6 Skills for Every Data Scientist: Soft & Technical Skills

The useful skills for a data scientist include a variety of soft and technical skills. While some skills may be used more than others depending on the field a data scientist is working within, a majority of the skills are still useful regardless of the data scientist's position. Familiarizing yourself with these skills may help you become a data scientist or enhance your experience portfolio.

The following are some of the more common technical and soft skills for a data scientist to have:

Technical Skills:

1. Data Visualization
2. Programming/Software
3. Statistics/Mathematics

Soft Skills:

1. Communication
2. Business Acumen
3. Data-Driven Problem Solving

While many choose a master’s in data science program to gain data science skills, there are other ways too. Read on to learn more!

Data Science Technical Skills

Though a data scientist may require many skills, their technical knowledge is what sets them apart. There are many technical skills and specialized tools that data scientists need to be familiar with. Different businesses will use different tools and languages in their workflow. However, all data scientist positions will require a core set of technical knowledge applicable to a wide range of problems. These core technical skills could be considered essential for all data scientists. According to the U.S. Bureau of Labor Statistics, data scientists earned a median annual wage of $120,230 as of May 2025, with 262,440 employed nationally.

Data scientists use programming to apply techniques such as machine learning, artificial intelligence (AI) and data mining. They should have an understanding of the mathematics and statistics involved in these techniques in order to understand when to apply each technique. In addition to understanding the fundamentals, data scientists should be familiar with the popular programming languages and tools used to implement these techniques. They should also understand the principles of software engineering in order to integrate the languages and tools they use.

1. Data Visualization

Data visualization could be an essential data scientist skill. Humans are inherently visual and have far easier time recognizing patterns visually. Visualization plays two essential and equally important roles in data science. First, it enables the data scientist to see patterns and inform their exploration of the data. Second, it allows them to tell a compelling story using data. These are both essential parts of the data science workflow.

Scatter plots and histograms are essential elements of exploratory data analysis. Without visualizing data, it is difficult to know where to start. Deriving meaning from data only matters if you can share that meaning with others. In order to do this, the data should be presented in attractive and informative visuals. The data scientist skill of data storytelling requires a data scientist to creatively use data visualization to craft a narrative that informs the audience and explains their reasoning. Without these tools, data science could be ineffective at implementing change.

There are many data visualization tools available to data scientists: most programming languages provide libraries for visualizing data. Python data visualization can be done with Matplotlib and pandas. JavaScript offers the D3.js visualization library. R offers ggplot2 as well as many other data visualization tools. Tableau is a high-level platform for visualizing data from many different sources.

2. Programming/Software

Data scientists use a variety of programming languages and software packages to flexibly and efficiently extract, clean, analyze, and visualize data. Though there are always new tools in the rapidly changing world of data science, a few have become essential across most roles. Here are the core tools that aspiring data scientists should familiarize themselves with:

• R: R was once confined almost exclusively to academia, but social networking services, financial institutions, and media outlets now use this programming language and software environment for statistical analysis, data visualization, and predictive modeling. R is open-source and has a long history of use for statistics and data analytics. This means it has a huge network (called CRAN) that provides packages for many data analysis tasks.
• Python: Python is the dominant programming language in data science. The pandas library was created to address the limitations of Python's general-purpose origins, enabling efficient data storage and vectorized processing. Organizations such as Bank of America and Meta use Python for data science at scale. The language is powerful, fast, friendly, open, and easy to learn, with a vast ecosystem of libraries covering everything from data analysis to machine learning and AI.
• Tableau: Tableau offers a suite of products that complement programming-based tools such as R and Python. Tableau may not be the best tool for cleaning or reshaping data, but it is widely used for data exploration and interactive analysis, providing a high-level interface for visualizing data through dynamic dashboards without requiring code.
• Jupyter Notebooks: Jupyter Notebooks are the standard working environment for most data scientists, allowing users to combine code, visualizations, narrative text, and results in a single shareable document. It supports Python, R, and dozens of other languages and is widely used for exploration, reproducibility, and communication of findings to colleagues.
• Cloud Platforms (AWS, GCP, Azure): Data scientists are expected to have working familiarity with at least one major cloud platform. Unlike data engineers — who build and manage cloud infrastructure — data scientists primarily use cloud services to access and store data, run notebooks and training jobs, and leverage managed machine learning services such as AWS SageMaker, Google Vertex AI, and Azure Machine Learning. Proficiency with cloud platforms has become a baseline expectation across most data science roles.
• Git/GitHub: Version control is now considered a professional baseline for data scientists. Git allows teams to track changes to code and notebooks, collaborate without overwriting each other's work, and maintain a reproducible project history. GitHub is the most widely used platform built on Git and is where most open-source data science projects and professional portfolios live.
• SQL: SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. There are multiple implementations of the same general syntax, including MySQL, SQLite, and PostgreSQL. Some of what you can do with SQL — data insertion, queries, updating and deleting, schema creation and modification, and data access control — you can also accomplish with R or Python, but writing your own SQL code is more efficient and yields reproducible scripts.
• Apache Spark: A cluster computing framework that enables the distributed processing of large datasets across many machines in parallel. Spark is significantly faster than earlier big data tools and supports batch processing, real-time streaming, and machine learning at scale. It is the de facto standard for large-scale data processing in enterprise environments.
• AI & Generative AI Tools: The rapid growth of large language models and generative AI since 2023 has added a new dimension to data scientists’ toolkits. As of 2026, familiarity with LLM APIs, prompt engineering, and AI evaluation frameworks is increasingly expected in data science roles. Python libraries such as LangChain, LlamaIndex, and Hugging Face Transformers have become standard tools for working with AI models at scale.

