Data Science vs. Big Data vs. Data Analytics

Data is everywhere and part of our daily lives in more ways than most of us realize in our daily lives. Hence, there is a need for professionals who understand the basics of data science (DS), big data (BD), and data analytics (DA). Image result for big 3 data The amount of digital data that exists—that we create—is growing exponentially. According to estimates, in 2021, there will be 74 zetabytes of generated data. That’s expected to double by 2024. Hence, there is a need for professionals who understand the basics of DS, BD, and DA. These three terms are often heard frequently in the industry, and while their meanings share some similarities, they also mean different things. In this article, we will differentiate between DS, BD, and DA. We will cover what these terms mean, where they are used, the skills needed to become a professional in the field, and the salary prospects in each field. Let’s begin with understanding what these concepts are.

What Is Data Science (DS)?

Dealing with unstructured and structured data, DS is a field that comprises everything that is related to data cleansing, preparation, and analysis. Image result for Data Science DS is the combination of statistics, mathematics, programming, 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.

What Is Big Data (BD)?

BD refers to significant volumes of data that cannot be processed effectively with the traditional applications that are currently used. The processing of BD begins with raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer. Image result for Data Science A buzzword that is used to describe immense volumes of data, both unstructured and structured, BD can inundate a business on a day-to-day basis. BD is used to analyze insights, which can lead to better decisions and strategic business moves. Gartner provides the following definition of BD: “BD 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.”

What Is Data Analytics (DA)?

DA is the science of examining raw data to reach certain conclusions. Image result for Data Science DA 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 DA companies to make more informed decisions, as well as verify and disprove existing theories or models. The focus of DA lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows. Now, let us move to applications of DS, BD, and DA.

Applications of DS

  • Internet Search
Search engines make use of DS algorithms to deliver the best results for search queries in seconds.
  • Digital Advertisements
The entire digital marketing spectrum uses DS algorithms, from display banners to digital billboards. This is the main reason that digital ads have higher click-through rates than traditional advertisements.
  • Recommender Systems
The recommender systems not only make it easy to find relevant products from billions of available products, but they also add a lot to the user experience. Many companies use this system to promote their products and suggestions in accordance with the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.

Applications of BD

  • BD for Financial Services
Credit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment banks all use BD for their financial services. The common problem among them all is the massive amounts of multi-structured data living in multiple disparate systems, which BD can solve. As such, BD is used in several ways, including:
  1. Customer analytics
  2. Compliance analytics
  3. Fraud analytics
  4. Operational analytics
  • BD in Communications
Gaining new subscribers, retaining customers, and expanding within current subscriber bases are top priorities for telecommunication service providers. The solutions to these challenges lie in the ability to combine and analyze the masses of customer-generated and machine-generated data that is being created every day.
  • BD for Retail
Whether it’s a brick-and-mortar company an online retailer, the answer to staying in the game and being competitive is understanding the customer better. This requires the ability to analyze all disparate data sources that companies deal with every day, including the weblogs, customer transaction, social media, store-branded credit card, and loyalty program.

Applications of DA

  • Healthcare
The main challenge for hospitals is to treat as many patients as they efficiently can, while also providing a high. Instrument and machine data are increasingly being used to track and optimize patient flow, treatment, and equipment used in hospitals. It is estimated that there will be a one percent efficiency gain that could yield more than $63 billion in global healthcare savings by leveraging software from DA companies.
  • Travel
DA can optimize the buying experience through mobile/weblog and social media data analysis. Travel websites can gain insights into the customer’s preferences. Products can be upsold by correlating current sales to the subsequent browsing increase in browse-to-buy conversions via customized packages and offers. DA that is based on social media data can also deliver personalized travel recommendations.
  • Gaming
DA helps in collecting data to optimize and spend within and across games. Gaming companies are also able to learn more about what their users like and dislike.
  • Energy Management
Most firms are usingDA for energy management, including smart-grid management, energy optimization, energy distribution, and building automation in utility companies. The application here is centered on the controlling and monitoring of network devices and dispatch crews, as well as managing service outages. Utilities have the ability to integrate millions of data points in the network performance and gives engineers the opportunity to use the analytics to monitor the network.

Skills Required to Become a DS

  • Education: 88 percent have master’s degrees, and 46 percent have PhDs
  • In-depth knowledge of SAS or R. For DS, R is generally preferred.
  • Python coding: Python is the most common coding language that is used in DS, along with Java, Perl, and C/C++.
  • Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Having some experience in Hive or Pig is also beneficial.
  • SQL database/coding: Although NoSQL and Hadoop have become a significant part of DS, it is still preferred if you can write and execute complex queries in SQL.
  • Working with unstructured data: It is essential that a data scientist can work with unstructured data, whether on social media, video feeds, or audio.
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Skills Required to Become a BD Specialist

  • Analytical skills: These skills are essential for making sense of data, and determining which is relevant when creating reports and looking for solutions. Creativity: You need to have the ability to create new methods to gather, interpret, and analyze. Mathematics and statistical skills: Good, old-fashioned “number crunching” is also necessary, be it in DS, DA, or BD.
  • Computer science: Computers are the backbone of every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
  • Business skills: BD professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business and its profits.
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Skills Required to Become a DA

  • Programming skills: Knowing programming languages, such as R and Python, are imperative for any data analyst.
  • Statistical skills and mathematics: Descriptive and inferential statistics, as well as experimental designs, are required skills for DS.
  • Machine learning skills
  • Data wrangling skills: The ability to map raw data and convert it into another format that enables more convenient consumption of the data
  • Communication and data visualization skills
  • Data intuition: it is crucial for a professional.
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