Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills. Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.
Spezialisierung Python Data Products for Predictive Analytics
Build Predictive Systems with Accuracy. Collect, model, and deploy data-driven systems using Python and machine learning.
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Was Sie lernen werden
Discover how to transform data and make it suitable for data-driven predictive tasks
Understand how to compute basic statistics using real-world datasets of consumer activities, like product reviews and more
Use Python to create interactive data visualizations to make meaningful predictions and build simple demo systems
Perform simple regressions and classifications on datasets using machine learning libraries
Kompetenzen, die Sie erwerben
Specialization Highlights
Understand the Basics
Process and visualize data
Ilkay Altintas and Julian McAuley concisely breakdown the fundamental concepts behind data products in this easy-to-follow course.
Start Building
Design predictive models
Discover the fundamental concepts of statistical learning as you learn about the various methods of building predictive models.
Advance Your Skills
Improve your predictive models
Evaluate and compare classifiers and performance measures for use in different regression and classification scenarios.
Real-world Project
Deploy data products
Use the tools and techniques required to deploy working recommender systems on real-world datasets.
Kursteilnehmer, die sich für Specialization entscheiden, sind
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- Entrepreneurs
- Risk Managers
So funktioniert die Spezialisierung
Kurse absolvieren
Eine Coursera-Spezialisierung ist eine Reihe von Kursen, in denen Sie eine Kompetenz erwerben. Um zu beginnen, melden Sie sich direkt für die Spezialisierung an oder überprüfen Sie deren Kurse und wählen Sie denjenigen Kurs aus, mit dem Sie beginnen möchten. Wenn Sie einen Kurs abonnieren, der Bestandteil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung Es ist in Ordnung, wenn Sie nur einen Kurs absolvieren möchten — Sie können Ihren Lernprozess jederzeit unterbrechen oder Ihr Abonnement kündigen. Gehen Sie zu Ihrem Kursteilnehmer-Dashboard, um Ihre Kursanmeldungen und Ihren Fortschritt zu verfolgen.
Praxisprojekt
Jede Spezialisierung umfasst ein Praxisprojekt. Sie müssen das Projekt/die Projekte erfolgreich abschließen, um die Spezialisierung abzuschließen und Ihr Zertifikat zu erwerben. Wenn die Spezialisierung einen separaten Kurs für das Praxisprojekt umfasst, müssen Sie zunächst alle anderen Kurse abschließen, bevor Sie damit beginnen können.
Zertifikat erwerben
Wenn Sie alle Kurse und das Praxisprojekt abgeschlossen haben, erhalten Sie ein Zertifikat, dass Sie für potenzielle Arbeitgeber und Ihr berufliches Netzwerk freigeben können.
Es gibt 4 Kurse in dieser Spezialisierung
Basic Data Processing and Visualization
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.
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.
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.
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.
Dozenten
Julian McAuley
Assistant Professor
Ilkay Altintas
Chief Data Science OfficerÜber University of California San Diego
Häufig gestellte Fragen
Wie erfolgen Rückerstattungen?
Kann ich mich auch nur für einen Kurs anmelden?
Ja! Um loszulegen, klicken Sie auf die Kurskarte, die Sie interessiert, und melden Sie sich an. Sie können sich anmelden und den Kurs absolvieren, um ein teilbares Zertifikat zu erwerben, oder Sie können als Gast teilnehmen, um die Kursmaterialien gratis einzusehen. Wenn Sie einen Kurs abonnieren, der Teil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung. Auf Ihrem Kursteilnehmer-Dashboard können Sie Ihren Fortschritt verfolgen.
Ist finanzielle Unterstützung möglich?
Kann ich kostenlos an diesem Kurs teilnehmen?
Findet dieser Kurs wirklich ausschließlich online statt? Muss ich zu irgendwelchen Sitzungen persönlich erscheinen?
Dieser Kurs findet ausschließlich online statt, Sie müssen also zu keiner Sitzung persönlich erscheinen. Sie können jederzeit und überall über das Netz oder Ihr Mobilgerät auf Ihre Vorträge, Lektüren und Aufgaben zugreifen.
Wie lange dauert es, die Spezialisierung abzuschließen?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 4 to 6 months.
What background knowledge is necessary?
Learners should have a basic understanding of the Python programming language.
Do I need to take the courses in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
Erhalte ich akademische Leistungspunkte für den Abschluss der Spezialisierung?
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.
What will I be able to do upon completing the Python Data Products for Predictive Analytics Specialization?
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.
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