Data Analytics vs. Data Visualization [+Comparison Table]

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Analytics vs. Visualization: An Introduction to the World of Data

The amount of data that exists today is unfathomable. For example, at the beginning 2020 it was estimated that there were 44 zettabytes of data in the world, but by 2025 it is forecasted that we will generate 463 exabytes every day. The sheer volume is hard to wrap your head around, but one vital approach we have in our repertoire to understand and break down all this data is data analytics and visualization.

These two disciplines are critical for countless aspects of day-to-day life and business. Data analytics and visualization impact health care, industry, business, government and politics, education, and more. But what does data visualization and analytics actually entail? And what are the differences and how do they work together? Read on for answers to those questions and more.

What is Data Visualization? 

IBM defines data visualization as “the process of translating large data sets and metrics into charts, graphs, and other visuals to discover hidden patterns. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data.”

Why Data Visualization is Important

Because data analytics deals with a wide variety of data sets, having a tool that clearly displays key determinations from these metrics is critical. This is why data visualization is so important — it displays this information in a manner that can be understood by a wide audience.

TechTarget, a leading digital technology resource, states that there are six primary benefits an organization can expect from utilizing data visualization.

  1. The ability to absorb information quickly, improve insights, and make faster decisions.
  2. An increased understanding of the next steps that must be taken to improve the organization.
  3. An improved ability to maintain the audience’s interest with the information they can understand.
  4. An easy distribution of information that increases the opportunity to share insights with everyone involved.
  5. An increased ability to act on findings quickly and, therefore, achieve success with greater speed and fewer mistakes.

What is Data Analytics? 

According to online dictionary Investopedia, data analytics is the process of analyzing raw data in order to make conclusions about that information. To do that, data analytics relies on a number of advanced tools and technologies, such as Tableau and Python and R. And because data analytics is largely digital, many aspects of these processes can be automated and used to support other advanced tools, like machine learning.

There are four types of data analytics, which can also be explained as the four stages of data analysis, as they occur in consecutive order:

  • Descriptive analytics: “a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.”
  • Diagnostic analytics: “a form of advanced analytics that examines data or content to answer the question, ‘why did it happen?’.”
  • Predictive analytics: “the use of statistics and modeling techniques to determine future performance.”
  • Prescriptive analytics: “a form of advanced analytics which examines data or content to answer the question, ‘what should be done?’.”

Why Data Analytics are Important

Data analytics are critical in today’s world because the answers to important questions and complicated problems lie in these complex data sets. Data analytics are helping physicians find patterns that improve diagnostic accuracy, the world of education can use data analytics to identify areas students need help with, and even in the fight against COVID-19, data analytics played an important role in helping government and health officials make decisions that saved lives.

In the business world specifically, data analytics is important because it helps businesses optimize their performance across a number of operating areas. When businesses rely on data analytics, they can reduce costs, improve productivity, increase efficiencies and ultimately drive higher customer satisfaction.

What's the Difference Between Data Analysis and Data Analytics?

Data analysis and data analytics are often used interchangeably, but they are not the same thing. Data analytics refers to the entire concept and practice of analyzing raw data to make determinations. Data analysis is a subcategory of data analytics, and refers to a few specific actions that are performed during data analytics. These processes are cleaning, transforming, modeling, and questioning data.

Data Analytics vs. Data Visualization 

 

Data Analytics

Data Visualization

Purpose

To analyze raw data in order to make conclusions about that information that can be used to inform decision-making processes.

To use visual representations of data to more easily identify trends, outliers, and new insights about the information represented in the data.

Selected Tools

Python, R, Excel, RapidMiner, Apache Spark

Tableau, Qlikview, FushionCharts, Sisense, Google Charts

Benefits

Identify patterns, inform data visualization, initiate artificial intelligence and machine learning projects, help improve business operations

Create clarity around complex data sets, help forecast future behavior

Career Paths

Business intelligence analyst, Data analyst, Marketing analyst, Systems analyst

Data visualization practitioner, Business analyst, Data visualization analyst

Data analytics and data visualization, while they are different disciplines, have a closely related if not symbiotic relationship. Data visualization supports data analytics in that it clearly communicates the outcomes and patterns in data, and gives end users a new perspective on that information. And data analytics supports data visualization by providing the critical ingredients needed to create visual models, charts, and displays.

