Match the data professional role with the skill sets required.
- Data scientist – Ability to use statistical and analytical skills, programming knowledge (Python, R, Java), and familiarity with Hadoop; a collection of open-source software utilities that facilitates working with massive amounts of data.
- Data engineer – Ability to understand the architecture and distribution of data acquisition and storage, multiple programming languages (including Python and Java), and knowledge of SQL database design including an understanding of creating and monitoring machine learning models.
- Data analyst – Ability to understand basic statistical principles, cleaning different types of data, data visualization, and exploratory data analysis.
Answers Explanation & Hints:
Data Scientist: A data scientist is responsible for designing and implementing complex analytical projects to extract insights from data. They must have a strong background in statistics and computer science, and possess skills in data cleaning, data exploration, data visualization, machine learning, and programming in languages such as Python, R, or Java.
Data Engineer: A data engineer is responsible for developing, constructing, testing, and maintaining the data architecture and systems required for processing and managing large amounts of data. They should have expertise in data acquisition, storage, and processing, with knowledge of SQL database design and big data technologies such as Hadoop, Spark, or NoSQL databases.
Data Analyst: A data analyst is responsible for exploring and analyzing data to help organizations make informed decisions. They must have a strong understanding of statistical principles, data cleaning, data visualization, and programming languages such as Python or R. Data analysts should also be able to use data querying tools such as SQL and work with data visualization tools like Tableau or Power BI.