Data Science at Scale Specialization [15006]


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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Все статьи раздела Образование

Новини

21:00 - МАГАТЭ требует от рф разъяснений по поводу захвата гендиректора Запорожской АЭС
20:05 - Еще 11 судов с продовольствием вышли из украинских портов
19:15 - Оккупанты ракетами вывели из строя две подстанции “Укрэнерго” на юге Украины
18:25 - Уклонисты. А что, если на войну никто не придет?
13:20 - “Укрзализныця” и Deutsche Bahn создадут совместное грузовое предприятие
13:20 - Минэкономики призывает банки поддержать украинский экспорт через механизмы Экспортно-кредитного агентства
12:25 - “Сухая Балка” Александра Ярославского построила насосную станцию для пожаротушения
11:35 - “Метинвест Покровскуголь” Рината Ахметова купил новый проходческий комбайн
11:35 - Канада расширяет санкции против рф в ответ на аннексию территорий Украины
10:45 - Президент отобранного у Михаила Фридмана “Киевстара” надеется на возвращение мобильной отрасли к росту
10:45 - За час повномасштабної війни ми реалізували заарештовану власність майже на 300 млн грн — т.в.о. голови АРМА
10:45 - “Нафтогаз” и власти Львова к февралю запустят ТЭЦ на щепе на 1 млрд гривен
09:50 - МВФ предоставит Украине дополнительные 1,3 млрд долларов
09:50 - В Україні зявляються перші біометанові заводи. Чи варто інвестувати в цей бізнес і скільки можна заробити?
09:50 - Оккупанты задержали гендиректора Запорожской АЭС и вывезли его в неизвестном направлении
09:50 - Украина собирает на IT-чемпионат специалистов из Европы для разработки решений по защите и восстановлению
09:50 - Владимир Зеленский отменил осенний призыв
09:00 - Правительство поможет ритейлу с кредитованием по программе “5-7-9”
09:00 - Не воюй — здавайся в полон: як українцям врятуватися від примусової мобілізації в окупації
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20:50 - США создадут отдельное командование для снабжения и обучения украинской армии
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Більше новин

ВалютаКурс
Австралійський долар23.6343
Канадський долар26.6157
Юань Женьміньбі5.1388
Куна4.7351
Чеська крона1.4517
Данська крона4.7921
Гонконгівський долар4.6584
Форинт0.084431
Індійська рупія0.44945
Рупія0.0024014
Новий ізраїльський шекель10.2488
Єна0.25285
Теньге0.07676
Вона0.025539
Мексиканське песо1.8143
Молдовський лей1.8703
Новозеландський долар20.76
Норвезька крона3.3783
Російський рубль0.6141
Сінгапурський долар25.4603
Ренд2.033
Шведська крона3.2697
Швейцарський франк37.2731
Єгипетський фунт1.8717
Фунт стерлінгів40.3461
Долар США36.5686
Білоруський рубль13.2919
Азербайджанський манат21.5528
Румунський лей7.2001
Турецька ліра1.971
СПЗ (спеціальні права запозичення)46.7751
Болгарський лев18.2214
Євро35.6361
Злотий7.3483
Алжирський динар0.25917
Така0.36232
Вірменський драм0.090148
Домініканське песо0.68504
Іранський ріал0.00087068
Іракський динар0.025047
Сом0.4561
Ліванський фунт0.024258
Лівійський динар7.2354
Малайзійський ринггіт7.8854
Марокканський дирхам3.3231
Пакистанська рупія0.15951
Саудівський ріял9.7324
Донг0.0015321
Бат0.96722
Дирхам ОАЕ9.9558
Туніський динар11.1619
Узбецький сум0.0033202
Новий тайванський долар1.15184
Туркменський новий манат10.4482
Сербський динар0.30372
Сомоні3.5781
Ларі12.9506
Бразильський реал6.7712
Золото60824.55
Срібло693.86
Платина31525.42
Паладій80126.56

Курси валют, встановлені НБУ на: 03.10.2022

ТікерOpenMaxMinCloseVolume
207880916.15916.15916.15916.1519999554.50
21590936904.2997536904.2997536904.2997536904.299757380859.95
225668956.05956.05956.05956.05956.05
2258091030.081030.081030.081030.081030.08
2259081048.091048.091048.091048.091048.09
BAVL0.270.270.270.2737800.00
CEEN4.55.24.55.24850.00
KER209209209209209.00
MHPC185.119918519932021.00

Дані за 30.09.2022