10 Essential Skills for Data Scientists [+ Career Tips]

Data scientists. Man programmer using laptop analyzing and development at various information on futuristic virtual interface screen. Algorithm. marketing and deep learning of artificial intelligence.

Data science has taken a substantial role in common, daily tasks — from personalized recommendations online to supply chain logistics that keep businesses running. This relatively new field is known for enhancing efficiency and providing insight across industries.

Reasonably, that means the role of the data scientist is equally important in creating, analyzing, and improving complex sets of data. In this blog post we’ll cover everything you need to know to begin a career in the extremely lucrative field or enhance your existing data scientist skills.

Uses For Data Science

Data science plays many incredibly diverse roles, with applications that span industries and uses. Let’s take a look at some examples to get a sense of the impact of data science and its true potential.

Industry	Uses
eCommerce & Marketing	– Recommendation systems – Predictive analysis – A/B testing – Supply chain optimization
Finance	– Customer data management – Personalized services – Algorithm trading – Risk management
Healthcare	– Personalized medicine; to tailor care to specific health histories and circumstances – Predicting patient deterioration – Administrative logistics – Staffing models
Law Enforcement	– Crime trend analysis – Facial recognition software – Cybercrime detection – Biometric analysis: fingerprints, DNA, etc.
Sports	– Analysis of sports data; player performance, business operations, and recruitment – Optimized training programs – Real-time data collection – Fan demographic review
Transportation	– Predictive maintenance – Demand forecasting – Fleet management – Forecasting infrastructure needs

10 Important Data Science Skills

A successful data scientist will build a unique set of technical and analytical skills, including:

Mathematics & Statistics
Data science relies heavily on algorithms — which can be thought of as directions — that are built from mathematical models. These algorithms allow data scientists to discover patterns, make predictions, and take meaningful information from complex data sets.
Machine Learning & AI
Understanding machine learning and artificial intelligence (AI) allows data scientists to automate and scale models as needed, allowing for automatic processing of large amounts of data.

Data Visualization
The ability to arrange data in a meaningful and visually appealing way allows for easier forecasting and decision-making by stakeholders. Comprehensive data visualization is a great way to convey complex information in a more digestible manner.

Adaptability
Recognizing, understanding, and shifting plans is crucial in the ever-evolving world of data science. When systems are relied on to complete important tasks in real time, adaptability is essential to maintain success.

Programming
Programming is the means by which data scientists manipulate and manage information, making it a vital skillset.

Communication
Being able to communicate clearly with colleagues and stakeholders is essential for the success of any project. Implementing data-driven insights is often a group effort, requiring clear discussion of findings, recommendations, and issues.

Data Processing
Strong data scientists will be able to process data, including large datasets, while ensuring secure storage, accessibility, and accuracy.

Problem-solving
As with any newer technology, troubleshooting is part of the job. Working in data science is no exception. Data scientists must be able to recognize, remedy, and document problems and solutions for future reference.

Ethical Responsibility
Many questions about data gathering — and what information is private — have become common in the realm of data science. As advancements continue, security and transparency have become paramount.

Attention to Detail
Even the smallest error in data processing can render a project unusable. Meticulous attention to detail allows for fewer mistakes and interruptions.

Becoming a Data Scientist

Knowing these important skills for data scientists, it should come as no surprise that earning a degree is the first step in the process. Data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a related field. Some positions require a master’s or doctoral degree.

There is also incredible value in garnering first-hand experience in data science, allowing you to learn from mentors who are already established in the field. Industry-specific knowledge is helpful in some roles, for example, completing coursework or an internship in finance or banking to work as a data scientist at a financial services firm.

How To Improve Data Science Skills

There are multiple ways to improve skills required for data scientists, such as bootcamps, mentorships, networking, and committing to lifelong learning. That’s why the University of San Diego offers two relevant programs — a Practical Data Engineering Certificate and Data Analytics & Visualization Certificate.

Students are given the opportunity to learn from leading data scientists with up-to-the-minute knowledge of real-world applications. This allows newcomers to establish a solid foundation of knowledge, while anyone with experience builds confidence in their ability to contribute to advancements in the field.