5 in-demand technical skills for data scientists in 2022
by Analytics Insight
April 6, 2022
The field of data science is booming with the increasing transformation of industries. For data scientists to be impeccable in their work, they must commercialize crucial programming languages and develop strong communication and interpersonal skills. The growing use of data has also increased the need for talented data scientists.
In this video, we show you the 5 most sought after technical skills by recruitment experts in the industry.
Data scientists should have a solid understanding of statistics, probability, linear algebra, and multivariate calculus. Key concepts like mean, median, mode, maximum likelihood indicators, standard deviation, and distributions are key to understanding. As a data scientist, you will need to know Bayes’ theorem, probability distribution functions, central limit theorem, expected values, standard errors, random variables, and independence.
In the field of data science, Python is the gold standard. It is a versatile, object-oriented programming language that is easy to use in applications and websites, and has a thriving data science community, making it a popular choice among top IT companies. The majority of data scientists use Python on a daily basis, and it has overtaken R as the most popular data science language.
SQL, Spark, Hadoop, Hive, and Pig are examples of analytical technologies that can help you extract valuable insights from data and provide efficient frameworks for processing big data. In relational database management systems, SQL allows you to store, query, and modify data. Spark is a processing engine that works with large, unstructured information and is simple to combine with Hadoop. Hadoop is an open-source software framework from Apache Software Foundation for distributing massive data processing across a cluster of computing machines.
The more data a business manages, the more machine learning is likely to be part of its day-to-day operations. While not all data science jobs require deep learning, data engineering, or an understanding of natural language processing, if you want to process big data, you should be familiar with terminologies such as k-nearest neighbors, random forests, and ensemble techniques.
After collecting data from various sources, you will definitely come across sloppy data that needs cleaning. Data processing is based on coding languages and helps correct data defects such as missing information, string formatting, and date formatting.
Data scientists need to build a strong foundation in these areas. With growing demand, there is growing competition. Therefore, candidates must develop both their technical and non-technical skills.
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