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The high need for Data Science practitioners in companies, although it can be said that Data Science is a new field that is developing due to the rapid development of technology. That’s why there are still many people who don’t really understand what Data Science is and what a data expert does. Data science is the process of data processing activities using statistical, mathematical, and business methods that aim to see important insights behind the data owned by a company. However, because data science is a fairly new field, there are many myths related to data science that discourage people from pursuing a career in this field.

In this article, we will discuss data science myths that we often hear. Of course, by knowing the truth, you no longer need to hesitate to study data science or start a career in the data field.

1. Must Take Formal Education

Data Science is a multidisciplinary science where this science is a combination of other sciences such as mathematics, statistics, programming languages, and many more. Data Science itself can also be studied non-formally or following formal education. Formal education is education in schools that is obtained regularly, systematically, in stages, and follows the requirements of the government. While non-formal education is an educational path whose purpose is to replace, add and complement formal education.

Each education certainly has its advantages and disadvantages

Advantages of Formal Education

Learning from the Basics, In formal education you will start learning from the basics to the last, of course this will strengthen the basic principles you learn.
Learning with Teams, Formal education is not far from group work so you can exchange ideas and also increase relationships. By often studying as a team, you will not be surprised when you work because work really requires good teamwork.
Looking for Identity, Formal education has a lot of learning. With that, you also learn what things you are good at and also enjoy, making it easier for you to make decisions when majoring in college.

Disadvantages of Formal Education

Little Experience, Although you are often given assignments at school, but when you work there are often many new lessons you encounter while working.
Relatively Long Time, Formal education often takes longer than non-formal learning. Because in formal education you will learn from the basics.
Tends to be More Expensive, there are still many people who can get into college due to the high cost.

Advantages of Non-Formal Education

Study Time, in formal education the study time has been determined, in contrast to non-formal you can determine the time that suits your time.
Independent, in non-formal education you are also required to be more active in solving problems by using all that is available, for example looking for answers from books, google, friends, and others.
Practice, in non-formal education, direct learning in the field is a very effective method. Many people only understand one concept because they learn by learning by doing.

Disadvantages of Non-Formal Education

No Degree, Degree is also the reason why people choose formal education. Unfortunately when you study non-formal education a degree is difficult to obtain, but you can take a lot of training that guarantees a certificate.
Changes in motivation, of course, when we learn new things, we need great enthusiasm, especially when no one is watching, so having a strong motivation from the start is important in learning.

Also read: Recognize the Differences between Data Scientist, Data Analyst and Data Engineer

2. Data Scientist Only Needs to Master Data Tools

When in the real world of work, the role of a Data Scientist is very crucial and relates to many other fields. Many also think that mastering tools is the only key to becoming the best Data Scientist. The reality? That’s also one of the myths related to Data Science!

A Data Scientist, apart from having knowledge of the tools to be used in a data science project, is also required to master several other skills, even those that are not related to data science. This is useful so that you can actually apply the skills you have in your work in the future.

Good communication skills are very necessary for a Data Scientist so that the insights that have been generated can be conveyed and understood by the user. In addition, the Data Scientist must also master at least basic business knowledge, so that the insights made are right according to the targets to be achieved. Because

basically the company recruits a Data Scientist to be able to answer business questions that they have not been able to solve manually. So don’t just get carried away in data science tools without having other knowledge beyond that.

If you are shy of asking questions, you will get lost in your way. That one old adage is still relevant today. Well, for those of you who want to start a career in the data field, don’t be instigated by untrue news and ignore the bright future in the data field.

Don’t hesitate to ask the experts in the field if you have the opportunity. Now, while waiting for that opportunity to come, you can also read the articles on our blog so you can understand data science more deeply.

3. Data Science is Only for Large Organizations

Many companies believe that data science is only for large organizations with high infrastructure.

This is very wrong about data science. Data science is not made of machines, heavy equipment or the size of workers. Data science may be made up of big data, statistics, analysis, programming, presentations and some smart people who know how to make good use of data science and add value to an organization. This has nothing to do with the size of an organization.

A data scientist must be able to use the results optimally for his company. And no one cares about what tools and techniques they use to achieve the results. What is needed is a computing device, internet, and several tools that help data science to keep running. There are several tools that can be downloaded online to maintain satisfactory results.

Also read: Self-taught Data Science Learning? Here are the steps

Start Your Career with DQLab

DQLab is a Data Science learning center that offers online courses for those of you who want to start learning Data Science. DQLab itself has produced data practitioners who are proficient in their fields. With DQLab you will learn in a structured manner with case studies and data that are in accordance with those in the field. DQLab also provides a forum for sharing with 95,000++ members of DQLab, as well as with expert data practitioners.

So what are you waiting for? Immediately register yourself to DQLab and achieve your dreams together.


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