Data scientists are some of the professionals whose demand is on the rise today. To be competent and deliver their work effectively, data scientists need to be well equipped with a host of skills, experiences, and knowledge on a wide range of areas. Research has shown that data scientists with good knowledge of general data skills as well as the specific data skills relating to their area of practice have a competitive edge in the market. Here are some crucial skills that every data scientist needs to have.


  1. Programming skills

Programming skills are the most basic and highly essential to a data scientist. These skills come in handy in ensuring that the scientist can comfortably use programming languages; the most common of which include the SQL, Python, and R. Programming skills are crucial as the data scientist can develop statistical programs to help make their work easier.


  1. Statistical skills

Data and statistics go hand-in-hand. To be effective in their work, data scientists need to have a wide knowledge of statistical analysis such as statistical tests, distribution, estimation, experimentation, and data-driven decision-making. Such skills make data scientists competent in the discharge of their work.


  1. Machine learning skills

Machine learning critically depends on data to ensure accuracy and consistency in the processes. Scientists, therefore, need to have basic skills relating to machine learning, especially how they can apply different methods such as random forest and k-nearest neighbors, to enable them to make sense of machine learning-mediated processes.


  1. Communication

Data scientists work within a given spectrum of workplace arrangement. They, therefore, need to be well competent in the discharge of their responsibilities in coordination with other persons. Communication skills ensure that such partnerships are well organized and that information is relayed as accurately, timely, and conveniently as possible.


  1. Artificial intelligence

Artificial intelligence largely bases its operations on accurate data. Just like in machine learning, data scientists need to have competence in the area of machine intelligence and how data can be applied to facilitate particular intelligence in decision-making. With the area of artificial intelligence quite wide, data scientists can specialize their knowledge and focus on particular areas of interest that the workplace demands.


  1. Data wrangling

The process of data wrangling involves the transformation of data from one form to another format. It can be quite complicated and demanding, especially in cases where the data is inconsistent in various ways such as missing values and incoherency. Data wrangling skills enable data scientists to appropriately format data in a form that can be meaningfully utilized.