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Data Science & Analytics

The 10 Most Important Data Science Skills in 2023

Written By Ryan Loftus | February 3, 2023

From large language models to AI-power climate science, the data science field is producing more diverse and exciting innovations than ever before. In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works.

But as the possibilities of data science have expanded, so have the skills necessary to succeed as a data scientist. Top data scientists will need to balance their skill set between exciting new disciplines like natural language processing while maintaining traditional skills like statistics and database management. 

Artificial Intelligence

Artificial intelligence (AI) is the ability of a digital computer to perform tasks associated with intelligent beings. 

First theorized by Alan Turing in 1950, artificial intelligence (AI) has become a fast evolving discipline behind the world’s most innovative technologies. While AI has been around for decades, we’ve only begun to unlock its potential. Some experts believe AI is poised to usher in the next era of human civilization, with Google CEO Sundar Pichai comparing the advancement of AI to the discovery of fire and electricity. Given the nearly endless number of potential applications—including cancer treatment, space exploration, and self-driving cars—the tech industry’s need for data scientists with AI skills is vast.

AI is a complex and evolving field with numerous branches and specializations.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. With applications including fake news detection and cyberbullying prevention, NLP is among the most promising trends in data science.

Machine Learning

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning algorithms are dependent on human intervention and structure data to learn and improve their accuracy. Data scientists build machine learning algorithms using programming frameworks such as TensorFlow and PyTorch. 

Deep Learning

Deep learning is a sub-field of machine learning characterized by scalability, consumption of larger data sets, and a reduced need for human intervention.

The launch of ChatGPT in late 2022 was a pivotal moment for deep learning, giving consumers their first hands-on exposure to the potential of the discipline. With applications including autonomous vehicles, investment modeling, and vocal AI, deep learning is an exciting field of artificial intelligence.

Deep learning frameworks that data scientists use include TensorFlow, PyTorch, and Keras.

Database Management

Database management is the process of organizing, storing, and retrieving data on a computer system. Data scientists use database management to cultivate and interpret data.

Database skills can be divided into two different categories. The type of databases data scientists work with will vary depending on their specialization or the needs of a given project.

Relational databases use structured relationships to store information. Data scientists use the programming language SQL to create, access, and maintain relational databases. Relational database tools include SQL Server Management Studio, dbForge SQL Tools, Visual Studio Editor, ApexSQL.

Non-relational databases store data using a flexible, non-tabular format. Also known as NoSQL databases, non-relational databases can use other query languages and constructs to query data. Non-relational database tools include mongoDB, Cassandra, ElasticSearch, Amazon DynamoDB.

Data Wrangling

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. Data wrangling is the process of transforming raw data into a format more valuable or useful for downstream applications. Also referred to as cleansing or remediation, data wrangling is an essential of data science and analysis. However, this process is both time and labor intensive. Some sources estimate that data scientists spend most of their time on this mundane but vital task. Automated data cleansing using AI-based platforms is emerging as an efficient and scalable way for data scientists to work.

Because the creation of raw data is accelerating, it should come as no surprise that employer demand for the ability to transform data is accelerating. In 2022, demand for data wrangling grew by 405%, tying for first in our list of fastest-growing technical skills.

Data Modeling

Data modeling is the process of creating and analyzing a visual model that represents the production, collection, and organization of data. Data models are vital for understanding the nature, relationships, usage, protection, and governance of a company’s data. Tools that data scientists use for data modeling include ER/Studio, Erwin Data Modeler, SQL Database modeler, DBSchema Pro, and IBM InfoSphere Data Architect.

In 2022, demand for data modeling grew by 308%, ranking third in our list of fastest-growing technical skills.

Data Visualization

After unlocking valuable insights from raw data, data scientists need to communicate their findings in a clear and visual format. Data visualization is the process of creating graphical representations of data for presenting insights to technical and non-technical stakeholders. Data scientists create visuals like graphs, charts, and maps using data visualization tools such as Tableau or front-end languages such as JavaScript.

Like data wrangling, employer demand for this skill is accelerating. In 2022, demand for data visualization also grew by 405%, tying for first with data wrangling in our list of fastest-growing technical skills.


Data scientists use a range of programming languages to work with data. While there are a number of languages used in the field of data science, an individual data scientist might only learn a few languages that align with their specialization, interests, and career path.

Languages used for data science include:

  • Python (used for math, statistics, and general programming)
  • Java (used for data analysis, data mining, machine learning)
  • Julia (used for numerical analysis and computer science)
  • MATLAB (used for deep learning and numerical analysis
  • R (used for statistical computing and machine learning
  • Scala (used for big data and scalability)
  • C/C++ (used for scalability and performance)
  • JavaScript (used for data visualization)

Math and Statistics

One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while a job description may or may not mention it, competency in the following subjects is vital:

  • Statistics
    • Probability theory
    • Classification 
    • Regression
    • Clustering
  • Linear Algebra
  • Calculus
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