Language changes frequently, often without rhyme or reason. In scientific fields, it adapts and evolves as new discoveries are made and new needs arise. For example, the terminology we use in the technology world evolves at as blistering a pace as the technology itself.
Worse, what words mean to the laymen often translate into different terms in the industry. So, it’s understandable that some confusion about data terminology like “data science” and “data analytics” would arise.
So, what is the difference between data analytics vs data science? How do these two similar terms overlap, blend, and differ in meaning? Let’s dive deep and try to clear up the confusion.
What Is Data Science?
Let’s start off by clarifying some definitions. What is data science, according to industry insiders?
To simplify, data science is the overarching study of the creation, application, and future implications of datasets. A data scientist helps to built sets of data from the ground up, as well as clean and organize them for better presentation. Data scientists write algorithms to estimate unknown quantities. They also engage in much greater amounts of coding.
If a framework for organizing a given or necessary dataset doesn’t exist, that’s no problem for a data scientist. They create new systems to understand data the way any scientist would craft experiments to understand the world around them. A proper data scientist handles the visual presentation of data so that an outside observer can make correct decisions based on the provided information.
What Are the Applications of Data Science?
Now that we’ve covered an overview of what data science is, let’s delve into greater detail about what it does. What are the potential applications of data science?
In our modern world, the applications of data science are all around us. They exist in every industry from business to retail to healthcare. Some of the ways we see data science used nowadays include:
- Detecting fraud and risk for banks and other financial centers
- Analysis of medical photography
- Drug development and genetic testing
- AI assistance and customer support for retailers
- Voice recognition software
The applications listed above barely scratch the surface of what you can do with the proper application of data science. However, in order to make use of this field, you’ll need certain skills.
What Skills Does a Data Scientist Need?
Let’s discuss that in further detail now. What skills, precisely, does a data scientist need at their disposal? In order to succeed, a data scientist must have a solid grasp of:
Data Wrangling
Data wrangling is the process that supersedes data mining. Put more plainly, data mining is a part of the data wrangling process. This process involves cleaning and organizing data into more useable formats. This can also include merging multiple datasets, deleting irrelevant or outlying data, and identifying gaps in existing data.
Statistical Modeling
Statistical modeling includes running data through several different types of models to identify relationships and gain greater insight into the data presented. Common statistical models include regression, classification, and clustering.
Programming
As the more programming-heavy of the two fields, data science involves a greater knowledge of programming languages than data analysis. Common programming languages used by a data scientist include Python, R, and SQL. With proper training and understanding, a data scientist can write programs to compile and organize data much faster than doing it manually.
What Is Data Analytics?
What’s the biggest difference between data analytics vs data science? Where data science concerns itself with constructing programs to create and organize datasets, data analytics concerns itself with analyzing data to identify trends, gain insights, and answer questions.
To wit, data analytics can be considered a subfield of data science, which is why some data scientists may instead be called data analysts.
Depending on the type of analysts conducted, a data analyst will use different tools and skills. If you want to work at a large company like Peter Dodge Hanover Research, you’ll need to understand each type. The four major types of data analytics include:
Diagnostic Analytics
Diagnostic analytics does not stop at describing the datasets and what they present. Rather, it tries to dig deeper into the data presented to gain an understanding of why something coughed up the data it did.
Descriptive Analytics
Descriptive analytics, by comparison, is more surface-level. It seeks to understand, examine, and describe the data regarding something that happened.
Prescriptive Analytics
Think of prescriptive analytics as a prescription that a doctor would give you. Prescriptive analytics examines a given dataset to determine which actions a company or person should take based on the given information.
Predictive Analytics
A fortune-teller examines your body language and the information you give them about your past. So, too, does a predictive data analyst take in the information you provide them. In so doing, they can attempt to predict the course your venture will take based on the given data.
What Are the Applications of Data Analytics?
As with data science, the applications of data analytics are all around us. Data analytics sees use in a wide variety of fields and industries. These industries include, but are not limited to:
- Finding the best routes and estimating delivery times in shipping and logistics
- Delivering the most relevant search results to you within a search engine
- Planning around congestion, accidents, and other issues in transportation
- Discovering possible treatments for or causes of symptoms in medicine
However, where data analytics arguably sees the greatest amount of use is in the Business field.
Business Applications of Data Analytics
The business uses of data analytics are so varied and cast such a wide net that they require their own section to explore in the proper amount of depth. A business data analyst’s specific goal is to gain insight from a dataset to guide the company on a course that will help it reach its ultimate objectives. This often arises in four distinct departments.
- Research and Development: An analyst can look at how clients responded to previous products to guide new creations
- Budgeting and Forecasting: Analysts examine a company’s goals and past financial performance, crafting action plans
- Risk Management: A trained data analyst can help companies mitigate risk from new ventures or restructuring efforts
- Marketing and Sales: By far the largest application of business data analytics used to streamline the sales pipeline
Each of these business applications of data analytics is crucial to keeping the modern business running. However, despite their importance, businesses should be cautioned against buying too deeply into datasets. Or worse, examining the wrong sets of data to determine their overall success.
One need only look at the recent travails of Activision Blizzard to see the costs of examining and acting upon incorrect assumptions and datasets.
What Skills Does a Data Analyst Need?
Now that we’ve discussed the difference between data analytics vs data science and the applications of data analytics, let’s discuss the skills that a data analyst will need for their job. In order to have a successful career as a data analyst, you must have the following skills:
Strong Command of Microsoft Excel
“Must have a strong command of Microsoft Office/Microsoft Excel” might seem like a standard requirement in most office settings these days. However, it’s especially important in the field of data analytics.
That’s not just because of the ubiquity of the spreadsheet crafting program! Despite its nature as a simple spreadsheet maker on the surface, there’s a lot more analysis power hiding under the hood. With VBA lookups and written Macros, you can wrangle a decent amount of data analysis and compiling functionality out of this spreadsheet tool.
While it’s no substitute for a dedicated data analysis program, it’s often the tool of choice at newer or leaner operations.
Exceptional Critical Thinking
You can stare at datasets all day and get nothing out of them if you don’t know the right question to ask. Data analysts require exceptional critical thinking skills to sort out what questions they should ask to coax cohesive data narratives from the information before them. For the most part, this ability is an innate skill, but it can be trained if you keep yourself grounded in the practical needs of an organization.
Strong Data Visualization and Presentation Skills
You can analyze and understand data all day long. However, none of that matters to Big Data if you don’t know how to present your findings. This means you need to not only visualize your data for outside observers but know how to present it to them as well.
Data Analytics vs Data Science: Let’s Review the Differences
Let’s review what we’ve learned about data analytics vs data science. The main difference between the two lies in their scope and focus. Data science creates the programs and solutions that data analytics relies upon to convey the information to the public. The fields often overlap and require similar skillsets, so you don’t need to neglect one to study the other.
Do you need further help understanding data terminology or finding the tools to advance your career in Big Data? If so, check out our blog for more helpful articles like this one!