Data Science & Data Analytics, main differences

Welcome to a new entry of the Data for everyone newsletter. On this week's article I'll be explaining the main differences between Data Science & Data Analytics.

Since we live in a data-driven era, the terms ¨Data Science¨ / ¨Data Analytics¨ are heavily used these days, but not many people are able to differentiate the differences between both terms. Usually leading to misconceptions & misunderstandings, specially in professional environments, where data actually helps extract business value... Hence, the need for data prodessionals to be able to understand the differences between both terms.

What is Data Analytics?

Data Analytics has many definitions, but one that can simplify it's whole purpose is the following: The process of examining raw data in order to identify trends that lead to put that raw data into context and therefore creating valuable conclusions of that information.

To be clear, most data analytics techniques are automated into algorithms or routines that can be performed through data tools or softwares like Python or Excel in order to create that valuable information. Data Analytics is widely used in many industries like financial services, retail, insurance, airlines... All of these cases are driven by optimization and risk analysis algorithms that rely heavily on data storage and computation which are manually intensive and require of human touchpoints for its performance, meaning that in order to analyze data you will need human capital in order to draw conclusions, since data can be analyzed in many ways from a computational standpoint, but without a human performing the correct analysis and drawing a conclusion the analysis would not be fully valuable for a business.

Data Analytics is generally geared towards business performance and business intelligence, it focuses more on the drawing of results in a business oriented way, deploying more data analysis concepts & techniques and less algorithmic-based, mathematical concepts. On this field we find data and business analysts, skilled data professionals that have business backgrounds or are specialized on an specific line of business.

What is Data Science?

On the other hand, Data Science is a multi-disciplinary science that combines statistics, mathematics and computer science in order to analyze massive amounts of data in order to optimize decision-making in business. Data Science uses advanced machine learning techniques to extract insights and predict future patterns and behaviors. This ¨approach¨ is more sophisticated as it requires complex modeling of variables and often uses advanced programming techniques with softwares like Python or R.

At the same time, Data Science uses statistical methods and algorithms such as logistic regression that can be used to determine things like default risks for a service provider. Another example is how big companies like airlines use other data science methods to provide optimal pricing for its customers.

Summarising, data science and data analytics have been used for decades to drive business outcomes across industries, both fields are complementary and though they're different they complement each other in order to optimize the decision-making process in organizations, as technology and programming techniques have developed so have these two fields, creating new data-related positions.

Main differences?

As mentioned before, many people use the term interchangeably, but these are unique fields whose major difference is... It's scope.

Data Science is an umbrella term, hence it is a group of fields used to mine large datasets. Data Analytics are generally compromised of softwares that serve as focused versions of Data Science and can be considered part of the larger process.

With this said, Data Science's scope can be considered macro as is a broader term englobing multiple fields like mathematics, statistics, data structures, algorithms... Meanwhile Data Analytics is a much more focused term devoted to the process of data extraction, manipulation and analysis. Therefore, having a micro scope.

On a second note, a major difference between these two fields is data exploration. Data Science works with a macro scope, which means that is main objective is not to answer specific queries but to explore massive datasets in order to expose insights and turn them into actionable insights. Meanwhile, Data Analytics works in a more focused way trying to answer specific questions that need answers tied or based on existing data.

Data Science PRODUCES INSIGHTS based on which questions should be asked, Data Analytics aims to DISCOVER ANSWERS to questions being asked.

Conclusion

As the functions of both fields are highly cohesive and interconnected, I've always considered them two flips of the same coin. Data Science is the science that lays the important foundations of insights discovery by analyzing datasets to find observations, future trends and potential insights, information that already has value and is useful for many fields like data modeling, AI or machine learning. Data Science is the tool used to ask the important questions we were unaware of... When adding Data Analytics, we turn that valueable knowledge into actionable insights and answers that develop into practical applications.

I never liked seeing both fields as ¨Data Science¨ VS ¨Data Analytics¨, I always liked to think that we are lucky to have two parts of a puzzle that help us is analyzing and understanding the information that surrounds us.

I hope that you enjoyed this week's entry! If you liked the article make sure to connect with me on LinkedIn to stay updated with my newsletter, see you next week!

-Alfredo S.