Difference between Data Scientist and Data Analyst, ‘Trendy’ Jobs

perperbedaan data scientist dan data analyst, Difference between Data Scientist and Data Analyst, ‘Trendy’ Jobs

Difference between Data Scientist and Data Analyst

In the age of information and technology, data is increasingly shaping the systems we deal with on a daily basis.
Starting from online search engine pages, product recommendations on e-commerce sites, to
Company reports are the result of data processing. The term ‘Big Data’ is also widely used
sound. It is not surprising that data-related professionals are increasingly
needed and the type is also growing.

The Society for Human Resource Management (SHRM) predicts that 59% of organizations will
adding positions requiring data analysis skills by 2021. Thomas H.Davenportdan D.J. Patil wrote in the Harvard Business Review that data scientists (scientists data) is the sexiest job of the 21st century. LinkedIn research also found
that data scientist ranks 2nd as the fastest growing profession
over the past five years in the United States. There are 6.5 times as many data scientists
today compared to five years ago.

What exactly is a data scientist? Is it just a more trendy name for data analyst (analyst
data)? In terms of similarities, data scientists and data analysts are two professions that play a role
in interpreting data so that the knowledge gained can be digested by all
part of an organization and become material for making decisions. Both must
possess business acumen, master mathematics/statistics and computer science/programming.
For those who want to stay up-to-date with industry developments or those currently charting
next step in career journey, here are the differences between data scientist
and data analysts.

Data Scientist

Data scientists don’t just analyze data, they are responsible for identification
trends and making predictions, such as discovering consumer preferences, and providing code
in order to produce a better product. Therefore, data scientists also often do
test. Data processed by data scientists in large quantities (big data)
and scientists are usually more common in large companies.
Even though they have expertise similar to data analysts, the knowledge of programming a data
stronger scientists. Data scientists require more training than data analysts,
because it is mandatory to become an expert in critical thinking and apply more data modeling techniques
complicated. Programming languages that must be mastered include R, Python, SAS, Hive, MatLab,
SQL, Pig, Spark and Hadoop. In addition, data scientists have communication skills including
narrate and visualize the insights gained to colleagues.

Data Analyst

Just like data scientists, data analysts are tasked with interpreting data into insights
can be applied. The process carried out by a data analyst is changing data
raw to another format (data munging), collect, process and apply
algorithm on structured data. Common programming languages are mastered, among others,
R, Python, SQL, NOSQL, HTML, Java Script, C/C++. However, if the company already has
large volumes of data, a data scientist is needed. The final form of work
data analyst is report and presentation.

Data Scientist or Data Analyst: How to Choose One
Before choosing, it is necessary to pay attention to personal inclinations. If you like to explore,
starting something from scratch and comfortable with ‘uncertainty’, then data science can become
fun challenge. Meanwhile, if you are more comfortable with the job
more structured, data analyst is more suitable.

Parrish & Co.

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