Data is everywhere and part of our daily lives in more ways than most of us realize. The amount of digital data that exists—that we create—is growing exponentially. According to estimates, global creation of data will top 180 zetabytes.
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Therefore, there is a need for professionals who understand the basics of data science, big data, and data analytics, and can do comparisons such as data science vs data analytics, which help differentiate between the various data processing disciplines.
These three terms are often heard frequently in the industry, and while their meanings share some similarities, they have some profound differences. This article will give you a clear understanding of the meaning, application and skills required to become a data scientist, big data specialist, or data analyst.
Let’s begin by examining each concept separately.
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Data science is a field that deals with unstructured, structured data, and semi-structured data. It involves practices like data cleansing, data preparation, data analysis, and much more.
Data science is the combination of: statistics, mathematics, programming, and problem-solving;, capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data. This umbrella term includes various techniques that are used when extracting insights and information from data.
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Big data refers to significant volumes of data that cannot be processed effectively with the traditional applications that are currently used. The processing of big data begins with raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.
Big data is a buzzword used to describe immense volumes of data, both unstructured and structured, that can inundate a business on a day-to-day basis. Big data is used to analyze insights, which can lead to better decisions and strategic business moves.
In summary, Gartner provides the following definition of big data: “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
Data analytics is the science of examining raw data to reach certain conclusions.
Data analytics involves applying an algorithmic or mechanical process to derive insights and running through several data sets to look for meaningful correlations. It is used in several industries, which enables organizations and data analytics companies to make more informed decisions, as well as verify and disprove existing theories or models. The focus of data analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.
Now, let’s explore the applications of data science, big data, and data analytics.
My decision to upskill myself in data science from Simplilearn was a great choice. After completing my course, I was assigned many new projects to work on in my desired field of Data Analytics.
Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.
Data has become the engine that drives almost all of today’s activities, no matter if they’re in the fields of healthcare, technology, education, research, or retail. Additionally, business orientation has evolved from a product-focused model to a data-focused one. Companies of all sizes value information, no matter how trivial that data may seem at first glance. Information analysis and data visualization helps marketers and analysts acquire actionable business insights. This demand has created a need for experts who can pull useful, meaningful insights out of the terabytes of data available today.
While big data helps banking, retail, and other industries by supplying important technologies like fraud-detection and operational analysis systems, data analytics enables industries like banking, energy management, healthcare, travel, and transport develop new advancements by utilizing historical, and data-based trend analysis. Data science expands on that in more ways by enabling companies to explore new strategies in scientific discovery, medical advancements, web development, digital advertisements, ecommerce – literally, anything you can imagine.
In an effort to better understand the whole data science vs. data analytics comparison, let’s take a look at what each occupation does.
Data scientists work closely with business stakeholders to gain an understanding of their goals, and figure out how to use data to meet those goals. They are responsible for cleaning and organizing data, collecting data sets, mining data for patterns, refining algorithms, integrating and storing data, and building training sets.
As for Big Data professionals, well, the term “Big Data” is no longer a “big” thing when describing a career or job position. Big Data professionals are now known more as analytics professionals who review, analyze, and report on the massive amounts of data stored and maintained by the company. These professionals identify the challenges of Big Data and devise solutions, employ fundamental statistical techniques, improve the quality of data for reporting and analysis, and access, modify, and manipulate the data.
Finally, data analysts collect, clean, and study data sets to turn them into actionable resources to help solve problems or meet goals within the organization.
If it seems that the three occupations have a significant amount of overlap, that’s because they do! Each business has its own structure and procedures, and you are bound to see some blurring of the distinctions between these positions. Perhaps, in some companies, the data scientist wears multiple hats.
My decision to upskill myself in data science from Simplilearn was a great choice. After completing my course, I was assigned many new projects to work on in my desired field of Data Analytics.
Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.
Although they are in the same domain, each of these professionals—data scientists, big data specialists, and data analysts—earn varied salaries.
According to Glassdoor, the average base salary for a data scientist is over $117,000 per year.
According to Glassdoor, the average base salary for a big data specialist is over $104,000 per year.
According to Glassdoor, the average base salary for a data analyst is over $69,000 per year.
Of course, these are just averages and will vary based on several factors. Many professionals earn—or have the potential to earn—higher salaries with the right qualifications. For more details, you can also check out this salary calculator.
No matter which path you ultimately decide to take, Simplilearn has dozens of data science, big data, and data analytics courses available online. If you’d like to become an expert in data science, data analytics or big data, check out our Post Graduate program in Data Science, Data Analytics, and Data Engineering.
With industry-recommended learning paths, exclusive access to experts in the industry, hands-on project experience, and a master’s certificate awarded upon completion, these online courses will give you what you need to excel in these fast-growing fields and become an expert.
From the video below understand the difference between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field.
In this article, we discussed minor and major differences between Data Science vs. Big Data vs. Data Analytics, touching upon concepts like definition, application, skills, and salary-related to the specific position.
Are you planning to take a course on Data Science, Big Data, or Data Analytics? If you do, we suggest you visit Simplilearn, and take advantage of the excellent courses dedicated to those concepts. Simplilearn’s courses offer in-depth technical content on Data Science, Big Data, and Data Analytics.
If you have any questions related to this article Data Science vs. Big Data vs. Data Analytics, please drop your queries in the comments section below.
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