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What is Data Science: Meaning and Scope

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Today while I was going through some Data Science and machine blogs online, looking for user guides, case studies, and tutorials, I was amazed by some of the remarkable articles on Data Science. Written by eminent scholars and Data Science influencers in India and abroad, these online resources not only answer the basic premises of what is Data Science but also share advanced concepts. Data Science as a subject of research and a hot career option has gained tremendous momentum in the recent past.

According to the 2017 Forbes report on Data Science prospects, more than 190,000 data scientists will be required in 2018. What more? The number does not even include the 1.5 million more analysts and leaders needed to work on the information big data supplies.

With the increased role of automation, machine learning, robotics and AI, more menial jobs are being replaced by technology. However, Data Science has immense potential for professionals to move up the value chain and find work opportunities in areas where machines do not have a role to play. Artificial Intelligence and machine learning are building upon the digital revolution and are bringing another wave of automation. Having relevant qualification and expertise in this field can certainly give Data Science professionals a competitive advantage over others.

Data Science being one of fastest growing industries today, requires you to stay updated with the latest trends in technology, tools, industry trends and job opportunities, to stay afloat as a data practitioner. In this discussion, I aim to discuss Data Science definition, techniques, relation to Big Data and analytics. I have also addressed certain common queries on a career in Data Science; the logical path, the options for transitioning into a Data Scientist role, or suggested certifications for getting into a junior role.

What is Data Science

World of Data Science

What is Data Science:

Data Science as a multi-disciplinary subject encompasses the use of mathematics, statistics, and computer science to study and evaluate data. The key objective of Data Science is to extract valuable information for use in strategic decision making, product development, trend analysis and forecasting.

Data Science concepts and processes are mostly derived from data engineering, statistics, programming, social engineering, data warehousing, machine learning and natural language processing. The key techniques in use are data mining, big data analysis, data extraction and data retrieval.

Data Science Definition:

Data Science may be defined as a multidisciplinary blend of data inference, algorithm development, and technology to solve complex data analysis issues. Data Science deals with identification, representation, and extraction of meaningful information from data sources to be used for business purposes. Data engineers are responsible for setting up the database and storage to facilitate the process of data mining, data munging and other processes.

What is Data Science

Text Data

Data Science Benefits:

Data Science has several benefits. Here, I have listed some of the best things about Data Science:

  • Better business value:

The principal advantage of enlisting Data Science in an organization is faster and better decision-making. These data-driven decisions, in turn, lead to higher profitability and improved operational efficiency, business performance, and workflows. Data Science helps to identify and refine target audiences in customer-facing organizations.

  • Identification and refining of target audiences:

Data gleaned from Google Analytics to customer surveys, need to be analyzed to identify demographics. A Data Science professional helps with the identification of the key groups with precision, via a thorough analysis of disparate sources of data. Organizations can also tailor services and products to customer groups and help profit margins flourish.

  • Better risk analysis:

Predictive analytics, fueled by Big Data and Data Science allows users to scan and analyze news reports and social media feeds to stay updated on the latest industry trends. Moreover, it also promotes detailed health-tests on your suppliers and customers. This is useful for assessing risks and taking necessary action for mitigation well in advance.

  • Recruit better in lesser time:

Sifting through thousands of resumes is one of the biggest pains of any recruiter. Thanks to Big Data, finding the right candidate is much easier now, with the vast amount of profiles available through social media, corporate databases, and job search websites. Data Science professionals find it much easier to sort the data and hunt the best of candidates for the organization.

Through data mining, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, Data Science can help your recruitment team make speedier and more accurate selections.

What is Data Science

Data Science vs Big Data vs Data Analytics

Relation of Data Science to Big Data:

Data scientists work with Data Science and Big Data for data analysis. The question that is most commonly asked is: What is the basic difference between Data Science and Big Data? and, are the two related?

What is the basic difference between Data Science and Big Data? Well, I may say that both are independent fields, and their techniques are used in combination to solve various data problems.

Data Science to Big Data, despite being individual fields, have a lot in common. To understand their interrelation better let us first understand Data Science definition and then can move on to Big Data.

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What is Data Science:

Data Science is a field that covers data cleansing, preparation, and analysis. It is an umbrella term that includes several scientific methods, such as mathematics, statistics, and many other tools scientists apply to extract knowledge from data sets.

First, the Data scientist gathers datasets from multi-disciplines and compiles it. Next, he applies machine learning, predictive and sentimental analysis to analyze the data and eventually deciphers a meaningful pattern out of the huge datasets. A data scientist makes use of tools and languages like R, MATLAB, and DB Management for data analysis and machine learning.

