13 Important Skills for Data Scientist

Azzahrah Olinda Nurva, Mahasiswa Entrepreneurship UNP

What I got after reading the article "14 Important Skills for Data Scientists" from volume 18 of Rintisan magazine.

In this era full of technology, of course, data is the most important asset in an organization and various other operational aspects. Therefore, it requires the ability to analyze or process data so that it becomes something valuable. Data Scientist skills are the most needed thing today. Data scientist is an ability where someone can uncover large amounts of information and insights from structured data. This ability can help us get answers to various questions and make the right predictions.

To become a data scientist is certainly not easy, in this article let's discuss 13 skills that you need to hone to become an expert data scientist.

1.            Statistics

Statistics is an ability that must be mastered by a data scientist because this ability can facilitate his work in collecting, interpreting, and presenting data.

2.            Multivariable Calculus and Linear Algebra

For a data scientist, the ability in mathematical concepts can optimize their work so that they can match the model to the data set.

3.            Predictive Modeling

Predictive modeling is the ability to use data to predict future events and outcomes, with applications in customer analysis, equipment maintenance, and medical diagnosis.

4.            Machine Learning and Deep Learning

Many companies are looking for data scientists to apply machine learning and deep learning to analyze data and discover insights. Skills in these two areas are increasingly important and make data scientists stand out.

5.            Data Wrangling and Data Preparation

These are skills that include overcoming data quality issues and imperfections in data collection. Hence data scientists spend more than 80% of their time preparing data for analysis including profiling, cleaning, and data modeling.

6.            Model Development and Production

Data scientists must be able to choose the right algorithm so that they can use the training data for supervised learning or automatically run algorithms or patterns in unsupervised learning.

7.            Data Visualization

Data scientists need to have this skill in order to highlight and explain the insights they produce, and data visualization is the basic foundation for them. Because a data scientist should understand the use of Tableau, D3.js or various other data visualization tools.

8.            Critical Thinking

The ability to look at data with a skeptical eye can help data scientists reach accurate and unbiased conclusions.

9.            Business Knowledge

This ability can help data scientists to be able to generate the right data analysis questions so that they can help companies find new ways to use their data.

10.         Problem Solving

During the data processing process, data scientists need to think sharply in solving problems and figure out how they can get into the calculation and determine what data should be included or left out.

11.         Curiosity.

Curiosity is an important skill for data scientists. With great curiosity, you can sift through large amounts of data to find answers and insights.

12.         Collaboration

Working with a team is also an ability that data scientists must master so that it can make it easier for them to collaborate with other divisions.

13.         Communication

Since data scientists work with data every day, of course they must have communication skills so that they can share their understanding of data and analytic results so that teams and leaders can use the information and make wise decisions.

It turns out that there are also many abilities that must be possessed by a data scientist, it turns out that they must also be good at communication, collaboration, critical thinking and many others. Of course, these skills cannot be learned in school alone. You have to learn from various sources to be able to understand data science because technology is always developing every day. Hopefully this article can help you become the next data scientist.