light the Exploratory Data Analysis or EDA. It helps analysts, researchers, and business leaders utilize aesthetic visual tools to learn from the data. EDA is a technique in which scientists evaluate vast collections of information and recognize patterns and sum up the data’s main characteristics beyond what they gain from hypothesis screening and modeling.

EDA generally makes use of these four objectives:

  1. Exploring a single variable by considering trends over the time
  2. Validating data errors
  3. Inspecting assumptions
  4. Checking out relationships between variables

Altogether, the primary purpose of the EDA is to look up the data before making any assumptions, and the results will bring desired business outcomes and goals.

Our team of experts has curated a list of courses in data visualization for you that they think will be most helpful if you want to build your career or upskill yourself with the help of data visualization.

Along with the courses in data visualization, we have included hands-on projects. These projects are intended to help learners apply what they have learned immediately and give them a taste of the actual industry application.

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1. EDA using Python and/or R

Exploratory Data Analysis courses in data visualization using Python and/or R serve as a Launchpad for getting into this sophisticated technique, as Python and R programming languages are the two most widely used languages in the field of Data Science.

Why EDA with R?

When it comes to EDA with R, it is considered one of the best languages with better visualizations. So, most data scientists are interested to learn the R programming language. This EDA with R is for visualization and will summarize the data effectively and engagingly.

Why EDA with Python?

Python is considered a robust tool, and the EDA with Python is preferred by scientists who like to have elegant and informative visuals. EDA with Python will help draw meaningful patterns and insights, and even it will help the business in decision making.

Both Python and R; are suitable for EDA, but each tool has certain advantages and disadvantages over the other. EDA is mainly used for visualization and statistics, so most people prefer the R language as it is good at visualization and statistics.

i)  EDA using Python

Analyzing data through Python is often carried out with seaborn and matplotlib. Nevertheless, the syntax of seaborn and matplotlib can be intimidating to several learners. Enrolling in a full-fledged data visualization course using Python with EDX will help you learn and even earn a skill badge. So, the following are the skills that you are going to get through this course.

COURSE: Visualizing Data with Python

  1. Presenting data using libraries in Python that include Matplotlib, Seaborn, and Folium.
  2. Basic visualization tools such as bars, charts, histograms, and plots
  3. Specialized visualization tools such as box plots, pie charts, scatter plots, and bubble plots
  4. Advanced visualization tools such as waffle charts. Word clouds, Seaborn, and regression plots
  5. Creating maps and visualizing geospatial data.

HANDS-ON Project: Exploratory Data Analysis With Python and Pandas

Here are the two hours of the hands-on project from Coursera, where you will learn how to work with EDS using Python. Also, you will be using external Python packages such as Pandas, Numpy, Matplotlib, Seaborn, and others for analysis and to identify the missing data.

Step by step:

  1. Reading few data, short look over some rows, and calculate some summary statistics
  2. Analyze the variable, one at a time
  3. Looking at the relationship between the two variables
  4. Removing the duplicate rows and replacing the missing values
  5. Looking at the correlation of the numerical variable and interpreting them

ii) EDA using R

R programming’s ggplot2 is one of the best libraries for visualizations across any language. Therefore, many aspiring data scientists opt for R programming instead of Python. An online course on EDA in R presented by EDX will teach you the following points.

COURSE: Visualizing Data with R

  1. Basics of creating charts, pie charts, histograms, scatter plots and maps in R using packages, such as ggplot2.
  2. Customization of charts and plots using themes and other techniques (faceting).
  3. Create maps in R using the Leaflet package.
  4. Create interactive dashboards using the R package (Shiny).

Note: This data visualization course requires you to have at least some working knowledge of R.

HANDS-ON Project: Exploratory Data Analysis with Textual Data in R / Quanteda

This is a one-hour long project-based data visualization course, where you will learn how to import the textual data from raw text files and then turn these files into a corpus (collection) using the R package ‘quanteda’. Also, you will learn how to extract meaningful information and to create visualizations of textual data using various R packages (Stringr and ggplot2)

Step by step:

  1. Import textual data from raw text files.
  2. Turn files into a collection of text documents (corpus)
  3. Tokenize the text and turn text into a document of feature matrix
  4. You can extract meaningful information from the filenames
  5. Finally, from the textual data, you can be able to generate visualizations.

