Corspedia | Machine Learning for Analytics

Overview

The curriculum in this program was developed to support learners who want to take advantage of exponential job growth across industries for data analysts and scientists. In this online program, you’ll learn to apply mathematical theory and decision-making techniques that are vital to solving business problems and you’ll work with proprietary datasets developed at the University of Chicago to gain real-world insights and make predictions to inform business decisions in a wide range of industries.

Through hands-on projects designed by expert faculty at the Graham School of Continuing Liberal and Professional Studies, you’ll learn to apply mathematical theory and decision-making techniques that are vital to solving business problems. You’ll develop skills in statistics and machine learning by practicing on real-world data and real-world applications, including predicting property values using actual real estate tax data derived from one of the most populous counties in the U.S., all while benefiting from graded instructor feedback and peer collaboration.

Syllabus

Course 1: Statistical Thinking for Machine Learning
- In this course, you’ll build a foundational knowledge in statistical thinking and get introduced to thinking critically about data analytics. You’ll begin working on a case study developed at the University of Chicago in which you’ll use a proprietary dataset to make real-world insights using statistical techniques.

Course 2: Advanced Statistical Thinking for Machine Learning
- In this course, you will learn to work with more sophisticated datasets and interpret them using more advanced statistical techniques, such as multivariate OLS, transforming variables, and advanced binary classification.

Course 3: Introduction to Machine Learning
- This course will introduce you to Machine Learning as a discipline and build on the statistical techniques that you have learned. You’ll also understand machine learning as a discipline with its own mode of thinking where practitioners train models to create predictions that are used in a growing number of analytical applications. This and the next Machine Learning course take the same approach: teaching you how an algorithm works under the hood--often times by showing you how to code it from scratch--and then introducing you to tools that will apply them effectively and efficiently.

Course 4: Advanced Applications
- In this final course, you’ll learn additional machine learning techniques. You’ll explore how to fine tune the models that you have been creating, more advanced ways to manipulate your data, and more sophisticated approaches, such as ensemble methods.

Machine Learning for Analytics

Brought by: Coursera

Overview

Syllabus

Machine Learning for Analytics

Brought by: Coursera