Why earn your MasterTrack® Certificate from the University of Chicago?

The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Its commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world. The University of Chicago is ranked #3 in the United States, and that high quality and rigor is a trademark of our MScA program. In all we do, we are driven to dig deeper, push further, and ask bigger questions—and to leverage our knowledge to enrich all human life.

How do I turn my MasterTrack® Certificate into university credit?

Once you have completed your MasterTrack coursework, you may apply to the MScA degree from the University of Chicago. If admitted, your MasterTrack Certificate will equate to the first two classes in the program (approximately 18% of course requirements), allowing you to get a headstart on your degree.

Are additional materials required to complete this program?

No. The University of Chicago will provide all learning materials directly in the courses.

When will I have access to the lectures and assignments?

You will have access to your onboarding course and all available course content immediately after enrolling and paying. Once the first course officially begins, you’ll be able to ask questions from the teaching team, get feedback on your projects, and work with fellow peers on group assignments.

What will I get if I pay for the program?

You will get access to all MasterTrack courses. You will also get feedback from the teaching team on your projects. You’ll work with an elite network of peers towards your goals.

Can I take this program for free?

No. MasterTrack® courses are available only to learners who have paid for the program.

What is the refund policy?

You will have two weeks after the start of the first course session to get a refund on your payment.

Is financial assistance available?

Financial assistance is not available at this time for MasterTrack courses.

Who can I contact if I have questions about enrolling?

Contact a program advisor at online@uchicago.edu.

MasterTrack® Certificate

Machine Learning for Analytics

In this 4-course program, you'll develop skills in statistics and machine learning by practicing on real-world data and real-world applications. Advance your career as a data science professional.

The next cohort will be announced soon.

Stay tuned for updates.

7 months

8-10 hours per week

$4000

In 4 installments of $1000 or pay all at once to save $100

100% online

+Live session classes

No application necessary - enroll today

Ranked the #3 Best College for Statistics in the U.S

According to College Factual

Accelerate your path to a degree

Accelerate your progress toward the part-time Master of Science in Analytics degree from The University of Chicago.

Live sessions

Interact with TAs, instructors, and peers to help build your professional network.

Program description

Approach machine learning in a critical way and use it to solve real business problems.

Required background

A bachelor’s degree is required to enroll in this program. The program will build on any professional experience you have related to statistics, basic programming (Python), and linear algebra.

Skills you will gain

Predictive Modeling
Machine Learning
Python Programming
Logistic Regression
Multinomial Logit
Regression Trees
Univariate and Multivariate Statistical Analysis
Feature Engineering

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.

4 courses in this 7 month program

Course 1 of 4

Statistical Thinking for Machine Learning

What you will learn

Sourcing, organizing, and managing data
Probability and distributions
Descriptive analytics
Statistical tests and causation
Modeling techniques
Manipulating data to prepare it for analysis

Project(s)

Critical Analytics and Linear Modeling

Throughout, you will work on a proprietary dataset developed from public and private data about property values in Chicago, including data from the Cook County Assessor’s office.

As you will learn during this course, there are high stakes involved in predicting property values in Cook County. Billions are generated from property taxes each year, but this has not always been done in the most accurate and equitable way. In the first project, you will use the skills and tools that you have developed to create a model that predicts home values in Cook County.

Overview

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 of 4

Advanced Statistical Thinking for Machine Learning

What you will learn

Multivariate linear regressions
Variable transformations
Binary classification
Working with data and outliers
Experiment design and natural experiments

Project(s)

Advanced Modeling and Classification

Building on your work with property value prediction you will now receive additional data on properties from multiple sources, utilize more complex data preparation techniques, and apply more advanced regression procedures.

This time, you will face a new challenge: how to model the odds of individuals appealing the assessed value of their homes. This will entail trying to understand whether there are statistically significant relationships between appeals and factors like education, wealth, and race. This challenge was one of the most hotly-contested political issues in the past years in Chicago.

Overview

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 of 4

Introduction to Machine Learning

What you will learn

Principles and process of machine learning
Different categories of machine learning
Statistical approaches to regression
Types of regularization to improve models
Multi-class classification and SVMs
Generative models

Project(s)

Machine-Learning Models for Prediction

This project focuses on combining statistical techniques with new approaches from machine learning.

Your goal in this project is to make predictions. You will work from a larger, enhanced dataset that now contains new information, such as the number of stores within a certain radius of a property.

You are expected to test and fine tune your models to optimize their performance on new data so that you can predict whether a homeowner will appeal and estimate property values.

Overview

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 of 4

Advanced Applications

What you will learn

Building decision trees from scratch
Neural networks
Implementing unsupervised learning techniques
Dimensionality reduction
Tools to better address machine-learning tasks

Project(s)

Beat the Assessor

In this project 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. You will be given a technique to measure how effective your predictions are, and you will be tasked with creating a modeling technique whose performance, by that metric, exceeds the Cook County Assessor’s.

Overview

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.

Earn credit toward a part-time master’s degree from The University of Chicago

Enrolling in a MasterTrack® Certificate program means you can start learning job-relevant skills right away and give yourself a pathway to a full degree program.

MasterTrack® Certificate

Machine Learning for Analytics

Machine Learning for Analytics MasterTrack® Certificate accelerates your path toward:

Part-time Master of Science in Analytics

If you apply and gain admission to the part-time Master of Science in Analytics offered by The University of Chicago, credits earned from your MasterTrack® Certificate program will carry over as you pursue your full degree. Upon successful completion of your MasterTrack® program, you will receive an official University of Chicago certificate, regardless of whether you elect to enroll in the full master’s program.

Instructors

Frequently asked questions

The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Its commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world. The University of Chicago is ranked #3 in the United States, and that high quality and rigor is a trademark of our MScA program. In all we do, we are driven to dig deeper, push further, and ask bigger questions—and to leverage our knowledge to enrich all human life. ‎

More questions? Visit the Learner Help Center.

Coursera does not grant academic credit; the decision to grant, accept, or recognize academic credit, and the process for awarding such credit, is at the sole discretion of the academic institutions offering the MasterTrack® Certificate program and/or other institutions that have determined that completion of the program may be worthy of academic credit. Completion of a MasterTrack® Certificate program does not guarantee admission into the full Master’s program referenced herein, or any other degree program.

Machine Learning for Analytics

The next cohort will be announced soon.

7 months

$4000

100% online

No application necessary - enroll today

Ranked the #3 Best College for Statistics in the U.S

Accelerate your path to a degree

Live sessions

Program description

Required background

Skills you will gain

Overview

4 courses in this 7 month program

Earn credit toward a part-time master’s degree from The University of Chicago

Instructors

Frequently asked questions

Why earn your MasterTrack® Certificate from the University of Chicago?‎

How do I turn my MasterTrack® Certificate into university credit?‎

Are additional materials required to complete this program?‎

When will I have access to the lectures and assignments? ‎

What will I get if I pay for the program? ‎

Can I take this program for free?‎

What is the refund policy? ‎

Is financial assistance available?‎

Who can I contact if I have questions about enrolling?‎

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