Data Analytics vs Data Science: Know the Difference
Data Analytics vs Data Science: Know the Difference
Datahub Analytics Team
Data is the biggest asset for businesses nowadays. Many organizations are using data to their advantage to grow their business and move it toward profitability. Companies that are not using data are sure to be left behind as the current and emerging trends have heavily conveyed the importance data holds. Data science and data analytics are both emerging fields that help businesses make sense of their data. These fields are assets as well as tools for businesses to facilitate growth and improve returns on investment.
Both data analytics and data science consist of some of the top-paying jobs. However, it is common for a lot of people to confuse the two terms. When one is dealing with data, it is normal to be confused about what is the actual use of it. Data scientists and data analysts deal with the different uses of data. While one uses data to ask questions, the other uses the data to answer these questions and create actionable insights out of them. It is also important for business owners to know the difference between data analytics and data science so, they can decide which one to invest more in based on their business goals.
What is Data Analytics?
Data analytics is a field of work that focuses on processing and performing statistical analysis. It is a technique through which individuals make sensible conclusions from existing datasets. Data analytics deals with capturing, processing, and organizing data to find answers that are asked by data scientists. Data analysts make use of various techniques that are all aimed at creating conclusions that can help businesses create strategies that are most likely to provide them with profitable outcomes. Data science is all about finding solutions that are not known yet.
The work of data analysts includes reviewing data to identify insights that are key for businesses to gain a better understanding of their data and customers.
What is Data Science?
Data science is an extremely valuable field in the current business scenario. It is a field that is multidisciplinary. It focuses on analyzing the raw and structured data and creating actionable insights out of them. Data science deals with knowing the various trends and patterns in addition to doing predictive analysis. The field is mainly concerned with the creation of data sets that allow them to ask important questions. The field is not very much concerned with the answers but creating concrete questions. The right questions are essential to know the exact problems that need solutions.
The work of a data scientist includes applying various skills that will manage big data and extract important insights.
Data Analytics vs Data Science – Highlights
It is extremely common to confuse the terms data analytics and data science. Both deal with data but in different ways to achieve different conclusions. It is important to understand these differences to make the most use of these technologies. In the table below, we have provided the difference between data analytics and data science.
| Differentiating Factors | Data Analytics | Data Science |
| Language of Code | Python and R languages are crucial to know for Data Analytics. | Python is the most important coding language for data science. Other languages used for data science are C++, Perl, Java, etc. |
| Programming Skills Needed | Only basic Programming skills are required for Data Analytics.
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One needs in-depth knowledge of programming to work efficiently in the field of data science. |
| Goal | To ask the correct questions that will provide answers relevant to the business. | To gain actionable insights that will help with the creation of strategies that will help with growth and profitability. |
| Machine Learning Uses | Machine learning is not needed in Data Analytics. | Machine learning algorithms play an important role in gaining insights into the Data Science field. |
| Data Type | Data Analytics uses structured data. | Data Science uses unstructured data. |
| Statistical Skills | There is no or minimal use of statistical skills in data analytics. | Statistical skills are very important in the field of Data Science. |
| Other Need Skills | Conclusions are drawn from raw data using Hadoop-based analysis. | Data Science leverages data mining activities to gain meaningful insights. |
To Sum Up
Data analytics and data science are extremely useful for businesses in almost all industries. Businesses can leverage data to create strategies and plans that help in their growth and profits. It is important to know the difference so businesses can invest in their teams efficiently. The goals of the organizations can help them determine the kind of investment and effort they want to put into these technologies.
While data analytics deals with asking the right questions, data science deals with answering these questions in a way that helps in creating profitable strategies. Both functions are equally important, and businesses should ensure they are using them most efficiently.
