Applied Data Analytics
| Instructor | CEU Units | # of Lectures | Hours per Week | Tuition |
| Ravi Starzl | 4.8 | 12 | 8-10 | $2,700 |
Course Objectives
This course prepares students to become entry-level technical team players on data analysis projects, and trains them to use pre-built analytic libraries to answer business intelligence, regression, classification, and clustering problems using the R platform. Students will learn to program in the R environment, and also how to connect R to a cluster for large-scale data processing. Suitable for expert excel users who are not developers. No java programming required.
Upon course completion students will:
- Understand basic principles of data preparation and analysis
- Fluently program R scripts to conduct a wide range of analysis
- Develop techniques to hone in on the information need of your stakeholders
- Understand the application of machine learning techniques to various problems
- Understand how to use statistics to describe data and fulfill stakeholder information needs
Prerequisites
Familiarity with the unix/linux command line, a creative and inquisitive mind, and determination. Familiarity with basic statistical concepts or any prior machine learning experience will be helpful, but is not a prerequisite. This course will require a computer capable of installing and running the R computing environment, such as R studio available at: https://www.rstudio.com
Required Textbook
"An Introduction to Statistical Learning"
James, G., Witten, D., Hastie Tlk Tibshirani. R. 2013, XIV, 426p 150 illus. Available at: http://www-bcf.usc.edu/~gareth/ISL/
Topics
| Lecture 1: |
Introduction to Machine Learning and Data Analysis |
| Lecture 1-2: | Evaluating Information Needs and Conducting Regression Analysis |
| Lecture 3: | Structuring Problems, Data, and Features for Regression Analysis Evaluating Regression Models and Presenting the Results |
| Lecture 4-5: | Evaluating Information Needs and Conducting Classification Analysis |
| Lecture 6: | Structuring Problems, Data, and Features for Classification Analysis Evaluation of Classification Models and Presentation of Classification Results to Non-Technical Audiences |
| Lecture 7-8: | Evaluating Information Needs and Conducting Clustering Analysis |
| Lecture 9: | Structuring Problems, Data, and Features for Clustering Analysis Evaluation of Classification Models and Presentation of Clustering Results to Non-Technical Audiences |
| Lecture 10: | Conceptual Introduction to Big Data Systems and Analysis |
| Lecture 11-12: | Conduct Business Intelligence Analysis on AWS Redshift and Present Results to Non-Technical Audience |