Posted by Punk POS ● Dec 3, 2020 11:30:00 AM
Data Science Vs Data Analytics: What You Need to Know
A billion gathered data points mean nothing on its own. However, when this data is cleaned, processed, and analyzed, it can provide business owners critical information that can be used to optimize business performance. This insight is only possible with the power of data science and data analytics. These highly in-demand fields operate together to turn large amounts of scattered data points into applicable knowledge that businesses can use to maximize sales, improve marketing strategies, entice different consumers and much more.
What is Data Science?
Data science is a broad term and multidisciplinary field that encapsulates the use of data modelling, statistics, analytics, and machine learning to mine large amounts of raw data. This data could be anything such as images or numbers. Even when the sources of data seem unrelated, data science can extract insight from the treatment and organization of this data.
The responsibility of a data scientist is to build and utilize data modelling and machine learning to gain general insight from unstructured data. What distinguishes data science is that data scientists are not trying to find a specific solution or answer. Simply put, they’re trying to find connections between the data. Through data science, piles of raw data are refined in order to help businesses discover new potential areas of study and shape the questions that a business should be asking about their operations. For example, data science helps businesses find new areas of research that they can implement to provide better products or services.
What is Data Analytics?
Data analytics is a more concentrated sub-field that is a part of data science. In data analytics organized datasets are processed and examined using statistical software like R Studio and SQL. Using these datasets, data analysts are responsible for answering a specific inquiry that a business may have. Businesses may want to understand why their sales dropped the previous year or if their new product feature is desirable among consumers. Overall, data analytics provides businesses immediate insight to solve business problems.
As such, data analytics is used for a more focused and critical analysis of data that helps business owners to understand existing trends and derive solutions to current issues.
What’s the Difference between Data Analytics and Data Science?
The difference between data analytics and data science is their scope and the purpose they serve for businesses. Data science works on a more general and macro level. More specifically, data science is responsible for organizing tons of raw and unstructured data. It heavily relies on building predictive modelling and artificial intelligence to sift through data and provide general insight businesses may never have considered.
Alternatively, data analytics is more narrowed and works on a micro level. Its purpose is to analyze and interpret already established datasets in order to find answers to specific questions or problems. Data analytics relies more on programming with Python and R, with less emphasis on statistics in comparison to data science.
While data analytics and data science differ in some respects, they are highly interwoven and work together to help businesses optimize their usage of data. Both are used to provide useful information for businesses. Data science provides the foundation needed for data analytics to provide immediate and actionable solutions.
Importance of Data Science and Data Analytics
The importance of data science and data analytics speaks for itself. With such an accumulation of data in current times, businesses can take advantage of this information in order to implement innovative solutions and strategies within their business. Business owners can now predict market trends, understand the impact of their business decisions on consumers, and the list goes on. Data is a vital tool for businesses no matter how small or large. However, the only way to extract insight from data and use it to maximize business performance is through the application of data science and data analytics. Data science and data analytics is the medium between the data and its invaluable insight.
With data science, businesses can uncover brand new areas of study and predict profitable leads in order to outperform business competitors. On the other end, data analytics can help businesses analyze current problems and gain a deeper understanding of consumers to make businesses remain relevant. Overall, data science and data analytics are essential for businesses to maximize their potential and solidify themselves among competitors.