Essential Skills Required for Data Scientists


Both technical and non-technical skills, like programming and curiosity, are required for a data scientist. Read our 12 essential data scientist skills here.
It’s no surprise that, as a discipline and career, data science continues to expand and mature. Over the past four years, job postings for roles that include “data” in the title have grown 135%. Increasingly, employers are seeking candidates with strong technical competencies and the soft skills required to help introduce and embed data science techniques into traditional disciplines like manufacturing, energy and even medicine. In important ways, Data scientists are catalyzing transformation within their organizations, helping them realize significant value and competitive advantage, if done well.

Whether you’re an aspiring Data professional or already working in the field, read on for Rice University’s top 12 in-demand skills for today’s Data scientists:
Technical Skills
Advanced Math
Statistical Analysis and Modeling
Machine Learning and Modern Algorithmic Models
Big Data Analytics, Processing, and Storage
Data Visualization and Storytelling
Human Soft Skills

Scientific Method Expertise
Ethics and Integrity
Business Acumen
Executive-Level Communication

Technical Skills in Demand for Data Scientists
Fundamentally, technical (“hard”) skills and big data competencies are what set Data scientists apart from Data analytics, Business analytics and other data professionals. For example, machine learning, algorithms and programming languages are much more prevalent in Data scientist job postings compared to Data analyst or even Data engineer job postings.

Here are our 6 most important technical skills for data scientists:
Advanced Math
Advanced math and statistics (our skill #2) are at the top of the core data science skillset. A career in data science requires skills in applied math to effectively collect, organize, analyze, interpret and present data, form hypotheses, and create models. In terms of education, you’ll want to brush up on linear algebra, calculus, trigonometry, and boolean logic.

Statistical Analysis and Modeling
Data scientists are part statistician, part computer scientist–scrubbing and modelling big data to make predictions at scale about future behaviors, events and opportunities. No matter where you land on the statistical theory vs. applied statistics spectrum, there’s no denying that statistical and probabilistic methods are an essential part of hypothesis generation, data analysis and exploration.

Understanding Bayesian statistics, regressions, sampling methods, probability distributions, correlations, parametric/non-parametric tests, and other statistical concepts help data scientists assess, understand, and make predictions about people, customers and other types of data.

On the computation side, statistics and probability are also core to the efficiency analysis of algorithms and randomized algorithms. In algorithmic modelling, data scientists run simulations with real-world, massive data sets to make sure they’re asking the right questions, in ultimate pursuit of the right answers.

If you’re looking for an educational program to help you break into (or advance in) data science, look for a Data Science curriculum that covers these topics, as we do in the Rice Master of Data Science program’s COMP 680 and COMP 614 courses

Programming for Data Science
Programming skills help set Data scientists apart from Data analysts, Statisticians and Applied Math experts, enabling Data scientists to build computer programs and models that analyze behavior, prioritize resources, and predict unknowns. Job postings analytics reveal Python as the most requested programming language across the field of data science, with R in close second. SQL and SAS continue to be useful skills for most data professionals in querying databases as well.

Machine Learning and Modern Algorithmic Models
Algorithms, supervised and unsupervised, are at the heart of machine learning, another essential data science skill. For example, one of the most practical and commonplace applications of machine learning today is Natural Language Processing (NLP), which we teach in the Machine Learning specialization of Rice’s online master of data science program.

NLP is defined as a subset of AI and machine learning in which computers learn to analyze, interpret and autonomously derive meaning from human text language, whether written or spoken (video or audio transcripts). NLP is revolutionizing the the analysis, generation and transformation of text across a host of applications, from document analysis to language translation to speech recognition. For example, AI chatbots leverage NLP to automate responses to prompts.

 Big Data Analytics, Processing and Storage
“Big” data refers to datasets that are too large to be processed or analyzed on a typical personal computer, requiring powerful databases and cloud computing solutions. Data scientists and Data engineers must stay up-to-date on the software tools and frameworks (as one example, Apache Spark) used by practitioners of modern data science, as well as the statistical and analytical models that are employed in conjunction with those tools.

As more organizations move to cloud services for big data storage, Data Engineers, Data Architects and data professionals are upskilling on AWS, Microsoft Azure, and other cloud services providers.

 Data Visualization & Storytelling
Executives and leaders are investing to build data science competencies within their organizations because of the promise of using data to make more precise, evidence-based decisions across everything from investments to business operations, medicine to marketing. Therefore, presenting insights and recommendations in a visually-intuitive way is an imperative for Data scientists, Data analysts and other professionals. Data scientists must be skilled in creating visual frameworks that help others understand their methodologies, findings, and highest-value opportunities.

Data scientists know how to curate the right data visualization or tool for each situation. Common tools and approaches include but aren’t limited to: Python visualizations, geometric algorithms, Tableau, Matplotlib, Numpy, Plotly, pandas, vector quantization like K-means clustering, and more.

Valuable Human Soft Skills for Data Scientists
Having the technical ability to perform your job as a Data scientist is only half of the story. Effective Data scientists know how important soft skills like collaboration and communication are to ensuring data science practices are adopted within their organizations.

These 6 essential data science soft skills will help ensure success in your professional career.

One of the most essential data science soft skills is a natural curiosity about what’s true, what works and what is likely to happen in the future. Data scientists use big data to answer questions every day: urgent questions, interesting questions, or questions that have not yet been asked by anyone. Exploring, experimenting, analyzing and discovering will be second nature to a Data scientist.

Scientific Meth
Curiosity leads to the implementation of the scientific method, in which a hypothesis is stated and then proven or disproven through experimentation. A Data scientist is an expert in applying the scientific method systematically across every project to achieve increasing degrees of consistency and certainty through discipline.

Collaboration With Subject-Matter Experts
Often one member of a transformation team or initiative, a Data scientist needs the ability to work with people from various disciplines and backgrounds. Working with medical data, for example, would bring the Data scientist in close contact with doctors and researchers who may be more comfortable with medical concepts than with big data. Relationship building is paramount to building trust, whether with business partners or with other tech and data professionals like Data engineers or Software engineers.

 Ethics and Integrity
Today, data is among an organization’s most valuable and sensitive assets–-and extensive privacy laws and regulation exist to ensure it is handled with integrity. Data scientists must be trusted to maintain confidentiality and protect the security of information.

Data scientists must also be aware of how their machine learning models may be designed with inherent biases and strive for objectivity.




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