Data Analytics vs Data Science – Know the Most Important Differences

If you are interested in the field of data analytics, it is important for you to cut through the hype and understand data analytics vs data science. In this article, you will learn about the recent, explosive origins and the driving technological forces behind this scientific discipline, key definitions, and a concise list of real-world applications to distinguish data analytics vs data science.

The Origins and Driving Forces Behind The Explosive Growth of Data Analytics and Data Science.

For centuries accountants, mathematicians, and statisticians have been crunching numbers. Now, thanks to computers these numbers and equations have turned into data and algorithms. Consequently, data analytics and data science have come into their own as a field of study and profession. Below is a summary of the origins and current circumstances faced by both the fields of data analytics and data science.

1. Computers And Its Data Output Are The Driving Forces For The Origins Of Both Disciplines.

In 1962 John Tukey, a mathematical statistician, published a paper, “The Future of Data Analysis”. In this paper he pointed to the existence of an as-yet unrecognized science called data analysis. Further, he pointed to the driving forces in data analytics which still apply today (see 50 Years of Data Science) for both data analytics and data science. First, is the phenomena of computers that continue to expand its capabilities to store, crunch, and display data. Second, is the ever increasing amount of data that computers are producing known as “Big Data”. Lastly, every industry and profession is coming to terms with “data wrangling” and quantifying “Big Data“.

2. Businesses Start Talking Data.

It was just in 2008 that we started hearing the buzzword “data scientist”. Also, businesses and IT departments started talking and working with “data”. Moreover, they started to regularly use terms like “Big Data“, business intelligence (BI) dashboards, and data analytics. Indeed, business leaders are now seeking to harness all this data and find ways to make it effective. Without reservation, they see leveraging data as key to the success of their business.

“Data are becoming the new raw material of business.”

Craig Mundle

3. We Are Now Awashed In Data Where We Need Data Professionals To Make Data Useful.

We are now awashed in data. According to TechJury,  “if you were to take all of the data generated by humanity in 2020 and divide it among the world’s population, you’d find that each person created 1.7 megabytes of data every second. In fact, it’s estimated that more than 90 percent of the total data created by humans has been generated in just the last two years.”  It is now obvious that businesses need data professionals. For instance, Data analytics and data science are both popular fields where the demand for data professionals was over 2.7 million by 2020.

Data Analytics Vs Data Science – Definitions.

You need to beware of these fuzzy “Big Data” terms like data analytics, data science, and data mining. For example, Dan Ariely, a well-known behavioral economics expert, says this about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

Employers literally have thousands of job openings for  both Data Analysts and Data Scientists. In many cases, companies are using the same job descriptions and listing the same skill sets for both job titles. Yet in a technical sense these terms are very different. On the other hand, both of these professions are growing rapidly due to “Big Data” and the fact that businesses do need both data scientists and data analysts. To better understand and distinguish the differences between these two job titles, I like these definitions:

• Data Scientist. Uses scientific methods (data science) to liberate and create meaning from raw data (ref: Data Science Association). So a data scientist conducts basic and applied research using data. They use the scientific method to create meaning, data models, and software applications.
• Data Analyst. Examine large data sets to identify trends, develop charts, and create visual presentations (i.e. data analytics) to help businesses make more strategic decisions (ref: Northeastern University). So the data analyst uses a wide range of software tools (software applications, algorithms, methods, etc.) applied against large data sets. With this data, they create actionable information for decision makers. Many, if most, of these software tools that data analysts use originated from data science.

For a more detailed discussion on the definition and span of data science, see SC Tech Insights’ Data Science Definition – The Truth About This Discipline And Its Massive Growth.

Data Analytics Vs Data Science – A Breakout Of Each Discipline By Subtype.

4 Types Of Data Analytics

You can apply data analytics in a variety of ways. In particular, this includes budgeting and forecasting, risk management, marketing and sales, and in product development. Below are four types of data analytics:

1. Descriptive Data Analytics. Examine, understand, and describe something that’s already happened. For example, a dashboard displaying year-over-year pricing changes.
2. Diagnostic Data Analytics. Seeks to understand the why behind what happened. For example, examine the market to determine the reasons behind product demand.
3. Predictive Data Analytics. Relies on historical data, past trends, and assumptions to answer questions about what will happen in the future. For example, real estate brokers provide projected home values to buyers.
4. Prescriptive Analytics. Identify specific actions that should be taken to reach future targets or goals. For example, a transportation company provides cost-effective delivery through better route planning and auto-correction of shipping addresses.

The 6 Divisions Of Data Science.

Data science, its domain knowledge, and its software tool sets are rapidly growing as well as the amount of data in general. Additionally, data scientists apply data science across multiple disciplines, organizations, and industries. To illustrate, David Donoho, a professor of statistics at Stanford, describes and classifies the various activities of data science in 50 years of Data Science. Specifically, he describes GDS (Greater Data Science), the science of learning from data, as divided into six divisions. To list, the 6 types are 1) Exploring and Preparing Data; 2) Representing and Transforming Data; 3) Computing with Data; 4) Modeling Data; 5) Visualizing and Presenting Data; 6)Science about Data Science. For a more detail description, see SC Tech Insights’ 6 Divisions of Data Science.

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