How are Python and R Related to Data Analysis and Visualization?

Data Analytics

Python and R are two of the most widely used and in-demand skills in the field of data analytics. To work in data analytics in any capacity from entry level to senior manager, candidates must be knowledgeable if not proficient in these coding languages.

In day-to-day practice, R is designed for statistical analysis of data while Python is a more general coding language that is used in data analysis, but also supports advanced machine learning initiatives. Python is so highly regarded in this field because it allows data analysts to complete their tasks in a faster, more stable manner. On the other hand, R is preferred by statisticians for analyses and breaking down complex data-driven projects into subsets that are easy to understand and work with.

Data Visualization

Python and R are used for data analytics, it only makes sense that these programs also have visualization capabilities and powerful visualization packages. And because data analytics and visualization are so closely related, it is also critical for data analysts to be well-versed in data visualization techniques.

What is Data Modeling?

Data modeling, while connected to data analytics and visualization, is a slight deviation from these two disciplines. IBM defines data modeling as “the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized, and its formats and attributes.” In layman’s terms, data modeling is an expansion of data visualization.

Data Analytics & Visualization Formal Education Options

Working in the field of data analytics and visualization requires some type of advanced education and training. While there are always anomalies, the most common formal education paths you can take to launch a career in data analytics and visualization are:

  • Bachelor’s degree: A bachelor’s degree in data analytics/data science or other quantitative majors will provide you with a solid foundation to enter the field of data analytics and visualization. You will likely learn the basic tools and technologies utilized in this discipline and gain essential knowledge around data analysis ethics and practices. Bachelor’s degrees are typically four-year commitments with tuition totaling in the tens of thousands of dollars.
  • Master’s degree: Master’s programs are designed for current data analytics professionals and enthusiasts looking to advance up the career ladder, or for those looking to make a career change. A data analytics/data science master’s program will typically teach more specialized analytics concepts and tools, but will also focus on leadership skills. Master’s degrees usually take two to three years to complete and also have tuition totals in the tens of thousands of dollars.
  • Certificate program: Most data analytics and visualization certificate programs do not have prerequisites, but are more geared toward college-educated professionals who would like to excel in this in-demand field. Data analytics and visualization certificates will focus on a broad range of analytics topics, with an emphasis on the most critical tools and technologies, like, R, Python, and Tableau. A certificate program can be completed in months, and costs anywhere from $5,000 - $7,500.

Data Analytics & Visualization Formal Education Options

Q: What is data visualization?

A: Data visualization is “the process of translating large data sets and metrics into charts, graphs, and other visuals to discover hidden patterns.”

Q: What are examples of data visualization?

A: Any graph, chart, table, or visual example of data is data visualization. So things like bar charts, scatter plots, line graphs, histograms, waterfall charts, and tree diagrams are all examples of visualized data sets.

Q: What is data analytics?

A: Data analytics is “the process of analyzing raw data in order to make conclusions about that information.”

Q: Are there different types of data analytics?

A: There are four different types of data analytics, and they each refer to a different step in the data analytics process. The four types are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Q: What is data analytics and visualization together?

A: Data visualization supports data analytics in that it clearly communicates the outcomes and patterns in data, and gives end users a new perspective on that information. And data analytics supports data visualization by providing the critical ingredients needed to create visual models, charts, and displays.

Q: What's the difference between data analysis and data analytics?

A: Data analytics refers to the entire concept and practice of analyzing raw data to make determinations, while data analysis is a subcategory of data analytics, and refers to a few specific actions that are performed during data analytics.

Q: What is data modeling?

A: Data modeling is “the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures.”

 

Data analytics and visualization professionals are in demand by top organizations across nearly every industry. Just look at job listing sites to see just how widely available and high paying these careers can be. If you’re thinking of making the jump, consider earning your certificate in Data Analytics and Visualization Certificate from the University of San Diego. Our cutting edge program focuses on the latest data analytics tools and concepts, and is taught by experienced industry professionals and data scientists. To learn more about this or any of our online programs, contact our admissions team.

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