Thank you for signing up for the course! We look forward to working with you and hearing your feedback in our forums.
Need help getting started?
Read this article more about the medicare data
Read this article to learn more about additional outlier detection techniques
You can download Supplemental Materials, Lesson Videos and Transcripts from Downloadables (bottom right corner of the Classroom) or from the Dashboard (first option on the navigation bar on the left hand side).
Learn about the Question, Modeling, and Validation (QMV) process of data analysis. Understand the basics behind each step and apply the QMV process to analyze on how Udacity employees choose candies!
We will drill in on the questioning phase of the QMV process. We’ll teach you how to turn a vague question into a statistical one that can be analyzed with statistics and machine learning. You will also analyze a Twitter dataset and try to predict when a person will tweet next!
Building upon lesson 2, you will learn how to build rigorous mathematical, statistical, and machine learning models so you can make accurate predictions. You look through the recently released U.S. medicare dataset for anomalous transactions.
So how do you tell if your model is doing well? In this lesson, we will teach you some of the fundamental and important metrics that you can use to grade the performance of the models that you’ve build. You will analyze the AT&T connected cars data set and see if you can tell which driver is which by analyzing their driving patterns.
You will create a program that examines log data of net flow traffic, and produces a score, from 1 to 10, describing the degree to which the logs suggest a brute force attack is taking place on a server.
Follow this link to access the final project.
We would like to thank Cheng-Han Lee, Jeremy Silver, Lauren Castellano, Caroline Buckey, Calvin Hu, and Larry Madrigal for their help with this course.