Data Science Versus Data Analytics: Two Sides Of The Same Coin

With data being “the new oil”, the two buzzwords – “Data Science” and “Data Analytics” can often be heard in a lot of conversations within the Computer Science world. While they are often used interchangeably, they are not the same thing. In this blog, we will breakdown the jargon and see what the two terms mean, where the two overlap, and how they are different.

Why is Data important?

Be it healthcare, research, retail, or any other industry, data is now an indispensable part of deriving useful information for company growth. With the explosion of data-backed business decisions, even small start-ups are investing time and effort into utilizing big data to reveal hidden business problems and business needs, customer preferences, market trends, and other meaningful insights. 

Because nothing is as persuasive as hard facts backed by data, organizations around the world require a proper understanding of analytical tools that can parse through large datasets to uncover the right information for top-tier product development and customer experience.

What is Data Science?

Simply put, Data Science is the umbrella term of techniques to extract insights and solutions from complex structured and unstructured data through data cleansing, data mining, data modeling, machine learning algorithms for making predictions and pattern discovery, sentiment analysis, and predictive analysis of datasets and transform them into actionable business strategies to problems that haven’t been thought of yet.

Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015. Be it your GPS route to work or tracking your fitness goals through a wrist band, Data Science experts are responsible for breaking down raw data into usable information and creating software and algorithms that help companies improve the relevance of their product in people’s daily lives.

What is Data Analytics?

Data Analytics is a subset of data science. This type of analytics entails the utilization of data to draw meaningful insights from structures data sources and stories that numbers tell so that business can optimize their processes. Exploratory data analysis also entails creating visual representations, such as charts and graphs to better showcase what the data reveals. Essentially data analytics determine trends and metrics that would otherwise be lost in a large pile of information.

The focus of Data Analytics lies in inference and statistical analysis of known data, where a researcher knows what he is looking for and is great for uncovering questions regarding business scenarios and questions already in mind, that requires immediacy. For example, if stakeholders need to know what the quarterly sales looked like or what call center productivity looked like, a data analytics tool will serve your needs better.

What’s the difference between the two?

While data analytics and data science both deal with data, the main difference lies in what they do with it. Here are some key differences –

While, the end goal of data science is to discover new questions that might drive innovation through prototypes, algorithms, predictive models; data analytics extract actionable insights by identifying trends, develop charts, and create visual presentations through HappyFox Business Intelligence, SQL, etc. 

While Data Analysts will typically work with existing databases and data structures and will be proficient in utilizing powerful tools; Data Scientists on the other hand will often work with sparse data in poor form. 

Career in Data Science and Data Analytics

With businesses striving to capitalize on their data, qualified data professionals are highly coveted in the industry. While both data scientists and data analysts need some level of programming skills, if your skillset and interest lie in making sense of the data such as why the sales dropped or what KPIs could strengthen businesses, a data analytics career path might be the right fit for you.

For a career in Data Science, one of the top jobs in the United States, employers often look for an advanced master’s degree and a few years of experience in the data science industry building statistical models, predictive modeling, machine learning.

Conclusion

With the growing interest in big data and artificial intelligence, the fields of data science and analytics are no longer just limited to academia but are becoming integral elements of Business Intelligence and analytics tools. It is important to note that while you’d be tempted to compare the two, to unleash the true power of data – data science and analytics have to complement each other and not against each other for optimal problem-solving and decision making.