About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approx. 4 months to complete

Suggested 5 hours/week

English

Subtitles: English

What you will learn

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    Discover how to transform data and make it suitable for data-driven predictive tasks

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    Understand how to compute basic statistics using real-world datasets of consumer activities, like product reviews and more

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    Use Python to create interactive data visualizations to make meaningful predictions and build simple demo systems

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    Perform simple regressions and classifications on datasets using machine learning libraries

Skills you will gain

Machine LearningPython ProgrammingPredictive AnalyticsData ProcessingData Visualization (DataViz)

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approx. 4 months to complete

Suggested 5 hours/week

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 4 Courses in this Specialization

Course1

Basic Data Processing and Visualization

4.7
20 ratings
2 reviews

This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

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Course2

Design Thinking and Predictive Analytics for Data Products

This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

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Course3

Meaningful Predictive Modeling

This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.

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Course4

Deploying Machine Learning Models

In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

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Instructors

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Julian McAuley

Assistant Professor
Computer Science
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Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

About University of California San Diego

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 4 to 6 months.

  • Learners should have a basic understanding of the Python programming language.

  • We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • After completing the Specialization, learners will have many of the skills needed to begin working as a Data Scientist, Senior Data Analyst, or Data Engineer. After completing this course, learners will be able to develop data strategies, create statistical models, devise data-driven workflows, and make meaningful predictions that can be used for a wide-range of business and research purposes. Learners will also understand how to use design thinking methodology and data science techniques to extract insights from a wide range of data sources.

More questions? Visit the Learner Help Center.