Top 6 Data Scientist Skills Required in 2023

Data scientist is a profession that is in great demand, especially in the digital era like now. What’s more, this profession is known for its high pay, so many people are interested in working in this field. Therefore, you need to master some data scientist skills to support your career.

According to Thomas Davenport and DJ Patil in the Harvard Business Review, data scientist is one of the hottest careers in technology in the 21st century.

Despite its popularity, there are still many people who don’t understand exactly what a data scientist is? How do they work? What skills are required to occupy the data scientist role?

Get to Know Data Scientist

A data scientist is a professional whose job is to handle big data and collect and analyze large data sets, both structured and unstructured data.

Later as a data scientist, you will analyze, process, and create data models, then interpret the results so that companies/organizations can act on them.

Data scientist jobs combine computer science, statistics, and mathematics.

As a data scientist, you are an analytical expert with the technology and social science skills to spot trends while managing data. You also use contextual understanding and industry knowledge to solve business challenges.

Data scientist tasks are usually related to understanding messy and unstructured data from various sources, such as smartphones, social media feedback, and e-mails that do not have a database.

Required Technical Skills

Some important technical skills for a data scientist are:

1. Statistic Skill

In carrying out their work, data scientists have the main task of processing data into meaningful information. This information will be beneficial for companies to make business decisions. Therefore, it is essential to master statistics and the science of probability. This skill is critical to processing data and making measurable estimates to determine company decisions.

2. Programming Skill

The next skill you must master to become an expert in data science is understanding programming languages. Programming skills can help you organize and organize unstructured data. The following are several types of programming languages that are commonly used:


This programming language is not only beneficial for web developers but very useful for data scientists. In this case, you can use it for data mining, displaying data as a graph, importing data from Microsoft Excel, and so on.


Another programming language you must master is R. This type of programming is usually used for data manipulation and graphing. Several large companies use R for their data scientists. Some big companies are Google, Firefox, and other technology companies.

3. Processing Big Data and Queries

Data scientists need a lot of understanding of SQL (Structured Query Language) to process structured data in the database so that it becomes easier to do analysis. Knowledge of SQL, including Data Manipulation Language, Query Language, Data Definition Language, and Data Control Language.

This profession must also be able to have the ability to process big data to facilitate the process of data analysis. Big data is data with such a large volume. Big data needs to be processed using specific tools.

4. Data Visualization

The fourth skill that you must master is visualizing data. Good data scientists have to realize that data contains a specific message, and they have to translate that message.

The data that has been analyzed must be presented in a form that is easy to understand when someone reads the report. Therefore, visualizing is essential because it can affect corporate decision-making. Some tools that can help in imagining data are Tableau and Power BI.

Required Non-Technical Skills

Non-technical data scientist skills refer to soft skills that will greatly support technical capabilities, including:

1. Strong Willing to Learn

Data science is a field that is constantly evolving and rapidly changing. Therefore, you must always be curious and want to continue learning.

Apart from that, you also have to have an open mind. Because with an open mind, you will receive all input and improve yourself.

2. Communication

The qualification to become a data scientist is, of course, having to understand how to extract and analyze data. However, for the data scientist’s task to run optimally and for the company gets the desired results from data analysis, communication skills are needed to convey these findings.

3. Business acumen

Not only dealing with data a data scientist also needs to have strong business acumen skills. With it, you can distinguish potential problems and challenges that need to be solved for organizational growth.

This skill is essential for your company/organization to explore new business opportunities.

4. Data Intuition

This skill can actually be obtained with the right experience and training. Data intuition is something other than something that can be measured.

Big data sets are not always valuable or of particular value to a company. A knowledgeable data scientist knows when to look at the wider lens to get the necessary information.

Understand Unsupervised and Supervised Learning

As we have discussed, machine learning skills also need to be mastered by a data scientist. There are two approaches to choose from in modeling: unsupervised learning and supervised learning.

A data scientist can apply both as needed. So, how do you determine the right approach? Check these essential things!

Unsupervised Learning

Unsupervised learning is a machine learning algorithm for analyzing and classifying unlabeled data sets. This algorithm finds hidden patterns in data without human intervention.

This model is mainly used to make 3 things: clustering, association, and dimensionality reduction.

Supervised Learning

Supervised learning is a machine learning approach defined by using labeled data sets. These data sets are designed to train or “monitor” algorithms to accurately classify data or predict results.

The model can measure its accuracy and learn over time by using specifically labeled inputs and outputs.

Supervised learning is divided into two types of data mining problems: classification and regression.


Data scientists are people who use analytical skills to solve complex problems in the business world. Suppose you are interested in a career as a data scientist.

In that case, you must have skills in programming, mathematics, statistics, processing unstructured data, communication, business acumen, and data intuition.

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