Data Analytics I: Data Sourcing, Feature Engineering and Preprocessing

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.

Who Is This Course For? 

for those considering to start a career in Data Analytics field

What Will You Learn? 
perform a data quality assessment.
perform feature aggregation and sampling for analytical efficiency
cleaning data

Cynthia V. Marcello

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.

5 weeks
Data Analytics Bootcamp

Course Schedule:

Starting Wednesday, January 20, 2021 - 6:00pm
146 3rd Street
Newburgh, NY 12550
This course is part of a bootcamp: Data Analytics Bootcamp
All class dates:
Jan 20, Jan 27, Feb 3, Feb 10, Feb 17