List of Skills for Data Scientists
For Resumes, Cover Letters, and Interviews
A data scientist is a broad term that can refer to a number of types of careers. Generally, a data scientist analyzes data to learn about scientific processes. 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. Data scientists need statistical, analytical and reporting skills.
Here's a list of data scientist skills for resumes, cover letters, job applications, and interviews. Included is a detailed list of the five most important data scientist skills, as well as a longer list of even more related skills.
How to Use a Skills Lists
You can use these skills lists throughout your job search process. Firstly, you can use these skill words in your resume. In the description of your work history, you might want to use some of these key words.
Secondly, you can use these in your cover letter. In the body of your letter, you can mention one or two of these skills, and give a specific example of a time when you demonstrated those skills at work.
Finally, you can use these skill words in an interview. Make sure you have at least one example of a time you demonstrated each of the top five skills listed here.
Of course, each job will require different skills and experiences, so make sure you read the job description carefully and focus on the skills listed by the employer.
Top Five Data Scientist Skills
Perhaps the most important skill for a data scientist is to be able to analyze information. Data scientists have to look at, and make sense of, large swaths of data. They have to be able to see patterns and trends in the data, and explain those patterns. All of this takes strong analytical skills.
Being a good data scientist also means being creative. Firstly, you have to use creativity to spot trends in data. Secondly, you need to make connections between data that might seem unrelated. This takes a lot of creative thinking. 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.
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, 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.
While soft skills like analysis, creativity, and communication are important, hard skills are also critical to the job. A data scientist needs math skills, particularly in multivariable calculus and linear algebra.
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 important.
Data Scientist Skills
- Analytical Tools
- Big Data
- Computer Skills
- Constructing Predictive Models
- Conveying Technical Information to Non-Technical People
- Creating Algorithms
- Creating Controls to Assure Accuracy of Data
- Critical Thinking
- Cultivating Relationships with Internal and External Stakeholders
- Customer Service
- Data Analysis
- Data Analytics
- Data Manipulation
- Data Wrangling
- Data Science Tools
- Data Tools
- Data Mining
- Decision Making
- Decision Trees
- Drawing Consensus
- Evaluating New Analytical Methodologies
- Executing in a Fast-Paced Environment
- Facilitating Meetings
- Google Visualization API
- High Energy
- Information Retrieval Data Sets
- Interpreting Data
- Linear Algebra
- Logical Thinking
- Machine Learning Models
- Machine Learning Techniques
- Microsoft Excel
- Mining Social Media Data
- Modeling Data
- Modeling Tools
- Multivariable Calculus
- Problem Solving
- Producing Data Visualizations
- Project Management
- Project Management Methodologies
- Project Timelines
- Providing Guidance to IT Professionals
- Reporting Tool Software
- Reporting Tools
- Risk Modeling
- Scripting Languages
- Self Motivated
- Statistical Learning Models
- Statistical Modeling
- Taking Initiative
- Testing Hypotheses
- Working Independently