As companies continue to grow their data assets, the need to extract meaningful information -- and business value -- from that data is becoming increasingly important. Analyzing and gleaning insights from data requires a different skill set than simply storing and managing it. Many organizations are quickly realizing that they need talented analytics professionals who have specific skills in scientific methods, statistical approaches, data analysis and other data-centric methodologies.
Emerging a little over a decade ago, the field of data science focuses on uncovering information and insights in large amounts of both structured data and unstructured data. It enables organizations to get answers to business questions, spot trends and make intelligent predictions based on analysis of their data.
Data science work is typically performed by data scientists. With backgrounds in mathematics, statistics, data mining, advanced analytics, algorithms and now machine learning and AI, data scientists can gain a comprehensive understanding of data and apply their skills to find relevant analytics results.
For prospective data scientists, and organizations looking to hire them, the critical skills they need to do their jobs effectively include both technical capabilities and soft skills -- personality traits and characteristics that can help them achieve the desired outcomes and bridge the gap between technologists and business executives and workers. Let's look more closely at these key data science skills.
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Data science technical skills
In order for data scientists to ask the right questions, develop good analytical models and successfully analyze the findings, they must have a variety of "hard skills" that require specific training and education. Here are eight technical skills that data scientists typically need.
Statistics. Since data scientists regularly apply statistical concepts and techniques, it should come as no surprise that it's important for them to have a good understanding of statistics. Being familiar with statistical analysis, distribution curves, probability and other elements of statistics helps data scientists collect, organize, analyze, interpret and present data -- better enabling them to work with the data to find useful results.
Calculus and linear algebra. Being able to apply mathematical concepts to understand and optimize fitting functions for matching a model to a data set is incredibly important to getting accurate predictions from the model. Additionally, data scientists should be versed in using dimensionality reduction to simplify complicated analysis problems involving high-dimensional data. These skills are also important in machine learning -- for example, to train an artificial neural network on large volumes of data.
Relevant coding skills. Many data scientists learn programming out of necessity. They typically aren't coding masters and usually don't have a degree in computer science, but they are familiar with the basics. Popular programming skills for data scientists include knowledge of the Python, R, SQL and Julia languages.
Predictive modeling. Being able to use data to make predictions and model different scenarios and outcomes is a central part of data science. Predictive analytics looks for patterns in existing or new data to forecast future events, behavior and results; it can be applied to various use cases in different industries. As a result, predictive modeling skills are heavily used by data scientists.
Machine learning and deep learning. While data scientists don't necessarily need to work with AI technologies, they're increasingly being hired by companies looking to implement machine learning applications, in which they train algorithms to learn about data sets and then look for patterns, anomalies or insights in the data. As a result, demand is on the rise for data scientists who are skilled in the supervised, unsupervised and reinforcement learning methods used in machine learning. Skills in deep learning, which uses neural networks to create complex analytical models, particularly help data scientists stand out.
Data wrangling. Over 80% of the time spent on data science projects is often devoted to wrangling and preparing data for analysis. While most of the data preparation tasks fall on data engineers, data scientists can benefit from being able to do basic data profiling, cleansing and modeling tasks. That allows them to be able to deal with imperfections in data, such as missing fields, mislabeled fields or formatting issues. Data wrangling skills also involve collecting data from multiple sources and massaging data formats to work with the required algorithms.
Model deployment and production. Data scientists spend the majority of their time building and deploying models. They need to be able to select the correct algorithm and then use training data for supervised learning approaches or run the algorithm to automatically find clusters or patterns in unsupervised learning ones. Once a model produces the desired results, data scientists, often working with data engineers, must deploy it in a production environment to help their organizations make practical business decisions on an ongoing basis.
Data visualization. Especially when working with sets of big data that are large and contain different data types, being able to present analytics results in a visually appealing format is another important data science skill. Data scientists must have the ability to use data storytelling to highlight and explain the insights they've generated, and data visualization is a core way that they communicate those insights to business executives and other stakeholders. As a result, they should master the use of Tableau, D3.js or various other visualization tools that are widely available to help with the process.
Nontechnical and soft skills
In addition to technical skills, it's just as important for data scientists to possess a set of soft skills. As mentioned above, many data scientists need to be able to translate analytics findings and report on them to their business colleagues. Additionally, certain innate traits help them look at large pools of data with an inquiring mind, form analytics hypotheses and find gems of knowledge hidden in the data. These six soft skills are part of the makeup of a well-rounded data scientist.
Business knowledge. At many organizations, data science teams fall under a line of business, rather than being in IT or a centralized analytics group. And even if that isn't the case, their work still focuses on business issues. As such, data scientists need to have a strong understanding of the business and the industry it's in. This helps them to ask better data analysis questions, identify new ways that the company should use its data and know which analytics problems to prioritize.
Problem solving. Data scientists are often asked to find information needles in very large data haystacks. To do so, they come up with a hypothesis related to a business opportunity or problem and then try to validate it by analyzing the data. As they work through the analytics process, they need to have a keen mind for problem solving to figure out how various pieces fit into the equation and determine which data should be included or left out, among other tasks.
Curiosity. Being curious, asking questions and having a desire to continually learn are important skills to possess as a data scientist. Curious minds are able to sift through large amounts of data to find answers and insights. Data itself constantly changes, so it's important not to be complacent in the ways you approach data or limit yourself to the current conclusions derived from the data.
Critical thinking. Critical thinking skills are also crucial for data scientists. They need to be able to assess data sets, analytics results and various additional information to form judgments about the validity and relevance. Looking at data with a skeptical eye helps data scientists reach accurate and unbiased conclusions.
Communication. Data scientists who work with data on a daily basis understand it, and its nuances and intricacies, better than anyone else. The same, of course, goes for the findings they produce as part of data science applications. They need to be able to successfully communicate their understanding of the data and explain the analytics results so business executives and workers can use the information to make good decisions.
Collaboration. Being able to work as part of a larger team is important, too. Data scientists often need to collaborate with each other and with data analysts, business leaders, subject matter experts, data engineers and other people in an organization.
Learning resources for data scientists
Because of the many technical skills that are required, data science isn't a field that one can master in just a few weeks or through casual online courses, code academies and bootcamps. Usually, data scientists have various academic degrees and certifications, and they partake in continuous learning to stay up to date on the latest data science techniques and tools. However, for those looking to get started, an increasing number of resources and opportunities are now available.
Many universities offer degrees in data science at both the undergraduate and graduate levels. Additionally, various online courses and other learning resources are available through websites such as Coursera and Udemy. If you're looking to learn the fundamentals or basics of data science, many analytics software vendors and traditional code academy programs have also set up specific data science training courses.
And now is a good time to take advantage of those resources. As more and more companies look to hire people with data science skills, and the talent crunch in this field continues, the need for well-trained data scientists and other analytics professionals will only continue to increase.