Специализация Наука о больших данных
Tackle Real Data Challenges. Master computational, statistical, and informational data science in three courses.
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Приобретаемые навыки
Специализация: общие сведения
Требуется релевантный опыт.
Требуется релевантный опыт.
Специализация включает несколько курсов: 4
Data Manipulation at Scale: Systems and Algorithms
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.
Practical Predictive Analytics: Models and Methods
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.
Communicating Data Science Results
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.
Data Science at Scale - Capstone Project
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.
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Вашингтонский университет
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.
Часто задаваемые вопросы
Какие правила возврата средств?
Можно ли зарегистрироваться только на один курс?
Можно ли получить финансовую помощь?
Могу ли я пройти курс бесплатно?
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
Получу ли я зачеты в университете за прохождение специализации?
Получу ли я зачеты в университете за прохождение специализации?
How often is each course in the Specialization offered?
Как часто предлагается каждый курс в специализации?
Will I earn university credit for completing the Data Science at Scale Specialization?
What will I be able to do upon completing the Data Science at Scale Specialization?
What background knowledge is necessary?
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