3. Statistics/Mathematics

Software runs all the necessary statistical tests these days, but a data scientist still needs the statistical sensibility to know which test to run, when to run it, and how to interpret the results. A solid understanding of multivariable calculus and linear algebra, which form the basis of many data analysis techniques, is likely to allow a data scientist to build in-house implementations of analysis routines as needed. An understanding of statistical theorems helps data scientists assess the capabilities, limitations, and assumptions of these techniques. A data scientist should understand the assumptions that need to be met for each statistical test.

Data scientists don’t only use complex techniques like neural networks to derive insight. Even linear regression is a form of machine learning that can provide valuable information. Simply plotting data on a chart and understanding what it means are basic but essential first steps in the data science process. Mathematical concepts such as logarithmic and exponential relationships are common in real-world data. Understanding and applying both the fundamentals and advanced statistical techniques are skills data scientists need to derive meaning from data.

Though much of the mathematical heavy lifting is done by computers, understanding what makes this possible is essential. Data scientists are tasked with knowing which questions to pose and how to get computers to answer them. Computer science is in many ways a field of mathematics. Therefore, the need for mathematical data scientist skills is clear. Understanding concepts such as rational and irrational numbers helps data scientists write efficient and accurate code.

Data Scientist Soft Skills

Data science requires a diverse set of skills. It is an interdisciplinary field that draws on aspects of science, math, computer science, business and communication. Data scientists may benefit from a diverse skill-set that enables them to both crunch the numbers and effectively influence decisions.

Because data scientists focus on using data to influence and inform real-world decisions, they should be able to bridge the gap between numbers and actions. This requires skilled communication and an understanding of the business implications of their recommendations. Data scientists should be able to work as part of a larger team, providing data-driven suggestions in a compelling form. This requires skills that go beyond the data, statistics and tools that data scientists use.

1. Communication

Data scientists should be able to report technical findings such that they are comprehensible to non-technical colleagues, whether corner-office executives or associates in the marketing department.

Make your data-driven story not just comprehensible but compelling.

The required data scientist skills can be sliced and diced in different ways. Mitchell Sanders’s Data Science Central blog post concludes with an assortment of breakdowns, and perusing these may help you wrap your head around what it takes to make it as a data scientist. It is also important to remember, as Dave Holtz points out on the Udacity blog, that the “data scientist” job title encompasses a variety of positions, which may demand vastly different data scientist skills from applicants. Holtz’s post identifies four types of data scientist jobs and breaks down which skills are most vital for each.

One important data scientist skill is communication. In order to be effective as a data scientist, people need to be able to understand the data. Data scientists act as a bridge between complex, uninterpretable raw data and actual people. Though cleaning, processing and analyzing data are essential steps in the data science pipeline, this work is useless without effective communication.

Effective communication requires a few key components. It starts with effective visualization. Humans are inherently visual and can understand and process data better when it is presented visually. This step is essential both for data exploration and communication.

Visualization allows a data scientist to craft a compelling story from data. Whether the story describes a problem, proposes a solution or raises a question; it is essential that the data be presented in a way that leads the audience to reach the intended conclusions. In order for this to happen, data scientists should describe the data and process in a shared language, avoiding jargon and unnecessary complexity.

2. Business Acumen

Data science is applicable to various fields. Each industry has unique goals, datasets and constraints. In order for a data scientist to be effective, they should understand the business they are applying their skills to.

Business awareness could now be considered a prerequisite for effective data science. A data scientist should develop an understanding of the field they are working in before they are able to understand the meaning of data. Though some metrics, like profit and conversions, exist across industries, many key performance indicators (KPIs) are highly specialized. This data makes up the industry’s business intelligence, which is used to understand where the business is and the historical trends that have taken it there.

The unique goals, requirements and limitations of each industry define every step that a data scientist takes. Without understanding the underlying aspects of the industry, it could be impossible to find meaningful insight or make useful recommendations.

A data scientist may be most effective when they truly understand the business they are advising. Though data can provide unique insights, it may not capture the full picture. This requires a data scientist to be aware of the processes and realities at play in their industry. Though they may share a job title, the precise goals and tasks of a data scientist will vary greatly by industry. To be successful, a data scientist should understand the industry that they are working in.

3. Data-Driven Problem Solving

Data-driven problem solving allows data to inform the entire data science process. By using a structured approach to identify and frame problems, the decision making process could be simplified. In data science, the vast quantity of data and tools creates nearly endless avenues to pursue. Managing these decisions is an essential job for a data scientist. Data science both informs and is informed by the data-driven problem solving process.

A data scientist is likely to know how to productively approach a problem. This means identifying a situation’s salient features, figuring out how to frame a question that will yield the desired answer, deciding what approximations make sense, and consulting the right co-workers at the appropriate junctures of the analytic process. All of that in addition to knowing which data science methods to apply to the problem at hand.

A data scientist’s job is to understand how to take raw data and derive meaning from it. This requires more than just an understanding of advanced statistics and machine learning. They also need to integrate their understanding of the problem domain, available information and their goals when deciding how to proceed.

Data science problems and solutions are never obvious. There are many possible paths to explore, and it is easy to become overwhelmed with the options. A structured approach to data-driven problem solving allows for a data scientist to track and manage progress and outcomes. Structured techniques such as Six Sigma are great tools to help data scientists and teams solve real world data science problems.

Information last updated June 2026, using May 2025 BLS OEWS data. Job growth projections from BLS Employment Projections, 2024–2034.