Advanced Data Science tackles big data at scale — Spark, Hadoop, Kafka, and cloud analytics for managing and analyzing large datasets.
What DATA 445 covers
Prerequisites: DATA 335 and DATA 430. A project-based introduction to the concepts, approaches, techniques, and technologies for managing and analyzing large data sets in support of improved decision-making. Activities include using technologies such as Spark, Hive, Pig, Kafka, Hadoop, HBase, Flume, Cassandra, cloud analytics, container architectures, and streaming real-time platforms.
Discussion covers how to identify the kinds of analyses to use with big data and how to interpret the results.
Typical DATA 445 assignments
Expect a project requiring you to apply a specific big data technology (Spark, Hadoop, Kafka) to analyze a large dataset and interpret the results.
Key topics in DATA 445
- Big data technologies (Spark, Hadoop, Kafka)
- Cloud analytics and container architectures
- Streaming real-time platforms
- Big data analysis selection and interpretation
Writing tips for DATA 445
Follow the assignment instructions and rubric line by line
UMGC assignments for DATA 445 are graded against a specific rubric or grading criteria your instructor provides — every requirement has to be visibly addressed. Skipping a requirement because it seems minor is one of the most common reasons a strong submission loses points.
Show your data work, not just the final numbers
Data Analytics courses like DATA 445 usually grade the actual analytical process — data cleaning, the code or queries used, and the reasoning behind method choices — not just a polished chart or summary statistic at the end.
Ground your work in a specific scenario, dataset, or organization
Strong submissions in this discipline are grounded in a specific, named scenario — a particular organization's policy gap, or a particular dataset's patterns — rather than discussing concepts generically. Evaluators check whether your conclusions are actually supported by the specific case given.
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Why students seek help with DATA 445
Students sometimes select a big data technology without justifying why it fits the specific data volume/velocity scenario given — the rubric typically wants that technology-selection justification shown, not a tool used arbitrarily.
How GradeEssays helps with DATA 445
Share your big data project and rubric, and your writer will help justify and apply the specific big data technology best suited to your scenario.
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Place Your Order View All ServicesPrerequisites and course context
DATA 445 requires both Data Visualization (DATA 335) and Foundations of Machine Learning (DATA 430). It is itself a required prerequisite for the DATA 495 capstone.
Related courses
Frequently asked questions
DATA 445 requires both Data Visualization (DATA 335) and Foundations of Machine Learning (DATA 430), and is itself a required prerequisite for the DATA 495 capstone.
A wide range — Spark, Hive, Pig, Kafka, Hadoop, HBase, Flume, Cassandra, cloud analytics, container architectures, and streaming real-time platforms — used to manage and analyze large datasets.