Data scientists analyze and interpret data, using machine learning and algorithms to inform business predictions and decisions.

Core skills:
Math modeling and statistics, programming, big data

Core tech stacks:
R, Scala, Python, data manipulation libraries — Numpy, Pandas, SciPy, Matplotlib, ML libraries (e.g. scikit-learn, TensorFlow, Keras)

Data analysts gather and interpret data. They use data analysis tools to come up with meaningful results and interpretations that are then used to make business decisions.

Core skills:
Data cleaning and visualization, statistics, programming, business domain

Core tech stacks:
Excel, SQL, ETL platforms (e.g. SSIS, Talend, Informatica), visualization tools (e.g. Tableau, PowerBI)

Data engineers prepare and manage big data for data scientists and analysts. Data engineers are responsible for designing, building and integrating data from multiple sources across multiple storages.

Core skills:
Programming, big data, cloud, DWH, system design

Core tech stacks:
Java, Python, SQL, cluster computing (e.g. Hadoop, MapReduce), data processing and streaming (e.g. Pig, Spark, Kafka), containers