Data Scientist vs. Data Analyst

Featured article by Steve Barnett, Experienced Writer and Content Manager

We have witnessed immense growth in data science over the recent years, and it is applicable in various fields of practice. Some of the popular areas where data science is of great significance include online marketing, Search Engine Optimization, banking, finance, e-commerce, cybersecurity, and healthcare.

Data science involves the use of scientific methods, mathematics, and statistics, among others to examine and handle data in a skillful manner. A data scientist consultant is responsible for this job. The consultant impacts change by intensifying their client’s analytics abilities, proficiency, and comprehending the plot of their business.

On the other hand, data analysis involves examining data to find bits of important things that can help an organization achieve its goals. They use the current tendencies in the market to help a company attain its goals. It is the work of a data analyst to gather, process, and examine the data needed.

For most organizations, there are many duties involving data, like becoming a data scientist or data analyst. Both roles may leave many confused because they do not know what each of them entails. Here are some differences between a data scientist and a data analyst.

Background

Data scientists are required to have a strong business understanding or reasoning abilities and creativity skills to take the right decisions on a business idea. Meanwhile, a data analyst does not need any form of business proficiency. Only the necessary visualization abilities would make the grade for their case.

Knowledge

Data scientists should be very experienced in learning machines and come up with demographic designs. Such designs are used widely in dimensional designs, approval systems, conjecturing modeling, managed categorization, and clustering. A data analyst is not required to be experienced in these procedures.

A data scientist should have a proper background in mathematics, correlation, data mining, and statistics. Data analysts need to be excellent in data building tools and elements. Another job of a data scientist is the use of classification and central-like diagnostic actions on data assortments. As for a data analyst, they only need to be excellent in data storage and retrieval tools.

Equally important, data scientists are required to be knowledgeable about database systems, especially those on NoSQL systems. On the other hand, a data analyst needs to have business acumen and understand the procedures of reporting data analysis.

Obligations

A data scientist is required to be excellent in prognosticative analytics. Acquiring very authentic predictions from previous data files is among their key obligations. Data analysts will then obtain important information from colossal data sources.

Data interpretation

Data scientists are required to make discover and use unknown features of a particular business, whereas a data analysts deal with the known business features from a new context. This explains why data scientists have more workload than data analysts. This is also why data scientists get more pay than data analysts.

Resolution of business concerns

Another role of a data scientist is to resolve business concerns and additionally take up those grave concerns that contain significant business importance. A data analyst will deal with business concerns only.

About the Author

Steve Barnett is experienced writer and content manager who has contributed to various publications in Data Science and IT. He has researched and written many publications for big data and technology blogs for the last 5 years.