Proficiency in programming languages such as Python or R is essential for data scientists. Python is widely used in the field of data science due to its versatility, extensive libraries (e.g., NumPy, pandas, scikit-learn), and readability.
Data scientists need a strong foundation in statistics and mathematics to analyze data, build models, and draw meaningful insights.
Machine learning is a core component of data science, involving algorithms and techniques for building predictive models and extracting patterns from data.
Data wrangling involves cleaning, transforming, and preparing raw data for analysis, which often accounts for a significant portion of the data science workflow.
Domain knowledge refers to expertise in a specific industry or field, such as finance, healthcare, marketing, or e-commerce. Data scientists need to understand the context and nuances of the data they are working with to develop relevant solutions and insights.