In this course, students will learn the various methods used to source and load data sets selected for analysis. The features and attributes of data will be reviewed after a data quality assessment has been conducted. Using a variety of data cleaning techniques, students will normalize the data in preparation for feature aggregation, sampling, dimensionality reduction and one hot encoding.
At the end of the course, student will know:
1. How to perform a data quality assessment.
2. How to clean a data set in preparation for data analysis.
3. How to identify different statistical data types ideal for different analytical algorithms.
4. How to perform feature aggregation and sampling for analytical efficiency.
Consider 3-hour lecture per week, and 4-6 hours per week on quizzes and ongoing real-world projects for homework.
for those considering to start a career in Data Analytics field
Cynthia V. Marcello, DM is a tech solopreneur, software applications developer, data scientist, and business management consultant with 25 years of experience working with small-to-medium businesses, corporations, government, and educational agencies. She has developed data-driven applications for the Department of Defense (logistics), and numerous custom software applications (front-end and back-end) for operations and enterprise materials handling processes of corporations, and order-entry and production systems for online retailers.