Data Science at Scale Specialization [11801]

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:

4 courses

Follow the suggested order or choose your own.


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


Highlight your new skills on your resume or LinkedIn.

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

    Data Manipulation at Scale: Systems and Algorithms

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

    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.
    4 weeks of study, 6-8 hours/week

    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.

    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.
    6 weeks of study, 3-4 hours/week

    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


  • 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



Class Central’s Top 100 MOOCs of All Time (2019 edition)
100+ Free Online Courses to Learn about the UN’s Sustainable Development Goals
200 Best Free Coursera Courses in 2019
Пропозиції для молоді від UN
Trading Strategies in Emerging Markets Specialization
450 Free Online Programming & Computer Science Courses You Can Start in August
Підготовка та впровадження проектів розвитку громад
Рада Європи: Доступ до публічної інформації: від А до Я
New MicroMasters® Programs: Gain In-Demand Knowledge to Advance Your Career
Data Science at Scale Specialization

Все статьи раздела Образование


23:30 - Харківська митниця ДФС перерахувала 10 мільярдів гривень до Державного бюджету України
22:12 - Deloitte прогнозирует падение цен на металлургическое сырье
20:24 - Давление на рынок земли идет только по одной причине, нам нужно успеть до конца года — Алексей Мушак
20:00 - Вперше за 6 років обсяги перевалки вантажів в українських портах зросли на понад 20%
19:54 - У квітні наступного року принцип «гроші йдуть за пацієнтом» запрацює на всіх рівнях медичної допомоги, - Бюджет-2020
19:54 - В Україні створять Єдину державну електронну систему у сфері будівництва, - Президент підписав відповідний Закон
19:30 - После протестов шахтеров Владимир Зеленский заявил о погашении долгов
19:30 - Рада поддержала новую модель управления государственным долгом
19:30 - Сокрушительный вариант для Украины — Тимошенко прокомментировала авантюру Геруса по импорту электроэнергии из РФ
19:30 - Украинские деловые СМИ хотят собирать деньги за свой убогий контент
18:36 - Межбанк закрылся долларом по 24,25
18:36 - Арсен Аваков хочет украсть еще 11 млрд гривен “на французских ветолетах”
18:36 - Фонд госимущества планирует за год продать минимум 5 госпредприятий
18:36 - В госсобственности должно остаться только 766 предприятий
18:36 - Кипрский собственник “Альфа-Банка” разместил облигации на 50 млн долларов
17:48 - В Україні стартує масштабний медійний проект «СТОП Фальсифікат»
17:48 - Прем'єр-міністр України доручив забезпечити швидку та прозору приватизацію до кінця 2019 року
17:48 - Держгеокадастр про результати здійснення державного нагляду (контролю) у сфері використання та охорони земель
17:42 - Долг обанкротившихся банков по кредитам рефинансирования составляет 46 млрд гривен
17:42 - Петр Дыминский вышел из состава акционеров банка WOG
17:42 - Альфа-Банк продает главный офис Проминвестбанка в центре Киева за 40 млн долларов
17:42 - Богдана Данилишина переизбрали главой Совета НБУ
17:42 - Молдавский металлургический завод просит у местных сепаратистов дотаций из-за прекращения поставок украинского лома
17:42 - Украина в третьем квартале замедлила темпы роста экономики
17:30 - Курси валют, встановлені НБУ на: 15.11.2019
17:24 - Опционный анализ - Эксперты Freshforex: GBP/USD: Отличная точка для продаж!
17:24 - Опционный анализ - Эксперты Freshforex: GBP/USD: Накопление продолжается!
16:54 - Страховая группа PZU хочет купить mBank — крупнейший онлайн-банк в Польше
16:51 - Все більше компаній обирають Хмарну АТС від Укртелекому
16:51 - МХП создает группу компаний для трансформации бизнеса

Більше новин

Австралійський долар16.444967
Канадський долар18.27682
Юань Женьмiньбi3.453209
Чеська крона1.043123
Данська крона3.568285
Гонконгівський долар3.09673
Індійська рупія0.3370699
Іранський ріал0.0005773
Новий ізраїльський шекель6.966692
Мексіканський песо1.247653
Молдовський лей1.379108
Новозеландський долар15.480593
Норвезька крона2.634659
Російський рубль0.37759
Саудівський рiял6.465333
Сінгапурський долар17.787861
Шведська крона2.493195
Швейцарський франк24.521499
Єгипетський фунт1.510328
Фунт стерлінгів31.131821
Бiлоруський рубль11.7969
Азербайджанський манат14.261764
Румунський лей5.592731
Турецька ліра4.207987
СПЗ(спеціальні права запозичення)33.269661
Болгарський лев13.632388
Алжирський динар0.208231
Вiрменський драм0.05212999
Іракський динар0.020874
Ліванський фунт0.0164856
Лівійський динар17.721592
Малайзійський ринггіт5.939711
Марокканський дирхам2.580215
Пакистанська рупія0.160227
Дирхам ОАЕ6.757723
Туніський динар8.774027
Узбецький сум0.002623
Новий тайванський долар0.817062
Туркменський новий манат7.091168
Ганських седі4.583396
Сербський динар0.236013

Курси валют, встановлені НБУ на: 15.11.2019


Дані за 13.11.2019