Data scientists need various skills at work, such as:
Programming skills: They need to know how to use statistical programming languages like Java, R, Python, and Structured Query Language (SQL).
Statistical skills: All data scientists should have a working knowledge of statistics. They should be familiar with statistical tests, distribution, and estimation. They need to use statistics to validate techniques and evaluate experiments.
ML skills: Working with both structured and unstructured data and using ML can help them accomplish tasks with ease. Familiarity with k-nearest neighbors (data classification approach depending on the nearest data points), ensemble methods (classifying data points that improve prediction), and random forests (method of classification using decision trees) would be useful for understanding key points.
Mathematical skills: Mathematical concepts help improve algorithm optimization strategies that can bring about profitable wins for organizations. Calculus and algebra form the basis of algorithmic processes, especially for those who want to build in-house implementations.
Data wrangling skills: Data scientists need to map and transform data from raw into a specific format to make it more useful for analytics, particularly when the dataset has imperfections.
Data visualization skills: These are incredibly important when it comes to making data-driven decisions. Data scientists need to make sure that they can make sense of the data they are working on. They must be familiar with visualization tools such as ggplot, matplotlib, d3.js, or tableau.
Software engineering skills: A strong background in software engineering can help data scientists handle data logging and develop products for data acquisition.