Data is everywhere and part of our daily lives in more ways than most of us realize in our daily lives. 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.
These three terms are often heard frequently in the industry, and while their meanings share some similarities, they also mean different things. This article will give you a clear understanding of the meaning, application and skills required to become data scientist, Big Data specialist and data analyst, and other topics in detail too.
Let’s begin with understanding what these concepts are.
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What Is Data Science?
Dealing with unstructured and structured data, data science is a field that comprises everything that is related to data cleansing, preparation, and analysis.
Data science 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 from data.
What is Big Data?
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.
A buzzword that is used to describe immense volumes of data, both unstructured and structured, big data 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.
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.”
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 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 us move to applications of data science, big data, and data analytics.
Applications of Data Science
Internet SearchSearch engines make use of data science algorithms to deliver the best results for search queries in seconds.
Digital AdvertisementsThe entire digital marketing spectrum uses data science algorithms, from display banners to digital billboards. This is the main reason that digital ads have higher click-through rates than traditional advertisements.
Recommender SystemsThe 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 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 all is the massive amounts of multi-structured data living in multiple disparate systems, which big data can solve. As such, big data is used in several ways, including:
- Customer analytics
- Compliance analytics
- Fraud analytics
- Operational analytics
Big Data in CommunicationsGaining 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 data and machine-generated data that is being created every day.
Big Data for RetailWhether 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 data, social media, store-branded credit card data, and loyalty program data.
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Applications of Data Analytics
HealthcareThe 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 data analytics companies.
TravelData analytics 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. Data analytics that is based on social media data can also deliver personalized travel recommendations.
GamingData analytics 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 ManagementMost firms are using data analytics 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 Data Scientist
- Education: 88 percent have master’s degrees, and 46 percent have PhDs
- In-depth knowledge of SAS or R. For data science, R is generally preferred.
- Python coding: Python is the most common coding language that is used in data science, 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 data science, 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.
Skills Required to Become a Big Data Specialist
- Analytical skills: These skills are essential for making sense of data, and determining which data 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 a data strategy. Mathematics and statistical skills: Good, old-fashioned “number crunching” is also necessary, be it in data science, data analytics, or big data.
- 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: Big data 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.
Skills Required to Become a Data Analyst
- 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 data scientists.
- 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 to be able to think like a data analyst.
Although they are in the same domain, each of these professionals—data scientists, big 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.
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Data Analyst Salary
According to Glassdoor, the average base salary for a data analyst is $62,453 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.
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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 write-up Data Science vs. Big Data vs. Data Analytics, we discussed minor and major differences between Data Science vs. Big Data vs. Data Analytics such as 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? I suggest you check out the Simplilearn course.
The course has in-depth technical content on Data Science, Big Data, and Data Analytics.