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Data Analytics vs Data Science

Data Analytics vs Data Science

In such a faced-paced world, it’s not surprising we sometimes confuse certain technical terms, especially when they evolve at such dizzying speeds and new scientific fields seem to emerge overnight. That’s why in the world of big data, which involves working with enormous and complicated amounts of information, some people still confuse certain concepts, tasks and roles found within this emerging and growing discipline.

One of the main points of confusion in this field is the difference between data analytics and data science, two very closely related, but distinctly different areas.

Although both are found at the crossroads between maths, stats and development, the purposes they serve have clearly differentiated tangents, meaning the profiles of professionals working in the two fields are also very different. It’s essential that anyone looking to specialise in big data knows what kind of knowledge and skills they will need to acquire should they decide to focus on either data analytics or data science.


What Is Data Analytics?

Data analytics focuses on processing and performing statistical analysis of existing datasets. Analysts concentrate on creating methods to capture, process, and organize data to uncover actionable insights for current problems, and establishing the best way to present this data. More simply, the field of data and analytics is directed toward solving problems for questions we know we don’t know the answers to. More importantly, it’s based on producing results that can lead to immediate improvements.

Data analytics also encompasses a few different branches of broader statistics and analysis which help combine diverse sources of data and locate connections while simplifying the results


What Is Data Science?

Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The field primarily fixates on unearthing answers to the things we don’t know we don’t know. Data science experts use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet.

Data scientists’ main goal is to ask questions and locate potential avenues of study, with less concern for specific answers and more emphasis placed on finding the right question to ask. Experts accomplish this by predicting potential trends, exploring disparate and disconnected data sources, and finding better solutions.

Common tasks for a data analyst might include:

Collaborating with organizational leaders to identify informational needs

Acquiring data from primary and secondary sources

Cleaning and reorganizing data for analysis

Analyzing data sets to spot trends and patterns that can be translated into actionable insights

Presenting findings in an easy-to-understand way to inform data-driven decisions

Data scientist role is generally considered a more advanced version of a data analyst. Some day-to-day tasks might include:

Gathering, cleaning, and processing raw data

Designing predictive models and machine learning algorithms to mine big data sets

Developing tools and processes to monitor and analyze data accuracy

Building data visualization tools, dashboards, and reports

Writing programs to automate data collection and processing.


Data Analyst vs Data Scientist – Career Growth

If you want to start your career in analytics, it is best to get into an entry-level data analyst role. This will help you get acquainted with using real-world business data to derive insights. You will use your existing skills to query databases, generate reports with BI tools and analyze critical data. Eventually, you can upgrade your skills, use advanced data analytics techniques, and apply mathematics to become a senior data analyst or data consultant.

Data Science is being used in nearly every industry, such as Healthcare, E-Commerce, Manufacturing, Logistics, and so on. There is a dearth of data scientists globally, with companies looking for professionals who can make critical decisions and drive business growth using data. Companies see a skill gap in this role and find it challenging to get qualified data scientists to develop the algorithms and build predictive models. You can indeed become a good data scientist with the right skills, domain knowledge, and business understanding. There is a vast scope to level up further and become a research scientist. 


What Is the Difference?

While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.

Another significant difference between the two fields is a question of exploration. Data science isn’t concerned with answering specific queries, instead parsing through massive datasets in sometimes unstructured ways to expose insights. Data analysis works better when it is focused, having questions in mind that need answers based on existing data. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.

More importantly, data science is more concerned about asking questions than finding specific answers. The field is focused on establishing potential trends based on existing data, as well as realizing better ways to analyze and model data.


Data Science vs Data Analytics — The Skills

Data Analytics — Knowledge of Intermediate Statistics and excellent problem-solving skills along with

Dexterity in Excel and SQL database to slice and dice data.

Experience working with BI tools like Power BI for reporting

Knowledge of Stats tools like Python, R or SAS

To become a data analyst, one need not necessarily hail from an engineering background but having strong skills in statistics, databases, modeling, and predictive analytics comes as an added advantage.

Data Science — Math, Advanced Statistics, Predictive Modelling, Machine Learning, Programming along with 

Proficiency in using big data tools like Hadoop and Spark

Expertise in SQL and NoSQL databases like Cassandra and MongoDB

Experience with data visualization tools like QlikView, D3.js, and Tableau.

Dexterity in programming languages like Python, R, and Scala.

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