Data Science vs. Big Data vs. Data Analytics

Content by Simplilearn

Firstly, data is part of our daily lives more than most of us realise in our daily lives. Moreover, the amount of digital data that exists is growing. According to estimates, in 2021, there will be 74 zettabytes of generated data. Therefore, there is a need for professionals who understand the basics of data science, big data, and data analytics. 

While these three terms are prominent in the industry their meanings share some similarities, they also mean different things.

Data Vacancies

What Is Data Science?

Data science combines statistics, mathematics, programming, problem-solving, and ingeniously capturing data. Additionally, The ability to look at things differently and the activity of cleansing, preparing and aligning data. This umbrella term includes various techniques used when extracting data insights and information.

What is Big Data?

Big data is data that you cannot process effectively with traditional applications. Big data processing begins with the essential information you don’t aggregate and is impossible to store in a computer’s memory.

Furthermore, big data can inundate a business daily, a buzzword used to describe immense volumes of unstructured and structured data. Big data helps to analyse insights, leading to better decisions and strategic business moves.

What is Data Analytics?

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 run through several data sets to look for meaningful correlations. Several industries and companies use data analytics to make informed decisions and verify theories or models. In addition, data analytics focuses on inference, which is the process of deriving conclusions solely based on what the researcher already knows.

Now, let us move to applications of data science, big data, and data analytics.

Data Science Applications

• Internet Search engines use data science algorithms to deliver the best results for search queries in seconds.
• The digital marketing spectrum uses data science algorithms, from display banners to digital billboards; this is the main reason digital ads have higher click-through rates than traditional ones.
• The recommender systems make it easy to find relevant products from billions of available products. Many companies use this system to promote their products and suggestions following the user’s demands and the relevance of information. The recommendations are based on the user’s previous search results.

Applications of Big Data

• Big Data for Financial ServicesCredit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment banks all use big data for their financial services. The common problem among them is the amount of multi-structured data living in multiple disparate systems, which big data can solve. There are several ways we use big data:
  1. Customer analytics
  2. Compliance analytics
  3. Fraud analytics
  4. Operational analytics
• Whether a brick-and-mortar company or an online retailer, the answer to staying in the game and being competitive is understanding the customer better, this requires analysing all disparate data sources that companies deal with daily, including weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data.

Data Analytics Applications

• The main challenge for hospitals is treating as many patients as efficiently as possible while providing a high. Furthermore, instrument and machine data are increasingly being used to track and optimise patient flow, treatment, and hospital equipment. It is estimated that a one per cent efficiency gain could yield more than $63 billion in global healthcare savings by leveraging software from data analytics companies.
• Travel data analytics can optimise the buying experience through mobile/weblog and social media data analysis. Thus, travel websites can gain insights into customers’ preferences. Therefore, products can be upsold by correlating current sales to the subsequent browsing increase in browse-to-buy conversions via customised packages and offers. Data analytics based on social media data can also deliver personalised travel recommendations. 
• Data analytics helps collect data to optimise and spend within and across games. Gaming companies can also learn more about what their users like and dislike.
• Most firms use data analytics for energy management, including smart-grid management, energy optimisation, energy distribution, and building automation in utility companies. The application focuses on controlling and monitoring network devices and dispatch crews and managing service outages. Utilities can integrate millions of data points in the network performance and allows engineers to use the analytics to monitor the network.

Data Scientist: Skills Required

• Education: 88 per cent have master’s degrees, and 46 per cent have PhDs
• In-depth knowledge of SAS or R. For data science, R is preferred.
• Python coding: Python is the most common language in data science, along with Java, Perl, and C/C++.
• 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.
• Although NoSQL and Hadoop have become a significant part of data science, it is still preferred if you can write and execute complex queries in SQL.
• It is essential that a data scientist can work with unstructured data, whether on social media, video feeds, or audio.

Big Data Specialist: Skills Required

• Analytical skills are essential for making sense of data and determining which data is relevant when creating reports.
• You need to have the ability to create new methods to gather, interpret, and analyse a data strategy. Mathematics and statistical skills: Good, old-fashioned “number crunching” is also necessary, whether in data science, data analytics, or big data.
• Computers are the backbone of every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
• Big data professionals will need to understand the business objectives in place and the underlying processes that drive the growth of the business and its profits.

Skills Required to Become a Data Analyst

• Knowing programming languages, such as R and Python, are imperative for any data analyst.
• Descriptive and inferential statistics, as well as experimental designs, are skills data scientists require.
• Machine learning skills
• The ability to map raw data and convert it into another format that enables more convenient consumption of the data
• Communication and data visualisation skills
• A professional must be able to think like a data analyst.

Salary Trends

Although they are in the same domain, each professional—data scientists, prominent data specialists, and data analysts—earn varied salaries.

Data Scientist Salary

According to Glassdoor, the average base salary for a data scientist is $113,000 per year.

Big Data Specialist Salary

According to Glassdoor, the average base salary for a big data specialist is $103,000 per year.

Data Analyst Salary

According to Glassdoor, the average base salary for a data analyst is $62,453 per year.

Of course, these are averages and will vary based on several factors. Many professionals earn—or have the potential to make—higher salaries with the right qualifications.