Data Analyst vs Data Scientist: Understand the 3 Differences!

Jobs in the big data field have Data Analyst vs Data become jobs that are in high demand in Indonesia. Quoting from the Harvard Business Review , ten years ago Hal Varian, Chief Economic at Google, had said that ” The sexy job in the next 10 years will be statistician “. This is proven by the large number of job vacancies that open jobs in the big data field , such as data analysts and data scientists .

For those of you who study in the field of machine learning , of course you are already familiar with these two roles . Considering that career prospects in the field of machine learning are also very broad with the emergence of jobs such as Data Analyst , Data Engineer , Data Scientist , and also Machine Learning Engineer .

In this article, we will get to know the two jobs which are of the same kind, but different. The two roles are Data Analyst and Data Scientist jobs . To make it easier to distinguish between the two jobs, we will recognize them in three points.

Duties and responsibilities

Duties and responsibilities
Judging from the duties and responsibilities given, of course Data Analyst and Data Scientist have different roles. The Data Analyst’s main task is to provide insight from existing data. Usually the Data Analyst team uses historical data or past data. This data can be obtained from various events or a decision made.

The Data Analyst team will provide Sweden Mobile Number List insight from the data. The resulting insights are made from the questions that arise. The form of insight obtained will become information for the need to identify past problems or become a determinant for future decisions.

In contrast to the Data Scientist team whose job is to explore the data that is already available. Data Scientist will conduct exploration to find out influential data for the future.

The Data Scientist team is responsible

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For finding the right data to get an insight . Exploration by Data Scientist can be started from descriptive analytic, predictive analytic, to prescriptive analytic.

The results of this exploration will create a solution, for example a machine learning model is made for a prediction or recommendation.

Both of these teams are both struggling CRYP Email List in the field of data. However, the tools used to process the data are different, friends. The Data Analyst team will usually use tools that are easy to use via the dashboard . Examples of tools used are Power BI, Excel, Spreadsheets, Tableau, and so on. The Data Analyst team needs these tools to facilitate the query process and also the required visualization.

On the other hand, the Data Scientist team more often uses programming languages, such as Python to process existing data. The Data Scientist team spends more time in front of the code to gain insight .

It is this responsibility for exploration that requires the Data Scientist team to use and learn programming languages, such as Python to solve existing problems.

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