Data science and data analysis are two related fields and the line of demarcation between them is very thin. This suggests that there is sufficient overlap between these two fields. Instead of exploring the domains of overlap, the aim of this article is to explore the differences between the two fields.
The domain of Data science
Data science is not a unified field but it is the intersection of numerous fields that are studied under a common umbrella. The fields which come under the common umbrella of data science include business intelligence, machine learning, deep learning, computer vision, artificial intelligence, and decision sciences. There are three core layers of data science. That said, there are a number of professionals who work in the domain of data science but are not necessarily data scientists. Data scientists deal with the inner core of this field which includes the design of algorithms, the development of codes, and the building of models to derive actionable insights from raw and unstructured data. In this way, a data scientist not only explores data but also finds the various underlying patterns in this data by harnessing the information that is not visible to the naked eye. Prediction of future patterns to achieve the necessary goals is a precursor to various types of analytical sciences. Professionals with data science certification in Malaysia are trained in predicting such patterns.
The science of Data analytics
The domain of Data Analytics also covers under its umbrella various types of sub-sectors which include customer analysis, pricing analysis, market analysis, revenue analysis, and business analysis. The primary function of a data analyst is to examine data and explore the various business questions related to it. These business questions revolve around the type of products, revenue methods, and outcomes that together make up the business strategy of an organization.
Firstly, the data analyst should be able to extract structured information from unstructured data sets. Secondly, he should be able to work with the tools to not only organize data but also to classify and cluster it. In simple terms, a data analyst keeps track of the current state of data and determines the course of action as and when the database gets updated.
Skillset: The real determinant
The skillset of a data scientist revolves around mathematical and statistical knowledge, data visualization, data mining, cleansing, insights, software development, machine learning algorithms, and the development of business models. On the other hand, the skills of a data analyst include knowledge about data warehouse, data modeling techniques, statistical analysis, and knowledge in the programming language of R, SAS, and SQL.
Concluding remarks
By now, the line of demarcation between a data scientist and a data analyst has been well sketched. Talking about a suitable fit, the answer lies in research and analytical capabilities. If the research interests dominate your interests, data science should be your preferred domain. You can choose to become a data analyst if analytical capabilities fascinate you a lot.
