Masters in Data Science

Masters in data science – Like a pilot, a data scientist is the person behind the control of computing technology. His area of ​​responsibility ranges from analysis to gaining knowledge or recommendations for business development. Data scientists are responsible for analyzing various types of data in large quantities (big data) that arise in the company.

This profession relies on data analysis skills such as Data visualization). In addition, a data scientist must have the ability to use statistical methods to identify useful information for business development.

If you are looking for vacancies frequently. Data Scientist job vacancies must be seen at a glance as you scrolled your screen. You are interested and click on the ad, because the offered salary is quite high and easily makes you want to change jobs to become a data scientist. Then ask yourself what skills a data scientist should have.

Learned data science

In fact, there have been data scientists for a long time, often referred to as statisticians. The difference lies in the method used. When statisticians have to process or collect data manually, data scientists today deal a lot with big data (structured and unstructured data). It is therefore not surprising that data scientists now often create algorithms in computer programs so that the incoming data can be processed directly by the computer itself.

Can all software developers then become data scientists? The answer is not necessarily. There are still many skills that data scientists must master. Let’s discuss one by one.


In any company that works as a data scientist, programming skills have become a must. A data scientist needs to have a thorough understanding of the trading tools used. This means that data scientists must at least master programming languages ​​such as R or Python (you can learn more about machine learning in Python here) as well as database query languages ​​such as SQL.

(If you want to learn about the R programming language, which can be used for machine learning, you can follow Comday’s “Introduction to the R Programming Language in Machine Learning.”)


A thorough understanding of statistics is the most basic thing to be a data scientist. In addition to determining which algorithm to use, statistical science is required to create machine learning software that will act as the legacy of a data scientist.

Machine learning

For a data scientist who works in a company where the product is data-driven, like Google Maps, Netflix or Uber, you definitely need machine learning. It is impossible for them to individually determine algorithms from very large amounts of unstructured data. Hence, data scientists need to be able to develop machine learning to process this amount of unstructured data, and it is not impossible when the machine learning developed by data scientists is applied to a concept of artificial intelligence.

(If you are interested in learning machine learning, you can join Inixindo Jogja’s machine learning class in 5 days)

Analysis & Algebra

Mastery of analysis with functions that can be applied to many variables and linear algebra is no less important to a data scientist. Calculus and algebra are the most basic and simplest concepts in data science. It is therefore not surprising that job seekers typically ask questions about calculus and algebra during job interviews.

Data mapping

Sometimes the data we get is not as perfect as we think it is. In the scriptures, the words “Yogyakarta” and “Jogja” are different but conceptually have the same meaning. And remember, computers are not as intelligent as humans. As data scientists, we have to let the computer recognize the data. For this reason, data sorting is necessary to keep the data “clean”.

Communication and data visualization

For data-driven management, especially in relatively young companies, data scientists in their companies are often asked for help in determining the direction of corporate policy. It is therefore not surprising that data scientists need to be good at visualizing and presenting their own processed data.

Software development

Tech start-ups usually involve their data scientists directly in software development. But wherever we work, it never hurts to learn a little about software engineering.


Rather, this skill is needed by a data analyst who needs to solve problems from processed data. Intuition often leads to gambling, which contradicts the word “science” in data scientists, but the words of the BBC series version of Sherlock Holmes say that “Intuition is born out of the rapid processing of millions of data in the brain, making our own Brain is unable to recognize it.

How does the dream of becoming a data scientist come true, right? If you want to learn more about the components of knowledge a data scientist needs to have, you can explore the basics of Hadoop or R programming for big data.

Data science knowledge and skills

  • Research ability
  • Ability to conduct analysis
  • Systematic thinking skills
  • Leadership skills
  • Programming ability
  • Statistical knowledge
  • Business knowledge
  • Foreign language skills

Roles and responsibilities

  • Do research on data about the company that will be used as a method of decision-making.
  • Create a pivot table using statistical software.
  • Visualize data in the form of graphics.
  • Using programming languages ​​to perform data analysis.
  • Building a data infrastructure.
  • Do research on data about the company that will be used as a method of decision-making.
  • Perform analysis of corporate data in bulk or big data.

Data Science Career Paths

Many people think that data scientists are only needed in technology companies. It turns out that this occupation is also needed in various companies, from banking, education, healthcare, transportation and logistics, sports, media, and many more. The following is an overview of the career of a data scientist:

  • Data scientist
  • Junior Data Scientist
  • Senior data scientist

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