What is Big Data:

Big Data, on the other hand, is an advanced science that aims at finding solutions to process, store and extract relevant information in a huge quantity of data, which can be relational (as in Excel Sheets or databases) or non-relational (such as tweets, images, or video).

Example of Big Data Tools: Apache Hadoop (free) and Bluemix of IBM.

Big Data management and processing strategies are part of Data Science. Data is collected to perform some analysis on it, therefore, data analysis is part of Data Science. Since all analysis problem or patterns cannot be identified with statistical analysis, machine learning is often used to identify patterns from data.

Understanding the basic differences between Data Science and Big Data is critical for organizations making a heavy investment in data strategy. Data is a competitive asset to any organization. The initial investment should be on converting data into value. Companies should focus on the Data Science solutions required to build models that move data from raw to relevant. Accordingly, Big Data approaches can be adapted to work in tandem with Data Science. This would also optimize data extraction and discovery in faster and better ways.

What is Data Analytics:

Data Analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is also known as data analysis.

What is Data Science

Data Science and Data Analytics

Data Science and Data Analytics:

Data Science is a broad term that encapsulates scientific methods, mathematics, statistics, and other tools that are used to analyze and manipulate data. In practice, Data Science boils down to connecting information and data points to find connections that can be made useful for the business

If Data Science is the box for the tools and methods, data analytics is one of the chambers in the box. It is related and like Data Science, but more specific and concentrated. Data analytics is generally more focused than Data Science because unlike Data Science, it does not stop at looking for connections between data; data analysts sort through data to look for ways to support. Data analytics is often automated to provide predictive insights.

Data analysis involves examining data to find useful information for achieving organizational goals. Analytics sorts data into things that organizations may or may not be aware of and can be used to measure events in the past, present, or future. Data Analytics often moves data from insights to impact by connecting trends and patterns with the company’s true goals and tends to be slightly more business and strategy focused.

The Data Scientist and Data Analyst are different. The Data Scientist starts by asking the right questions, while Data Analyst starts by mining the data. The Data Scientist needs substantive expertise and non-technical skills whereas a Data Analyst should have soft skills like intellectual curiosity or analytical skills.

What is Data Science

Data Science Career

Data Science Career:

According to Glassdoor’s Best Jobs in America list in 2016 and 2017, with 4,84 positions were available and with a median base salary of $110,000. DevOps engineer came in second, with a median base salary of $110,000 and 2,725 job openings. Data engineer rounded out the top three, with 2,599 job openings and a median base salary of $106,000. The average salary for a data scientist with fewer than five years’ experience in 2016 was $92,000.

How to be a Data Scientist:

While Data Science is fast growing, enlarging its scope and expanse, both as a field of learning and career choice, many are not clear about the specific skills required for exploring the career opportunities. Data Science as a multi-disciplinary field revolves around reading and processing data, pulling knowledge from that data. Having a qualified resource pool is essential for useful data conversion.

Data Science is for people who have a penchant for analyzing and explaining information in an intriguing manner. Data Science career is ideally suited for analytical and mathematical minds to analyze data. Having technical skills (in computer science, end-to-end development, and coding) is also a necessary requirement.

What is Data Science

Data Science skills

An aspiring Data Scientist should also a good communicator so that he can present complicated data insights in an interesting manner. Other soft skills like team spirit, business acumen, and the quest for knowledge are important.

Most Data Scientists work as researchers, but there are other roles available as developers or in business management. You may start working as a business analyst or a programmer and later switch to Data Science, with some years of experience.

Read my earlier post on key skills required for Data Scientist to prepare yourself better for winning positions.

Be a Data Scientist today and explore the abundant career options in Data Science. You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Applications. Students with a master’s degree in Economics or Social Science can also be a data scientist. Take up a Data Science or Data Analytics course, to learn Data Science skills and prepare yourself for the Data Scientist job, you have been dreaming of.

You may read How to Create a Killer Data Analyst Resume for creating CVs that leave an impression in the mind of the recruiters.

You may also enroll for a Data Analytics course for more lucrative career options in Data Science.  Industry-relevant curriculum, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.

A self-starter technical communicator, capable of working in an entrepreneurial environment producing all kinds of technical content including system manuals, product release notes, product user guides, tutorials, software installation guides, technical proposals, and white papers. Plus, an avid blogger and Social Media Marketing Enthusiast.

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