15 Top Skills for Data Scientists (With Job Duties)

By Indeed Editorial Team

Updated September 13, 2022

Published October 9, 2020

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Companies collect vast amounts of data about customer behavior, market trends, operating expenses and other measurable components of business operations. They often hire data scientists to find business insights within that data using technical and analytical skills. If you're interested in becoming a data scientist, learning about their key skills can help you decide if this is the right career path for you.

In this article, we define data scientists and describe 15 common skills they use to achieve their professional goals.

What is a data scientist?

Data scientists are information technology (IT) professionals who collect, organize and analyze vast amounts of data for a company or other organization. The information they collect might depend on the industry or the company's mission.

For example, a data scientist who works for a social media company might analyze user behavior, click-through rates and membership trends to understand how the brand is performing against competitors. Typically, data scientists work in an IT department, but they might support every department at the company, including sales, operations and customer service.

Here are some common responsibilities for data scientists:

  • Deciding which data sources to include in analyses

  • Building algorithms for data collection

  • Cleaning and standardizing data

  • Identifying trends in data

  • Creating reports about data trends

  • Making recommendations to company leaders and other stakeholders

Read more: Learn About Being a Data Scientist

Data scientist hard skills

Data scientists use a wide range of hard skills, which are technical abilities they learn in educational programs or through work experience. These abilities help them process data and identify trends. Here are 11 hard skills that a data scientist may possess:

1. Cloud computing

Cloud computing is the process of storing data in the cloud, which is a group of internet storage resources. Companies use cloud computing because cloud storage is typically inexpensive and secure compared to other data storage options. The cloud provides a place for data scientists to hold, retrieve and share data. Because many companies use cloud computing for their servers, storage and databases, data scientists use their skills to navigate their shared cyberspace.

Read more: What Is Cloud Computing? (With Usage Info and Benefits)

2. Statistics and probability

At its core, data science involves working with algorithms, systems and processes to gain insight into data. Data scientists use their knowledge of statistics and probability to predict how different data might perform and to make recommendations to their company's leadership team based on the available information.

With skills in statistics and probability, data scientists can predict trends and develop forecasts, discover anomalies that exist in the data set, establish a relationship between two points in the data and interpolate missing data points.

Related: Math vs. Statistics: What's the Difference?

3. Advanced mathematics

Along with statistics, data scientists use both multivariate calculus and linear algebra to perform their work. They use calculus to build machine learning models, and they may also work with derivatives, cost function, plotting, gradients and algebra. When working with advanced mathematics, data scientists can use tools to help them perform calculations, so it becomes more important that they know the principles of calculus and algebra and how they can affect their reports.

4. Machine learning

Machine learning is the artificial intelligence that data scientists use to find patterns in data and perform complex calculations. This tool helps relieve some responsibilities that data scientists have and helps prevent human error.

It's even more valuable when data scientists have very large sets of data to work with because machine learning can develop workable algorithms and models, allowing data scientists to analyze data in real-time. Many data scientists learn how to use machine learning through advanced graduate courses or certification programs.

Read more: Guide to Machine Learning and Business Intelligence

5. Data visualization tools

Data scientists use data visualization tools to translate information and data into visuals like relationship maps, 3D plots, bar charts, histograms, line plots and pie charts. They may create these resources to help themselves understand their own data report better and from another perspective, or provide details to partners who requested information from the data. With data visualization tools, it's easier for data scientists to see any trends, patterns and outlying points of data in a set.

Read more: What Is Data Visualization? Types, Uses and Tips

6. Query languages

A query language is a computer language that data scientists use to ask about databases and the information they hold. The most common query language is structured query language (SQL). Using this language allows data scientists to quickly retrieve data and use it to form solutions for a certain issue or answer specific questions. While data scientists might also know other query languages, SQL is often the language that employers expect from job candidates.

Related: Top Interview Questions About SQL Server

7. Database management

While many companies have both database managers and data scientists, the latter might perform some database management in their daily work. Knowing basic database management concepts can help a data scientist retrieve information from company databases more quickly. There are various database management tools and systems that data scientists may become familiar with so they can work with different companies to store, update and read the data from their systems.

8. Python coding

Python is an open-source programming language that data scientists use to manipulate data and understand it more. Python syncs well with machine learning and other artificial intelligence tools to provide data in a digestible format that even beginning data scientists can use. Data scientists might learn to use Python in computer programming classes in their degree pathway or by taking additional certification courses in the subject.

Read more: What Is a Python Class? (With Example)

9. Microsoft Excel

While there are more complex ways to form data listings, Microsoft Excel is a basic program that data scientists can use. With Excel, data scientists can create a database with custom labels, sort and filter data and form tables with calculation functions. They can also use Excel to export data to many other programs.

Related: FAQs about Microsoft Office Certifications (With Examples)

10. R programming

R is an open-source programming language with many statistical applications, making it particularly valuable in a data scientist's work. They can use this language to build tools that integrate with different programs and collect data from various sources.

Using R allows a data scientist to analyze data, identify trends, create visualizations involving data points or groupings and predict future data. Like other programming languages, R is featured in computer science degree programs, but a data scientist can also learn to use this language by taking a certification course.

Read more: R vs. Python: What Are the Differences?

13. Data wrangling

Another skill that many data scientists have is data wrangling, which is the process of cleaning raw data, removing outliers, changing null values and turning the data into a format that they can use in different programs.

Knowing how to wrangle data allows a data scientist to use information from various sources, even if the data points have different formats. By using data wrangling, a data scientist can send vast amounts of data to analytics tools, which can help them identify trends in the information.

Related: 11 Types of Data Science Jobs (With Responsibilities)

Data scientist soft skills

Along with hard, or technical, skills, data scientists use soft skills to achieve their professional goals. Soft skills are personal traits, like communication and resilience, that help professionals work effectively with others and manage their workloads. Here are four of the top soft skills for data scientists:

1. Independence

One of the most important soft skills for data scientists is the ability to work with minimal supervision. Many of a data scientist's key tasks, like building machine learning models, analyzing data sources and creating reports, involve a lot of individual work. Data scientists who consistently meet their deadlines independently might find it easier to advance in their careers, as independence is a key leadership trait in many industries.

2. Communication

While data scientists often complete their data collection and analysis tasks independently, they interact with colleagues and other people often. The information they collect and analyze often helps other departments run more efficiently, so the data scientist typically shares their findings with employees in the departments they support.

For example, a data scientist for a fulfillment company might analyze data about order trends to predict spikes in online business. After completing their analysis, they might meet with the operations team to explain what the data suggests about future ordering trends.

3. Project management

Data scientists might use their project management skills to lead strategic projects that involve data management. Project management skills ensure that project teams achieve their goals, meet deadlines and stay within their budgets. Depending on their background, a data scientist might develop these skills through work experience or by taking a course in project management.

Read more: How To Improve Project Management Skills (With Additional Tips)

4. Analytical thinking

A data scientist's need for analytical thinking goes beyond evaluating data. They might use their ability to think analytically to create data management systems and choose appropriate software products for their needs. Analytical thinking can also allow a data scientist to solve problems in the data collection and analysis processes, allowing them to collect more pertinent data and identify trends more accurately.

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