Data Scientist Skills You Need in 2023

Data scientist ranked third in U.S. News and World Report’s Best Technology Jobs, 2022 edition, and that comes as no surprise. The U.S. Bureau of Labor Statistics (BLS) estimates the field will grow another 31% (annually) from 2019 to 2029, with 7,100 job openings each year. In addition, data scientists currently earn a median U.S. salary of $101,900. Clearly, job seekers with data scientist skills have a bright future ahead.

Data science fuels profits for everyone — from startups to big tech companies like Facebook, Amazon, Google, and Netflix. Leaders in government, media, healthcare, and countless other industries increasingly lean on data to improve their operations. Any company will find it easier to gain support for decisions driven by past performance. 

Learners in a data science boot camp can prepare for success in the field by gaining data scientist skills in Python, JavaScript, SQL, and Tableau. Through interactive coursework, they can learn how to lead projects, collaborate with others, communicate with executives, and create professional portfolios. 

Do you thrive on learning technical competencies? Love visualizing consumer trends? Want to make a big splash at your workplace? If so, data science might be the path for you.

Data Science Explained

Data science for beginners often starts with understanding what a data analyst is — and how the data scientist definition differs from it. Both positions involve a passion for numbers, statistics, and programming, however, while a data analyst collects data to answer business questions, a data scientist develops new ways of capturing and analyzing data on a macro level that makes the data analyst’s job easier. They may also work with predictive algorithms or build machine learning models. 

Duties of a data scientist may include

• Scrubbing data using programming languages like Python or R
• Mining data using application programming interfaces (APIs)
• Building extract, transform, load (ETL) pipelines
• Performing statistical analysis 
• Creating automation techniques 
• Training machine learning programs
• Developing big data infrastructure 

Many of the skills overlap for either role — such as data mining, warehousing, visualization, Tableau, and SQL — but data scientists go deeper into storytelling, economics, and machine learning. There are a number of roles related to data science, and these can often lead to climbing the career ladder to become machine learning engineers or chief data officers.

For data science explained in greater detail, you might try reading Developing Analytic Talent: Becoming a Data Scientist by V. Granville, which explains the subtle intricacies of data science, the skills required to excel, and how to prepare for a job in this field. There are a number of other data science resources that can help you dig deeper into this exciting field as well.

20 Skills Needed To Be a Data Scientist

Data scientist job postings are filled with terms such as “Tableau,” “Python,” and “Hadoop,” which are just a few of the skills needed to be a data scientist. As you’ll see fairly quickly, data scientists amass a wealth of technical skills in order to perform their daily work. However, it’s not all backroom numbers-crunching for these professionals. A successful data scientist is a curious thinker, people person, and storyteller who often presents their findings and insights to senior management, stakeholders, and clients. 

Whether you choose to spend several years in pursuit of a traditional data science degree or immerse yourself in a comprehensive data science boot camp, a good program helps you hone both the hard and soft skills you’ll need to hit the ground running and contribute meaningfully from day one as a data scientist.

Hard Skills

The dynamic field of data science is particularly heavy on hard skills, which can be acquired through education, training, and practice. Hard data mining skills indicate aptitude, mastery, and expertise within the field. 

Necessary hard skills that enable a data scientist’s core data mining activities include

• • Clustering: Grouping data points based on specific criteria
  • Association: Looking for repetition and potential trends to analyze 
  • Cleaning: Eliminating errors, duplications, corruption, and irrelevant figures
  • Visualization: Presenting information on maps, charts, diagrams, or reports
  • Classification: Designating broad groups within a demographic or user base
  • Machine learning: Training a machine to make logic-based decisions
  • Neural networks: Applying artificial intelligence to accelerate business decisions
  • Outlier detection: Testing for fraud, error, anomalies, and incorrect sampling
  • Prediction: Modeling and forecasting past trends to determine a likely future
  • Data warehousing: Extracting, transforming, and loading data 

Before diving into advanced data science skills, you’ll need strong underpinnings in mathematics — particularly in linear algebra, statistics, probability, and multivariable calculus. This theoretical knowledge will help you understand how algorithms work and how to ultimately create your own.

Proficiency in programming languages such as Java, R, Python, and SQL goes a long way, though non-coders with the ability to analyze and model data sets from different perspectives may also be considered for open data science roles. 

Over time, hard skills requirements will inevitably change as new methods and technologies emerge. In the time it takes for a person to earn a bachelor’s degree, a whole new set of desired skills could become the standard employer requirements. 

Listing hard skills on your resume helps get your foot in the door, but consider applying to a position even if you don’t have experience using Apache Spark, Google’s visualization API, or TensorFlow. Prospective employers will be most keen to see that you’ve learned and applied similar tools to your work, indicating that you can adapt to the ever-evolving demands of the job and the field at large.

Soft Skills

Soft skills refer to how you communicate, lead, manage time, and work with others. While it’s possible to study and improve public speaking ability or critical thinking skills,  there are also innate abilities and qualities that make you well-suited for a role in data science. 

