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 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.
Spécialisation Python Data Products for Predictive Analytics
Build Predictive Systems with Accuracy. Collect, model, and deploy data-driven systems using Python and machine learning.
proposé par
À propos de ce Spécialisation
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Machine LearningPython ProgrammingPredictive AnalyticsData ProcessingData Visualization (DataViz)
Fonctionnement du Spécialisation
Suivez les cours
Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.
Projet pratique
Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.
Obtenir un Certificat
Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.
Cette Spécialisation compte 4 cours
Cours1
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.
Cours2
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.
Cours3
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.
Cours4
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.
Enseignants
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
À propos de Université de Californie à 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.
Foire Aux Questions
Quelle est la politique de remboursement ?
Puis-je m'inscrire à un seul cours ?
Oui ! Pour commencer, cliquez sur la carte du cours qui vous intéresse et inscrivez-vous. Vous pouvez vous inscrire et terminer le cours pour obtenir un Certificat partageable, ou vous pouvez accéder au cours en auditeur libre afin d'en visualiser gratuitement le contenu. Si vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Visitez votre tableau de bord d'étudiant(e) pour suivre vos progrès.
Une aide financière est-elle possible ?
Puis-je suivre le cours gratuitement ?
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Ce cours est entièrement en ligne : vous n'avez donc pas besoin de vous présenter physiquement dans une salle de classe. Vous pouvez accéder à vos vidéos de cours, lectures et devoirs en tout temps et en tout lieu, par l'intermédiaire du Web ou de votre appareil mobile.
Quelle est la durée nécessaire pour terminer la Spécialisation ?
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.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
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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