Data Scientist or Data Analyst is a profession that can be taken after you study Data Science. Data Science itself is a science that is predicted to be the most important science in the future. That’s all due to the increasing development of the times the more data is obtained. Data Science is a science that is learned to better understand data processing, data analysis, and much more.
Even though they both have jobs related to data, these two types of jobs have different job descriptions. These two professions are equally related to the development of technology and data processing, no less interesting, these two professions are also included in jobs that will rise to fame in the years to come, no kidding, these two professions can also earn tens of millions to hundreds of salaries. million.
Curious which one matches your passion? Let’s look at the differences and similarities between the two professions.
1. Data Scientist
Data Scientist aims to process data using various methods and algorithms, this data is used to find solutions to the problems to be solved. The processed data will be stored in the company database. The data collected needs to be analyzed covering a large amount of data so that it requires special skills.
For real examples of the use of large amounts of data, it can be seen from personalized ads, the use of this type of ads requires a lot of data in order to display ads that are suitable for the target market. For example, when you install an e-commerce application you will be given the option to allow your search history to be read by the application to match what you want with the ads that will appear on your cellphone. Processing a lot of data requires a Data Scientist.
A Data Scientist, to be able to process large amounts of data, requires expertise in statistics, programming, and business knowledge. A person who studies statistics in college can become a Data Scientist when he enters the workforce because the core of a Data Scientist is statistics.
The ability of a Data Scientist is not only used for coding, but is also able to transform unstructured data sets into something that is easier for ordinary people to understand. As a Data Scientist, he is also tasked with understanding business because companies need services to produce product recommendations to be offered to consumers.
The Data Scientist profession itself is currently the prima donna for companies that carry the concept of industry 4.0. Data Scientist capabilities are used to uncover a trend from data that has millions of rows.
2. Data Analyst
Unlike a Data Scientist, a Data Analyst is not required to understand the programming world, but if you understand the programming world it will be a strong plus to get this job. The Data Analyst is someone who is in charge of translating numbers into reports that are easy to understand so that they can be processed and made into reports to assist the management and processing of company data. A data analyst can also be said to be a person in charge of analyzing data to increase profits and the credibility of a company.
The tasks of the Data Analyst themselves include analyzing data, reading business directions, interpreting data, and much more. A Data Analyst must also have the skills to be a good Data Analyst. You must be able to do mathematics and statistics, be able to analyze, present and interpret data.
3. Similarities between Data Scientists and Data Analysts
In addition to the differences, there are also some similarities that a Data Scientist or Data Analyst must have, including:
- Both must have strong statistical skills, be familiar with data processing software such as My SQL, Cassandra, or Hadoop.
- Both are expected to be able to understand programming languages, especially for Data Scientists and can use the data to dig deeper information for the company’s business development.
- The skills needed from these two professions are also similar, you must be able to analyze and also present data to people who are still new to data
- The next equation is more of a mentality that does not give up easily and also requires high accuracy to be able to process data that amounts to millions.
Also read: Data Analyst vs Data Scientist, Let’s Recognize the Difference (Baca juga : Data Analyst vs Data Scientist, Yuk Kenali Perbedaannya )
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By Yohanes Ricky & Annissa Widya