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:
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.