Tackle Real Data Challenges. Master computational, statistical, and informational data science in three courses.
proposé par
Spécialisation Data Science at Scale
University of Washington
About this Spécialisation
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau intermédiaire
Approx. 3 mois pour terminer
6 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Coréen...
Compétences que vous acquerrez
Python ProgrammingR ProgrammingMapreduceSQL
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau intermédiaire
Approx. 3 mois pour terminer
6 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Coréen...
How the Spécialisation Works
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.
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.
In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered.
You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to:
Learning Goals:
1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields.
2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models.
3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics
4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends.
5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages.
write programs in Spark
6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams...
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
Learning Goals: After completing this course, you will be able to:
1. Design effective experiments and analyze the results
2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation
3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants)
4. Explain and apply a set of unsupervised learning concepts and methods
5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection...
Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.
While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so.
Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations.
Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both.
Learning Goals: After completing this course, you will be able to:
1. Design and critique visualizations
2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science
3. Use cloud computing to analyze large datasets in a reproducible way....
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice....
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....
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.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Cette Spécialisation n'est pas associée à des crédits universitaires, mais certaines universités peuvent décider d'accepter des Certificats de Spécialisation pour des crédits. Vérifiez-le auprès de votre établissement pour en savoir plus.
How long does it take to complete the Data Science at Scale Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 5 months.
À quelle fréquence chaque cours de cette Spécialisation est-il proposé ?
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.
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.
Will I earn university credit for completing the Data Science at Scale Specialization?
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 Data Science at Scale Specialization?
You will have experience working independently on data science challenges, analyzing real data sources on and off the web, potentially at terabyte-scale. You will be poised to pursue deeper technical study in software systems, scalable algorithms, statistics, machine learning, and visualization.
What background knowledge is necessary?
Learners will need intermediate programming experience (roughly equivalent to two college courses) and some familiarity with databases. Programming assignments throughout the Specialization will use a combination of Python, SQL, Scala, R, and Javascript; familiarity with one or more of these languages will be helpful.