2. EDA using Tableau

Data visualization with Tableau is a carefully curated specialization course for beginners. The specialization consisting of 5 courses, including a project-based course, is the go-to choice of beginners looking to get hands-on with Tableau.

You can view live examples from real-world business cases and journalistic examples from leading media companies. Completing this course will help you generate powerful reports and dashboards that will help people with decision making.

COURSE:  Data Visualization course with Tableau Specialization

  1. Examine and learn the various features of Tableau
  2. Perform EDA and assess the quality of data
  3. Create visualization and dashboards
  4. Combine the data

HANDS-ON Project: The above specialization comes with the hands-on project, where you can create a portfolio using Tableau.

Step by step:

  1. Create a project proposal to identify the goals of the project
  2. Explore the data
  3. Ability to find the data for the information that you are seeking
  4. Import the data into Tableau
  5. Analysis the data
  6. Create a dashboard that will explore the in-depth data manner
  7. Giving structure to the data
  8. Create a data story in Tableau

3. EDA using PowerBI

Microsoft has introduced powerful and robust business analytics and visualization tool known as Power BI. It is widely used to draw insights from an organization’s data. The PowerBI is a tool not only used by corporates but also by students, researchers and academics.

COURSE: Analyzing and Visualizing Data with Power BI

This course is especially for beginners who want to work with data. It will help you to bring the data into life and to make it meaningful.

  1. Identify and work with business-related data.
  2. Import and prepare data
  3. Identify different types of data visualizations and their purpose
  4. Create the functional Power BI reports and dashboards.

HANDS-ON Project: Getting Started with Power BI Desktop

This 2-hour course will help you to learn everything about the basics of Power BI software. Power BI is a free business intelligence application that will load, transform, and visualize the data.

Step by step:

  1. Overview of Rhyme interface
  2. Importing data
  3. Fixing column names
  4. Transforming data
  5. Creating reports

and more…

4. EDA using Excel

i) Excel for Data Visualization

Excel for data visualization is one of the most commonly used tools. Data visualization for excel includes tools that contribute to data analysis with improved visualization and more sophisticated business logic. The data science course by EDX will cover all the aspects, thus making you industry-ready.

The expert from the Microsoft product team will introduce you to the new tools in Excel 2016 in detail. Moreover, you will also learn to gather and transform data from multiple sources, discover and combine data in mashups, data model creation and explore, analyze, and visualize data.

COURSE: Analyzing and Visualizing Data with Excel

  1. Gather and transform data from multiple sources
  2. Combine the data in mashups
  3. Learn about the data model
  4. Explore, analyze, and visualize data

HANDS-ON Project: Excel Basics for Data Visualizations

At the end of this hands-on project course, you will be able to create different types of charts and graphs by using excel. You will also learn to create an interactive dashboard and to shape the data in a meaningful way.

Overall, you will be polishing your basic skills required to create data visualizations and familiarizing yourself with their importance in storytelling.

Step by step:

  1. Data visualization
  2. Creating interactive dashboard
  3. Create charts (funnel and waterfall)
  4. Histograms and treemaps
  5. Create scatter charts and line charts
  6. Pie diagrams and bar charts
  7. Creating excel for a web account

ii) Interactive Data visualization using Excel

The charts and plots are considered the tools that are used in Excel to communicate the data graphically. The above hands-on project in excel from Coursera will teach you how to draw different types of charts in Excel, but Udemy’s course below will teach you how to create a chart. Also, from this course, you will learn all types of charting tools.

COURSE: Data Visualization Course in Excel: All Excel Charts and Graphs

  1. Learn the techniques to communicate through the chart.
  2. Courses come with numerous exercises to practice
  3. Get familiar with the charts in Excel
  4. Create 25+ chart types in Excel.
  5. Master newly introduced chart types like sunburst, waterfall, etc.
  6. Proficiency in using tools like a pivot table
  7. Learn cool infographics to represent data

Conclusion

It is no longer a fact that data science is an emerging field. Hence, it would be best if you began preparing for the same. However, the traditional methods are long gone. The above mentioned courses in data visualization are going to transform the way data is analyzed and interpreted. Nevertheless, the above-mentioned courses are sure to transform every aspiring candidate’s data science journey positively.

The best part is, most of these courses in data visualization provide an option to audit the courses, which means you can go and see for yourself if a particular course suits your needs. Moreover, the hands-on projects will allow you to put your knowledge into practice and make it easier to gauge your interest.

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