Data Science Vs Data Analytics – Difference Simplified

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In such a Data-driven world as today, Data Science and Data Analytics form a major component and play an important role in helping to understand the true essence of the Data collected from various sources and extract every possible insight from them to increase business gains and run a smooth business with high levels of profit and productivity. Since the whole Database job involves not only its creation but its proper analysis, interpretation, and comprehension using the right tools, it can sometimes be an overwhelming job to do manually and this is where the skills of Data Science and Data Analytics come into the picture.

The Skills obtained through Data Science course and Data Analytics course help one to extract the true meaning of the large pile of data in front of them and allows them to interpret it in such a way that provides them with valuable insights and other relevant information that will make them one of the more important people in the business organization. This is because they, after such interpretation, will create reports of all the viable solutions with various plans that the business organization could take up to increase profits, productivity, and thus the overall growth of the business.

Often times, it can get quite confusing to be able to differentiate between Data Science and Data Analytics, and it can seem as though the difference between them blurs in a lot of areas. However, despite the two fields being quite interconnected, they offer different outputs and follow different approaches. It is thus vital to clear the air on what the two fields, viz., Data Science and Data Analytics, bring to the table and what values and benefits your business organization can get out of them.

So, what is Data Science in the first place?

Data Science is a multidisciplinary study that places its primary focus on looking for insights that are actionable, which are obtained from very large sets of data that is at this stage still quite raw and unstructured. This field of Data Science seeks to figure out the answers and viable options to the hidden business issues that weren’t as explicitly understood.

In order to do so, Data Scientists use a plethora of techniques and tools which include the knowledge and use of computer science, statistics, predictive analysis, and machine learning to make their way through such massive volumes of databases so as to be able to lay down clear cut solutions to issues that might be hindering business efforts that haven’t been looked into with such importance yet.

The main goal of a Data Scientist is to question and look for the areas of study that offer some potential. In essence, they look for and place a high amount of emphasis on finding the perfect question to ask rather than looking for and concerning themselves with finding extremely specific answers. Data Science experts and professionals accomplish this task by being able to predict trends that seem to have some potential, exploring data sources that are disparate or disconnected in-depth, and thus finding better methods to analyze information for the benefit of the business organization.

Now, what is Data Analytics?

The field of Data Analytics is one that is focused on the processing and performance of the statistical analysis and the effect that will have on the existing sets of databases. Data Analysts primarily focus on the creation of newer ways to capture or collect the data from various sources, process it, and organize it in a manner by which actionable insights can be unearthed so as to be able to solve the current problems faced by the business organization. Then, they also come up with and establish the best possible manner in which this valuable data will be presented.

In essence, Data Analytics work towards uncovering answers to the issues the business organization doesn’t know and thus is directed towards the production of results and outputs that seek to facilitate immediate gains and improvements for the business organization in terms of profit, productivity, and its overall growth.

Within its ambit, Data Analytics also includes some different branches of broad study statistics and analysis, which assist in the efficient compilation of data that is obtained from different sources while also effectively locating any connection that might seem to exist, and this will help drastically simplify the results.

Thus, what is the difference between Data Science and Data Analytics?

Many people often use the terms Data Science and Data Analytics interchangeably, but they are actually quite unique fields of study that have great potential in them for future career success. A major difference between Data Science and Data Analytics lies in their scope. While Data Science is very much like an umbrella term that encompasses a group of fields of study that are associated with the comprehension, analysis, and interpretation of large sets of data; Data Analytics is a more narrowed down and focused partner which can be viewed as a part of the whole process at large since Data Analytics is dedicated to extracting actionable insights that can be used immediately subject to the already existing queries.

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Another difference between the two fields of the study lies in their sense of exploration. Data Science is not as much concerned with finding the solution to specific issues or queries but is rather keen on sifting through large sets of data, even in unstructured methods to expose insights that aren’t very obvious. Thus, Data Science offers broader insights that hint at the questions that should be looked into. Data Analytics, on the other hand, works with a narrower perspective and looks for specific answers to queries that have already been identified. Thus, Data Analytics provides solutions to the questions already asked, in a sense suggesting that Data Analytics is a process that follows Data Science. Data Science is more concerned with the questioning part of the process, while Data Analysis is concerned with the solution aspect.

Data Science is a collection of different tools, principles, and algorithms to find patterns from raw data, and Data Analytics is the way to go for increasing productivity and gains in businesses. Data Analytics deals with the drawing of conclusions from the data information with the help of different techniques that are tailored according to organizational requirements.

Impliedly, Data Science has a larger scope to it than Data Analytics. Data Science is used in fields such as Artificial Intelligence, Machine Learning, Corporate Analytics, Search Engine Engineering, and many more. Data Analytics is used in fields such as Healthcare, Industries, and businesses that are in immediate requirement for data and analysis as well as in the gaming and travel industries.

Data Science lays down and establishes the critical foundations and sifts through large amounts of data to create primary observations, figure out future trends that are based on potential insights that are quite significant. Such information at this stage itself can be used in fields such as modeling, enhancing artificial intelligence algorithms and machine learning, since it suggests how the information is being sorted and questions how it can be improved.

A Data Scientist has to perform the tasks of exploratory analysis of data; process, organize and verify the data integrity; spot trends in the data, and make predictions based on potential and hence generate actionable insights using various skills and tools. A Data Analyst has to perform the jobs of exploratory analysis of data as well, but in addition to that will have to ‘clean’ the data; discover newer patterns and trends using statistical knowledge and tools, and develop visual representations and reports of the analyzed data.

Data Science requires the knowledge of database systems such as MySQL, Hive, and others, while Data Analytics requires the knowledge of a predominantly statistical nature. Data Scientists must also possess the knowledge of python and SQL database coding, be able to work with unstructured data, and have an in-depth knowledge of SAS/R. A Data Analyst must-have machine learning and programming skills, mathematical knowledge, proficiency in Spreadsheet usage, SQL, R, and Python, along with the requirement for Data Visualization skills.

The two fields of Data Science and Data Analytics go hand in hand. Data Science provides the questions and not so much the answers. Coupling it with Data Analytics, we have the perfect questions to find the answers to which will be provided by Data Analytics, which can be implemented immediately, and thus turning such actionable insights into solutions of practical applicability. Data Science is associated with the formation of the algorithm, which is then later developed by Data Analysts.

When looking at Data Science and Data Analytics, it is more wholesome to look at the two fields of study as parts of a whole rather than viewing them as wholly separate and disjoint fields. Looking at them as two sides of the same coin allows for the critical analysis and an understanding of how to better analyze, review, and pick out viable solutions from the data that we have.

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Author bio:

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkatta) with over 25 years of professional experience, Specialised in Data Science, Artificial Intelligence, and Machine Learning.

PMP Certified

ITIL Expert certified

APMG, PEOPLE CERT and EXIN Accredited Trainer for all modules of ITIL till Expert

Trained over 3000+ professionals across the globe

Currently authoring a book on ITIL “ITIL MADE EASY”

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis, and Project management process definition and end to end implementation of Project management best practices.

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