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What Is Data Visualization?

Data visualization is a method of displaying data in graphs, charts and maps to make it easy to understand for those without knowledge of the data set. Visualization efforts must include the insights received from data, trends and patterns found within the data, as well as a way to discern complex data in a simplified manner. Data visualization comes in two basic forms: static visualization and interactive visualization.

2 Types of Data Visualization

  • Static visualization refers to a method of displaying data that tells focuses on only a single data relationship.
  • Interactive visualization allow users to select specific data points in order to present findings and create customized visual stories to compare against each other.

 

Why Is Data Visualization Important?

Data visualization is important for communicating complex business insights and analysis results to all stakeholders in a simplified manner.

Data visualization is a method of understanding and displaying complex data and powerful insights. Strong data visualization allows for better communication with stakeholders throughout an organization, which is crucial to growing a business and capitalizing on new opportunities. The amount of raw enterprise data multiplies yearly and continually presents new information that, when analyzed, can help uncover trends regarding customer behavior, market evolution, overall consumer habits and more.

Data visualization, when preceded by the use of data mining and data modeling techniques, allows analysts to discover vital insights within large data sets. Data visualization helps analysts easily communicate those insights for immediate action.

Related Reading From Built In Experts7 Ways to Tell Powerful Stories With Your Data Visualization

 

What Are the 2 Types of Data Visualization?

The two basic types of data visualization are static visualization and interactive visualization.

 

Static Visualization

Static visualization refers to a method of displaying data that tells a specific story and focuses on only a single data relationship. A common example of static visualization is an engaging single-page layout like an infographic.

 

Interactive Visualization

Interactive visualizations, for the most part, only exist within software or web applications. This model allows users to select specific data points in order to present findings and create customized visual stories to compare against each other, thereby creating the opportunity for stakeholders to choose from a selection of insights to determine the best path forward, rather than deciding based on a single insight.

Both static and interactive visualization methods present opportunities to display data clearly and accurately. Data analysts should use their best judgment based on the target customer, data story and ROI when deciding on which visualization method to use.

The Beauty of Data Visualization — David McCandless. | Video: TED-Ed

 

What Are Data Visualization Best Practices?

Some best practices for data visualization include speaking to a specific audience, choosing a proper visualization and providing context.

It is crucial to follow best practices when presenting data visualizations:

  1. Know Your Audience: Data should always be used to tell a story and uncover trends. It’s vital to know who will be most interested in the information and tailor your visualizations so they can digest the data.
  2. Choose the Correct Visual: Data visualizations should always present the data in a way that makes it easy to understand. For example, a chart may be the best method of displaying data with a high degree of variability, while graphs may be better for displaying changes in data over time.
  3. Provide Context: Data without context isn't very helpful, so the data visualizations you choose to put the information in perspective is important. A good visualization will not only show the data is relevant and easily provable, but will also tell a cohesive story.  
  4. Keep It Simple:  Simple visualizations and dashboards go a long way in data visualization because they allow stakeholders to easily reference data and make informed decisions without becoming confused by the data’s purpose.
  5. Engage the User: Lastly, engagement is important when presenting complicated data to stakeholders. To prevent users from becoming overwhelmed or intimidated, the overall design and user experience should be graspable without being intimidating.
Courses

Expand Your Data Visualization Career Opportunities

Learn data visualization and other in-demand skills by taking one of Udemy’s expert-led data science courses.

General Assembly

Regardless of your industry or role, fluency in the language of data analytics will allow you to contribute to data driven decision making.

4.5
(462)
General Assembly

In this two hour live workshop you will walk through the typical data science workflow and see how the pros identify powerful business predictions. You’ll get first-hand experience to explore the key tools and…

4.5
(462)
Udemy

Topic: 

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!

 

What you'll learn

  • Have an intermediate skill level of…

4.5
(17077)
Udemy

Topic: 

Visualisation in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.

 

What you'll learn

  • Learn Complete…

4.2
(25845)
Certifications

Data Visualization Certifications + Programs

Take the next step in your career by earning a data science certification from Udacity.

General Assembly’s Data Science part-time course is a practical introduction to the interdisciplinary field of data science and machine learning, which lies at the intersection of computer science, statistics, and business. You will learn to use the Python programming language to acquire, parse, and model data for informing business strategy. 

This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics. There is an option to complete up to 25 hours of online preparatory lessons. Talk to the General Assembly Admissions team to discuss your background and confirm if this is the right fit for you..

 

What you'll accomplish

A significant portion of the course is a hands- on approach to fundamental modeling techniques and machine learning algorithms. You’ll also practice communicating your results and insights by compiling technical documentation and a stakeholder presentation. Throughout this expert-designed program, you’ll:

  • Perform exploratory data analysis with Python.
  • Build and refine machine learning models to predict patterns
  • from data sets.
  • Communicate data-driven insights to technical and non-technical audiences alike.
  • Apply what you’ve learned to create a portfolio project: a predictive model that addresses a real-world data problem.

 

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

 

Your next step? Submit an application to talk to the General Assembly Admissions team


 

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

 

General Assembly

General Assembly’s Data Analytics Immersive is designed for you to harness Excel, SQL, and Tableau to tell compelling stories with a data driven strategy. This program was created for analysts, digital marketers, sales managers, product managers, and data novices looking to learn the essentials of data analysis. 

 

What you'll accomplish

You will learn to use industry tools, Excel, and SQL to analyze large real world data sets and create data dashboards and visualizations to share your findings. The Data Analytics Accelerator culminates in a.

Throughout this expert-designed program, you’ll:

  • Use Excel, SQL, and Tableau to collect, clean, and analyze large data sets.
  • Present data-driven insights to key stakeholders using data visualization and dashboards.
  • Tell compelling stories with your data.
  • Graduate with a professional portfolio of projects that includes a capstone project applying rigorous data analysis techniques to solve a real-world problem

 

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

 

Your next step? Submit an application to talk to the General Assembly Admissions team


 

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

 

General Assembly

General Assembly’s Data Science Immersive is a transformative course designed for you to get the necessary skills for a data scientist role in three months. 

The Data Science bootcamp is led by instructors who are expert practitioners in their field, supported by career coaches that work with you since day one and enhanced by a career services team that is constantly in talks with employers about their tech hiring needs.

 

What you'll accomplish

As a graduate, you will be ready to succeed in a variety of data science and advanced analytics roles, creating predictive models that drive decision-making and strategy throughout organizations of all kinds. Throughout this expert-designed program, you’ll:

  • Collect, extract, query, clean, and aggregate data for analysis.
  • Gather, store and organize data using SQL and Git.
  • Perform visual and statistical analysis on data using Python and its associated libraries and tools.
  • Craft and share compelling narratives through data visualization.
  • Build and implement appropriate machine learning models and algorithms to evaluate data science problems spanning finance, public policy, and more.
  • Compile clear stakeholder reports to communicate the nuances of your analyses.
  • Apply question, modeling, and validation problem-solving processes to data sets from various industries to provide insight into real-world problems and solutions.
  • Prepare for the world of work, compiling a professional-grade portfolio of solo, group, and client projects.

 

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

 

Your next step? Submit an application to talk to the General Assembly Admissions team


 

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

 

General Assembly
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