14 most in-demand data science skills you need to succeed
The demand for data scientists continues to grow, but the job requires a combination of technical and soft skills. Here are 14 key skills for effective data scientists.
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 they need talented analytics professionals who have specific skills in scientific methods, statistical approaches, data analysis and other data-centric methodologies -- or, more simply, data science.
The field of data science focuses on uncovering information and insights in large amounts of both structured data and unstructured data. It enables data-driven organizations to get answers to business questions, spot trends and make informed predictions.
Data science work is typically done 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 various technical capabilities. But data scientists also need soft skills -- personality traits and characteristics that can help them achieve the desired outcomes and bridge the gap with business executives and workers on technology and data analysis. Let's look more closely at these key data science skills in both categories.
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.
1. Statistics
Because 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, standard deviation, variance and other elements of statistics helps data scientists collect, organize, analyze, interpret and present data. That better enables them to work with the data to find useful results.
2. Multivariable calculus and linear algebra
Being able to apply mathematical concepts to understand and optimize the fitting functions that match a model to a data set is incredibly important. Otherwise, the model won't make accurate predictions. Additionally, data scientists should be versed in using dimensionality reduction to simplify complicated analysis problems involving high-dimensional data. Calculus and algebra skills are also a must in machine learning -- for example, to train an artificial neural network on large volumes of data.
3. Programming and coding
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 of programming and writing code. Python is the most popular programming language among data scientists by a wide margin. In a 2020 survey done by Google's Kaggle subsidiary, which runs an online data science community, more than 80% of the 2,675 respondents who identified themselves as working data scientists said they use Python. Second on the list was SQL, at just over 40% usage. R is another popular language for data science applications and projects, particularly statistical computing and graphics uses. Other programming languages that data scientists often use include C and C++, Java and Julia.
4. 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 sets to forecast future events, behavior and results; it can be applied to various use cases in different industries, such as customer analytics, equipment maintenance and medical diagnosis. The potential uses and benefits make predictive modeling a highly valued skill for data scientists.
5. Machine learning and deep learning
While data scientists don't necessarily need to work with AI technologies, they're increasingly being hired by companies to implement machine learning applications. Doing so requires someone who can train machine learning algorithms to learn about data sets and then look for patterns, anomalies or insights that can be used to build analytical models. 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, a more advanced method that uses neural networks to create complex analytical models, particularly help data scientists stand out. So does knowledge of different types of algorithms, including the following:
- decision trees;
- random forests;
- Naïve Bayes classifiers;
- k-nearest neighbor;
- logistic regression;
- linear regression; and
- k-means clustering.
6. Data wrangling and preparation
Data scientists often say that more than 80% of the time they spend on data science projects is 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 enables them to deal with data quality problems and imperfections in data sets, such as missing or mislabeled fields and formatting issues. Data wrangling skills also involve collecting data from multiple sources and massaging different data formats, as well as doing data manipulation work to filter, transform and augment data for analytics applications. To aid in those efforts, data scientists should be familiar with using common data warehouse and data lake environments, including both relational and NoSQL databases and big data platforms such as Apache Spark and Hadoop.
7. Model deployment and production
Data scientists spend the majority of their time building and deploying models. They need to be able to select the right 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.
8. Data visualization
Especially when working with sets of big data that are large and contain different data types, being able to effectively visualize data when presenting analytics results 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 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 data visualization tools that are available to help with the process. They should also learn how to create different types of data visualizations: line, bar and pie charts; histograms; bubble charts; heat maps; scatter plots; and more.
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. Continuing the overall list of skills, these six soft ones are part of the makeup of a well-rounded data scientist.
9. Business knowledge
At many organizations, data science teams fall under a line of business, rather than being in IT or a centralized analytics group. 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 the company should use its data and know which analytics problems to prioritize.
10. 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 data science process, they need to have a keen mind for problem-solving to figure out how various pieces fit into the equation and determine what data should be included or left out, among other tasks.
11. Curiosity
Being curious, asking questions and having a desire to continually learn are must-have skills for a data scientist. Curious minds are able to sift through large amounts of data to find answers and insights. Data itself constantly changes, so data scientists shouldn't be complacent about how they approach data or limit themselves to the current conclusions they've derived from the data.
12. Critical thinking
Critical thinking skills are also crucial. Data scientists need to be able to assess data sets and analytics results to form judgments about their validity and relevance. Looking at data with a skeptical eye helps data scientists reach accurate and unbiased conclusions.
13. 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.
14. 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 someone can fully learn 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 on a career in data science, 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. For those 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 shortage of experienced data scientists continues, the need for well-trained ones will only continue to increase.
