Careers Finding a Job Important Job Skills for Data Scientists Share PINTEREST Email Print Compassionate Eye Foundation / Jamie Grill / Getty Images Finding a Job Job Searching Skills & Keywords Resumes Salary & Benefits Letters & Emails Job Listings Job Interviews Cover Letters Career Advice Best Jobs Work-From-Home Jobs Internships Table of Contents Expand Skills You Need to Be a Data Scientist Types of Data Scientist Skills Analytical Skills Open-Mindedness Communication Mathematics Programming / Technical Proficiencies More Data Scientist Skills By Alison Doyle Updated on 01/30/21 Over 2.5 quintillion bytes of data are created every day, according to research. Data scientists help organizations wrangle, interpret, and visualize that data. Unsurprisingly, the job is booming. According to the Bureau of Labor Statistics, career opportunities in this field are anticipated to grow 15% by 2029, much faster than average. While not all successful data scientists have college degrees, many have at least a bachelor’s degree in data science or a related field. Some also have graduate degrees, including master’s, Ph.D., and/or graduate certifications. What Kind of Skills Do You Need to Be a Data Scientist? “Data scientist” is a broad term that can refer to a number of different careers. Generally, a data scientist analyzes data to learn about scientific processes, market trends, and risk management. Some job titles in data science include data analyst, data engineer, computer and information research scientist, operations research analyst, and computer systems analyst. Data scientists work in a variety of industries, ranging from tech to medicine to government agencies. The qualifications for a job in data science vary because the title is so broad. However, there are certain skills employers look for in almost every data scientist. For example, data scientists need strong statistical, analytical, reporting skills, and more. Types of Data Scientist Skills Analytical Skills Perhaps the most important skill for a data scientist is to be able to analyze information. Data scientists look at, and make sense of, large amounts of data. They must be able to see patterns and trends and have an idea of what those patterns mean. All of this takes strong analytical skills. Artificial Intelligence Big Data Business Intelligence Constructing Predictive Models Creating Controls to Assure Accuracy of Data Critical Thinking Data Analysis Data Visualization Data Analytics Database Management Data Manipulation Data Wrangling Data Science Tools / Data Tools Data Mining DevOps Evaluating New Analytical Methodologies Interpreting Data Metrics Mining Social Media Data Modeling Data Modeling Tools Probability and Statistics Research Risk Modeling Testing Hypotheses Open-Mindedness Being a good data scientist also means being creative. First, you have to have an open mind in order to spot trends in data. Secondly, you need to make connections between data that might seem unrelated to someone that is biased. This takes a lot of open-mindedness. Finally, you need to explain this data in ways that are clear to the executives at your company. This often requires creative analogies and explanations. AdaptabilityConveying Technical Information to Non-Technical PeopleCreativityCuriosityDecision MakingDecision TreesExecuting in a Fast-Paced EnvironmentInnovationLogical ThinkingProblem SolvingWorking Independently Communication Data scientists not only have to analyze data, but they also have to explain that data to others. They must be able to communicate data to people of different skill sets, explain the importance of patterns in the data, and suggest solutions. This involves explaining complex technical issues in a way that is easy to understand. Often, communicating data requires visual, oral, and written communication skills. Assertiveness Collaboration Consulting Cultivating Relationships with Internal and External Stakeholders Customer Service Documenting Drawing Consensus Facilitating Meetings Leadership Mentoring Presentation Project Management Project Timelines Providing Guidelines to IT Professionals Reporting Storytelling Skills Supervisory Skills Training Verbal Communication Written Communication Mathematics While soft skills like analysis, creativity, and communication are important, hard skills are also critical to the job. A data scientist needs strong math skills, particularly in multivariable calculus and linear algebra. Identifying Algorithms Creating and Maintaining Algorithms Information Retrieval Data Sets Linear Algebra Machine Learning Models Machine Learning Techniques Multivariable Calculus Statistics Statistical Learning Models Statistical Modeling Programming and Technical Proficiencies Data scientists require basic computer skills, but programming skills are particularly important. Being able to code is critical to almost any data scientist position. Knowledge of programming languages such as Java, R, Python, or SQL is essential. AppEngineAmazon Web Services (AWS)AmChartsApache SparkC++Computer SkillsCouchDBjsECLFlareGoogle Visualization APIHadoopHBaseHighchartsJavaMATLABMicrosoft ExcelMicrosoft Office SuiteNoSQLPerlPythonRReporting Tool SoftwareSaaSSASScripting LanguagesSQLTables and QueriesTableauTensorFlow More Data Scientist Skills Mining Social Media Data Tables and Queries Project Management Project Timelines Cultivating Relationships with Internal and External Stakeholders Customer Service AppEngine Amazon Web Services (AWS) CouchDB js ECL Flare Google Visualization API Hadoop HBase R SAS Scripting Languages Mobile Devices Microsoft Office Suite SaaS Artificial Intelligence (AI) Apache Spark Curiosity Business Intelligence Innovation How to Make Your Skills Stand Out ADD RELEVANT SKILLS TO YOUR RESUME: Include your skills in your resume—in an initial summary of qualifications, in your work history section, or in a tech table describing your hardware and software skills. HIGHLIGHT SKILLS IN YOUR COVER LETTER: You should also describe your command of the most important of these skills in your cover letter. USE SKILL WORDS IN YOUR JOB INTERVIEW: In your interview, be sure to enhance your responses with examples of your skills.