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.
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 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.