Data Science at Scale Specialization [10426]


Tackle Real Data Challenges

Master computational, statistical, and informational data science in three courses.

About This Specialization

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.

Created by:


courses
4 courses

Follow the suggested order or choose your own.

projects
Projects

Designed to help you practice and apply the skills you learn.

certificates
Certificates

Highlight your new skills on your resume or LinkedIn.

Courses
Intermediate Specialization.
Some related experience required.
  1. COURSE 1

    Data Manipulation at Scale: Systems and Algorithms

    Upcoming session: Mar 13 — Apr 17.
    Commitment
    4 weeks of study, 6-8 hours/week
    Subtitles
    English

    About the Course

    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
    Show or hide details about course Data Manipulation at Scale: Systems and Algorithms
  2. COURSE 2

    Practical Predictive Analytics: Models and Methods

    Upcoming session: Mar 13 — Apr 17.
    Commitment
    4 weeks of study, 6-8 hours/week
    Subtitles
    English

    About the Course

    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
    Show or hide details about course Practical Predictive Analytics: Models and Methods
  3. COURSE 3

    Communicating Data Science Results

    Upcoming session: Mar 13 — Apr 10.
    Subtitles
    English

    About the Course

    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.
    Show or hide details about course Communicating Data Science Results
  4. COURSE 4

    Data Science at Scale - Capstone Project

    Upcoming session: May 8 — Jun 26.
    Commitment
    6 weeks of study, 3-4 hours/week
    Subtitles
    English

    About the 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.
    Show or hide details about course Data Science at Scale - Capstone Project

Creators

  • University of Washington

    The University of Washington is a national and international leader in the core fields that are driving data science: computer science, statistics, human-centered design, and applied math.

    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.

  • Bill Howe

    Bill Howe

    Director of Research

 

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Новини

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12:12 - В сентябре “Альфа-Банк” Михаила Фридмана окончательно поглотит “Укрсоцбанк”
12:12 - Тещу нардепа-партнера Коломойского, которая сбила женщину с ребенком, отправили под ночной домашний арест
11:18 - Борис Колесников снова хочет стать “кондитером номер два” в Украине
11:18 - Прибыль “Укргаздобычи” за полгода превысила 16 млрд гривен
10:24 - JP Morgan улучшил прогноз роста ВВП Украины до 4,3%
10:24 - В украинских судах рассматривается более 270 дел, связанных с ПриватБанком, в которых фигурирует НБУ
09:30 - Марионетка Виктора Пинчука облегчила кандидату Игоря Коломойского путь к месту второго вице-спикера
09:06 - Привітання Прем’єр-міністра України Володимира Гройсмана з Днем Незалежності України
08:36 - НБУ разрешил “Укрпоште” переоформить валютную лицензию по упрощенной процедуре
08:36 - Поддельной гривны стало меньше
20:00 - Буковинські фіскали розпочали активну фазу операції «Урожай - 2019»
20:00 - Харків’яни поповнили бюджет на 636,4 млн. гривень військового збору
20:00 - У ДФС відзначили День Державного Прапора та День Незалежності України
20:00 - Привітання в.о. Голови ДФС Дениса Гутенка з Днем Державного Прапора та Днем Незалежності України
19:36 - Фінансовий план ДП «Морський торговельний порт Усть-Дунайськ» на 2019 рік (зміни)
19:36 - Фінансовий план ДП «Морський торговельний порт Усть-Дунайськ» на 2020 рік
19:36 - Повідомлення про оприлюднення проєкту наказу Міністерства інфраструктури України
19:06 - Участники полумертвого фондового рынка Украины по ночам обсуждают, как его интересней закопать
19:06 - “Укрэнерго” подало иск к “Энергорынку” на 1,7 млрд гривен
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18:12 - АМПУ начнет разработку системы цифрового обмена данными для судов и портов


Більше новин

ВалютаКурс
Австралійський долар16.913935
Канадський долар18.812895
Юань Женьмiньбi3.535497
Куна3.75062
Чеська крона1.07618
Данська крона3.719482
Гонконгівський долар3.195825
Форинт0.0843998
Індійська рупія0.3496934
Рупія0.00176344
Іранський ріал0.0005967
Новий ізраїльський шекель7.124676
Єна0.2354367
Теньге0.064873
Вона0.0206772
Мексіканський песо1.260315
Молдовський лей1.401289
Новозеландський долар15.974705
Норвезька крона2.784542
Російський рубль0.38203
Саудівський рiял6.683437
Сінгапурський долар18.054745
Ренд1.646417
Шведська крона2.587793
Швейцарський франк25.458632
Єгипетський фунт1.515684
Фунт стерлінгів30.659113
Бiлоруський рубль12.18657
Азербайджанський манат14.742876
Румунський лей5.873949
Турецька ліра4.345701
СПЗ(спеціальні права запозичення)34.337571
Болгарський лев14.179409
Євро27.732088
Злотий6.367873
Алжирський динар0.209821
Така0.297265
Вiрменський драм0.0525856
Іракський динар0.021089
Сом0.358702
Ліванський фунт0.016634
Лівійський динар17.849019
Малайзійський ринггіт6.06338
Марокканський дирхам2.608774
Пакистанська рупія0.157116
Донг0.00107848
Бат0.814071
Дирхам ОАЕ6.812447
Туніський динар8.692591
Узбецький сум0.002887
Новий тайванський долар0.805095
Туркменський новий манат7.148787
Ганських седі4.655371
Сербський динар0.237682
Сомонi2.650588
Ларi8.419394
Золото37481.552
Срiбло425.067
Платина21428.771
Паладiй37042.951

Курси валют, встановлені НБУ на: 27.08.2019

ТікерOpenMaxMinCloseVolume
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Дані за 22.08.2019