Master’s in Data Science Engineering: What Will I Learn?
A typical master’s degree in data analytics engineering focuses on utilizing skills in constructing data pipelines, database management, software programming, and statistics to create efficient and effective ways to identify date-driven trends, and create computational models of data.
Focus areas will usually cover data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization.
Best Online Degree Programs:
A program fully focused on the engineering aspect is fairly new and only offered by a handful of universities in an online format:
Data Analytics Engineering vs. Data Analytics: What’s the difference?
The data engineer establishes the foundation that the data analysts and scientists build upon.
Data engineers typically have a software development skillset and often have to use complex tools and techniques to construct data pipelines and handle data at scale. The data constructed by the data engineer allows data analysts or scientist to focus on solving analytical problems as opposed to having to move data from point to point.
The following are examples of tasks that a data engineer might be working on:
Building APIs for data consumption.
Integrating external or new datasets into existing data pipelines.
Applying feature transformations for machine learning models on new data.
Continuously monitoring and testing the system to ensure optimized performance.
Data Analysts take data (harvested by the data engineer), and leverage it to answer questions and communicate the results to help make larger business decisions — aka data-driven decision making.
The core responsibility of a data analyst is to track progress and optimize focus using data trends. For example, how can the marketing department use data to optimize and launch their next campaign? How can a CEO better understand recent performance growth?
An underemphasized — but perhaps most important — skillset that data analysts must possess is the ability to translate and visualize past, present and future trending data via presentation and soft skills in order to make the data digestible for non-data stakeholders.