Helpful soft data science skills include

• Strong business acumen: Knowing what it takes for an organization to grow
• Communication skills: Translating data into insights and action
• Data intuition: Knowing when to look beyond the surface to extract more data
• Curiosity: Probing deep and uncovering hidden or overlooked solutions
• Storytelling: Building strong narratives that support data-driven decisions
• Adaptability: Adjusting to changing trends and industry conditions
• Critical thinking: Framing questions to quickly and objectively analyze challenges
• Product knowledge: Using foundational intelligence to build better stories and models
• Collaboration: Working as a team player, as well as independently
• Resourcefulness: Identifying new opportunities and developing solutions with existing resources

Data scientists are often referred to as “unicorns” due to their unique mindset: they’re able to assess the big picture and longview while simultaneously whittling vast data sets down to the details that matter. They solve problems at the macro level and resolve more granular matters of data security, reliability, and accessibility along the way. A data scientist always strives for more efficient ways to capture, clean, and use information to uncover actionable insights. 

You can excel in data science if you love to explain complex subjects in a simple manner that’s tailored to your audience. If you’re the type of person who is always asking questions and referencing the best available data to make decisions in your personal life, you may find it very rewarding to apply the same diligence professionally.

Data Science Careers

Once you have the right skills, you can start exploring several types of data science jobs. As you will notice below, data science job growth is at an all time high. Some of the most popular data science careers include the following.

Data mining analyst

Data mining analysts develop computer programs to analyze large customer information databases for companies and organizations. Specifically, they look for non-intuitive patterns that can uncover valuable insights and emerging trends. However, they must also have the skills and experience to discern the difference between outlying trends and those that are relevant. This role is ideal if you have strong logic skills, love complex hidden-object games, solving mysteries, and are naturally curious.

According to LightcastTM 2022 Labor Insights: 

• The median annual salary is $73,279.
• There are 91,639 available jobs per year.
• The industry will grow by 12.3% over the next 10 years.

Data analyst

Data analysts visualize data and transform it into relevant reports for particular teams, departments, projects, or objectives. Duties may include accessing and cleaning vast data sets, performing statistical analysis, visualizing information through graphs, and communicating findings. This role is ideal if you love numbers and you’re naturally organized and methodical.

According to BLS data from 2020–2021:

• The median annual salary is $82,360. 
• There are 104,100 available jobs per year.
• The industry will grow by 25% over the next 10 years.

Data engineer

Builders by nature, data engineers design, create, and maintain data pipelines. They organize, format, clean, and prepare data for analysis. This role is ideal if you love programming and testing. 

According to LightcastTM 2022 Labor Insights:

• The median annual salary is $111,435. 
• There are 45,851 available jobs per year.
• The industry will grow by 20.8% over the next 10 years.

Data scientist

A data scientist knows a bit of everything, playing an active role in every step of the data collection and analysis process. They develop new approaches to help with business challenges and assist analysts in visualizing data most effectively. Data science careers can veer more toward statistics and programming or more toward trend forecasting and management. This is ideal if you love solving challenges through big-picture thinking.

According to LightcastTM 2022 Labor Insights: 

• The median annual salary is $112,614.
• There are 50,567 available jobs per year.
• The industry will grow by 14.5% over the next 10 years.

How to Get a Job in Data Science

With high demand for data science skills and commensurate pay, building your data scientist qualifications is certainly a wise investment in your future. To get started: 

• Know yourself. Wondering how to get a job in data science was the precursor for many who are now in successful data science careers. Interest, curiosity, and a problem-solving mentality are essential data scientist qualifications that can’t be taught — yet, you’ll draw upon these innate soft skills every day working in this role.
• Gain skills. A bachelor’s degree in mathematics, computer science, or statistics can be helpful, but it’s not always a prerequisite for data scientist roles. Data science boot camps can also be a great way to upskill in data science. However, what matters most is that you can demonstrate proficiency in programming languages, big data organization and visualization, machine learning, and communication. 
• Collect experience. If you’re still enrolled in courses, internship opportunities provide valuable hands-on experience working alongside a data scientist. Recent college graduates may begin work with tech startups or large companies with robust training programs. If you prefer to take the DIY route, platforms like Kaggle, Omdena, and GitHub offer portfolio-building exercises and projects. 
• Get coached. Take advantage of 1:1 career coaching if your data science program offers it. Career service teams may provide portfolio reviews, technical interview training, resume and social media profile support, and panels featuring employers who are eager to connect with learners.
• Interview well. Ace your interview by preparing with a career services mentor who is invested in your success. While processes will vary by company, you can expect a phone screening with human resources to evaluate your potential culture-adds. Companies often send out skill evaluations, such as a request for data analysis or error scrubbing. Next, you’ll have a phone interview to evaluate math, statistics, coding, and communication skills. Finally, you’ll land a face-to-face interview where managers assess your passion for the company and data science, your willingness to learn, and your portfolio.

Learn Data Science Today

Given the current demand for data scientist skills, employers are partnering with training programs that serve as a continuous pipeline for highly skilled employees. At the same time, learners are pursuing faster paths to high-paying data science careers. They may work a day job to support themselves, raise a young child, or take foundational college courses while taking data science courses to build their skill sets. 

Data science boot camps provide one of the best opportunities to learn data science today. They offer a flexible schedule, comprehensive curricula, and career services support, helping learners gain data scientist skills in as little as 24 weeks.

A typical part-time boot camp schedule might involve live virtual courses on weeknights. Part-time learners generally devote 20 hours of outside study time per week to absorb the material.

Data Scientist Skills FAQs

Still curious? Others pursuing a career path in data science frequently ask: