机器学习分析 MasterTrack™ 证书

你将学到什么

课程概况

Advance your career with graduate level skills in data analytics, data science, and machine learning.

Data analytics and data science positions are growing exponentially across a variety of industries. With this online certificate program, you’ll study at the graduate level to gain the knowledge you need to advance in your career.

You’ll learn to apply mathematical theory and decision-making techniques that are vital to solving business problems through real-world projects designed by instructors from the University of Chicago. You’ll also benefit from graded instructor feedback and live sessions with groups of high-caliber peers.

By committing to online study for five months, you can earn the Machine Learning for Analytics MasterTrack Certificate and have the credentials in applied data science to land the job you want.

包含课程

Machine Learning: Statistical Thinking for Machine Learning

This course provides foundational knowledge in statistical thinking and introduces you 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.

Among other topics, you will learn about:

Sourcing, organizing, and managing data
Sampling, distributions, and the Central Limit Theorem
Measures of location and dispersion, correlation, and visualization
Statistical tests including t-tests, ANOVA, chi-squared, p-hacking, and multiple hypothesis testing
Bivariate OLS

Machine Learning: Advanced Statistical Thinking for Machine Learning

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.

Among other topics, you will learn about:

More sophisticated data management
Multivariate OLS
Identifying and managing outliers
Variable transformations
Measurement error
Probit and logit

Machine Learning: Introduction to Machine Learning

Get introduced 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.

Among other topics, you will learn about:

K-nearest neighbors
Least squares, lasso, and ridge regression
Loss, risk, and empirical risk minimization
Softmax and subgradient descent for softmax
SVMs
Generative models, LDA, and QDA
Clustering with K-means and hierarchical clustering

Machine Learning: 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.

Among other topics, you will learn about:

Dimensionality reduction and PCA
Boosting
Bagging
Decision trees and random forests
Neural networks and backpropagation
Image classification
Convolutional neural networks

面向人群

数据分析和数据科学专业人士

课程项目

Overview: Projects in the UChicago Machine Learning for Analytics Program

You will generate real insights using a proprietary dataset specifically gathered for this program about one of the largest income-generating operations in the United States: real-estate taxes in Cook County, Illinois, which is the second-most populous county in the United States. The data are derived from existing public records, FOIA requests, and APIs. You will use statistical and machine learning techniques to answer real-world business challenges, and the results from your work may even directly affect the models that the Cook County Assessor uses to assess properties.

Project 1: Critical Analytics and Linear Modeling

You will work with actual real estate data. After wrangling the data, you will focus on applying exploratory data analytical techniques. Using parametric statistical tests, you will create an operational linear model to predict the value of residential properties. During this project, you will learn about the importance and effects of different variables on real housing prices.

What you will learn

Working with University of Chicago Data Cases
Basic data wrangling techniques
Applying Python
Exploratory data analytics with actual, applied data
Parametric statistical tests
Linear regression modeling
Thinking critically about the data, and deriving and reporting insights

Project 2: Advanced Modeling and Classification

You will continue to apply what you learned on property value prediction, but you will take your work a few steps further. You will now receive additional data on properties from multiple sources, utilize more complex data preparation techniques, and apply more advanced regression procedures. You will also face a new important challenge: explore and then model the odds of individuals to appeal the assessed value of their homes. This challenge was one of the most hotly contested political issues in the past years in Chicago. To work on this part of the project, you will also get a new data set.

What you will learn

Handling missing values
Data restructuring and transformations
Regression diagnostics
Advanced linear regression
Binary classification with logistic regression
Presenting insights on complex data

Project 3: TBA

Project 4: Beat the Assessor

You will have the opportunity to apply the advanced modeling techniques that you have learned during the program to the same dataset that the data scientists at the Cook County Assessor use to create their models. Can you create a model that performs better? The team with the best model will have the opportunity to share their work with the lead data scientist at the Cook County Assessor.

预备知识

您应该具有与统计，基本编程（Python）和线性代数相关的本科教育或专业经验。