Essential Non Technical Skills of Effective Data Scientists

Foster curiosity, common sense, and communication in data scientists by asking questions, exploring data discovery and preparation, and using what-if analysis to challenge assumptions and drive business success.

Develop strong business acumen by translating data insights into actionable recommendations for business and IT leaders, understanding industry problems, customer lifecycle, and how data can drive product quality.

Develop strong communication skills for data scientists to translate technical findings into actionable insights for non-technical teams, empowering decisions and promoting data literacy across the organization.

Data scientists collaborate across the organization, partnering with executives, product managers, designers, marketers, and developers to build data pipelines, improve workflow, and create better products through united teamwork.

Develop critical thinking to perform objective data analysis, frame the right questions through user interviews, and question the data to remove bias and reveal actionable insights.

Data scientists must consider data ownership, rights, and privacy to ensure ethical use of data, mitigate bias, and protect people affected by automated decisions.

Develop active listening as a core data science skill by truly hearing colleagues, understanding priorities and business impact, and delivering insights that address challenges and opportunities.

Cultivate open mindedness as a data scientist to welcome diverse ideas, cultures, people, and results. An open mind enables creative insight, breakthroughs, and patient problem solving.

Explore data from multiple sources and apply creativity to modeling, data collection, and visualization. Identify how a creative data scientist analyzes meaning and recommends ways to apply findings.

Develop data intuition by perceiving patterns beyond surface level, evaluating data distribution, handling missing data, and testing multiple methods to explain results and identify likely causes.

Balance intuition with data to guide data scientists through data driven decision making. Enable a self-service data model with security and governance, plus training and executive advocacy to sustain it.

Drive data science success through collaboration and teamwork, exchanging discoveries, sharing ideas, insights, and criticisms, while partnering with executives, stakeholders, and customers to streamline workflows and access essential data.

Develop proactive problem solving for data scientists by identifying assumptions, resources, and opportunities, explaining problems and solutions, and pivoting to the most effective methods to uncover root causes.

Integrate data, visuals, and narrative to turn insights into compelling stories that persuade stakeholders and drive action, highlighting the growing role of data storytelling in self-service analytics and business intelligence.

Develop adaptability to thrive amid accelerated technology change, as data scientists collaborate across analytics disciplines, quickly learn new technologies, and remove bottlenecks to keep projects moving.

Develop essential non-technical skills for data scientists by advancing soft skills, online learning, coding proficiency, community involvement, reading materials, and mentorship to boost hireability and career relevance.