Ce cours fait partie de la Spécialisation Data Science at Scale

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Spécialisation Data Science at Scale

University of Washington

À propos de ce cours

4.3

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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...

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Réinitialisez les dates limites selon votre disponibilité.

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

Sous-titres : English...

Relational AlgebraPython ProgrammingMapreduceSQL

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Réinitialisez les dates limites selon votre disponibilité.

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

Sous-titres : English...

Week

1Understand the terminology and recurring principles associated with data science, and understand the structure of data science projects and emerging methodologies to approach them. Why does this emerging field exist? How does it relate to other fields? How does this course distinguish itself? What do data science projects look like, and how should they be approached? What are some examples of data science projects? ...

22 vidéos (Total 125 min), 4 lectures, 1 quiz

Appetite Whetting: Extreme Weather2 min

Appetite Whetting: Digital Humanities8 min

Appetite Whetting: Bibliometrics4 min

Appetite Whetting: Food, Music, Public Health5 min

Appetite Whetting: Public Health cont'd, Earthquakes, Legal4 min

Characterizing Data Science5 min

Characterizing Data Science, cont'd5 min

Distinguishing Data Science from Related Topics4 min

Four Dimensions of Data Science6 min

Tools vs. Abstractions7 min

Desktop Scale vs. Cloud Scale5 min

Hackers vs. Analysts2 min

Structs vs. Stats5 min

Structs vs. Stats cont'd5 min

A Fourth Paradigm of Science3 min

Data-Intensive Science Examples6 min

Big Data and the 3 Vs5 min

Big Data Definitions4 min

Big Data Sources6 min

Course Logistics7 min

Twitter Assignment: Getting Started14 min

Supplementary: Three-Course Reading List10 min

Supplementary: Resources for Learning Python10 min

Supplementary: Class Virtual Machine10 min

Supplementary: Github Instructions10 min

Week

2Relational Databases are the workhouse of large-scale data management. Although originally motivated by problems in enterprise operations, they have proven remarkably capable for analytics as well. But most importantly, the principles underlying relational databases are universal in managing, manipulating, and analyzing data at scale. Even as the landscape of large-scale data systems has expanded dramatically in the last decade, relational models and languages have remained a unifying concept. For working with large-scale data, there is no more important programming model to learn....

24 vidéos (Total 122 min), 1 quiz

From Data Models to Databases4 min

Pre-Relational Databases5 min

Motivating Relational Databases3 min

Relational Databases: Key Ideas4 min

Algebraic Optimization Overview6 min

Relational Algebra Overview4 min

Relational Algebra Operators: Union, Difference, Selection6 min

Relational Algebra Operators: Projection, Cross Product4 min

Relational Algebra Operators: Cross Product cont'd, Join6 min

Relational Algebra Operators: Outer Join4 min

Relational Algebra Operators: Theta-Join4 min

From SQL to RA6 min

Thinking in RA: Logical Query Plans4 min

Practical SQL: Binning Timeseries5 min

Practical SQL: Genomic Intervals6 min

User-Defined Functions3 min

Support for User-Defined Functions4 min

Optimization: Physical Query Plans5 min

Optimization: Choosing Physical Plans4 min

Declarative Languages5 min

Declarative Languages: More Examples4 min

Views: Logical Data Independence5 min

Indexes6 min

Week

3The MapReduce programming model (as distinct from its implementations) was proposed as a simplifying abstraction for parallel manipulation of massive datasets, and remains an important concept to know when using and evaluating modern big data platforms. ...

26 vidéos (Total 122 min), 1 quiz

A Sketch of Algorithmic Complexity5 min

A Sketch of Data-Parallel Algorithms5 min

"Pleasingly Parallel" Algorithms4 min

More General Distributed Algorithms4 min

MapReduce Abstraction4 min

MapReduce Data Model3 min

Map and Reduce Functions2 min

MapReduce Simple Example3 min

MapReduce Simple Example cont'd3 min

MapReduce Example: Word Length Histogram2 min

MapReduce Examples: Inverted Index, Join6 min

Relational Join: Map Phase4 min

Relational Join: Reduce Phase4 min

Simple Social Network Analysis: Counting Friends3 min

Matrix Multiply Overview5 min

Matrix Multiply Illustrated4 min

Shared Nothing Computing4 min

MapReduce Implementation5 min

MapReduce Phases6 min

A Design Space for Large-Scale Data Systems4 min

Parallel and Distributed Query Processing5 min

Teradata Example, MR Extensions5 min

RDBMS vs. MapReduce: Features6 min

RDBMS vs. Hadoop: Grep5 min

RDBMS vs. Hadoop: Select, Aggregate, Join3 min

Week

4NoSQL systems are purely about scale rather than analytics, and are arguably less relevant for the practicing data scientist. However, they occupy an important place in many practical big data platform architectures, and data scientists need to understand their limitations and strengths to use them effectively....

36 vidéos (Total 166 min)

NoSQL Roundup4 min

Relaxing Consistency Guarantees3 min

Two-Phase Commit and Consensus Protocols5 min

Eventual Consistency4 min

CAP Theorem4 min

Types of NoSQL Systems4 min

ACID, Major Impact Systems4 min

Memcached: Consistent Hashing2 min

Consistent Hashing, cont'd4 min

DynamoDB: Vector Clocks5 min

Vector Clocks, cont'd5 min

CouchDB Overview4 min

CouchB Views3 min

BigTable Overview5 min

BigTable Implementation5 min

HBase, Megastore3 min

Spanner5 min

Spanner cont'd, Google Systems6 min

MapReduce-based Systems5 min

Bringing Back Joins4 min

NoSQL Rebuttal4 min

Almost SQL: Pig4 min

Pig Architecture and Performance3 min

Data Model3 min

Load, Filter, Group5 min

Group, Distinct, Foreach, Flatten5 min

CoGroup, Join3 min

Join Algorithms3 min

Skew5 min

Other Commands3 min

Evaluation Walkthrough3 min

Review6 min

Context3 min

Spark Examples5 min

RDDs, Benefits6 min

Graph-structured data are increasingly common in data science contexts due to their ubiquity in modeling the communication between entities: people (social networks), computers (Internet communication), cities and countries (transportation networks), or corporations (financial transactions). Learn the common algorithms for extracting information from graph data and how to scale them up. ...

21 vidéos (Total 91 min)

Graph Overview6 min

Structural Analysis4 min

Degree Histograms, Structure of the Web4 min

Connectivity and Centrality4 min

PageRank3 min

PageRank in more Detail3 min

Traversal Tasks: Spanning Trees and Circuits5 min

Traversal Tasks: Maximum Flow1 min

Pattern Matching6 min

Querying Edge Tables4 min

Relational Algebra and Datalog for Graphs4 min

Querying Hybrid Graph/Relational Data3 min

Graph Query Example: NSA6 min

Graph Query Example: Recursion4 min

Evaluation of Recursive Programs3 min

Recursive Queries in MapReduce4 min

The End-Game Problem3 min

Representation: Edge Table, Adjacency List4 min

Representation: Adjacency Matrix2 min

PageRank in MapReduce5 min

PageRank in Pregel5 min

4.3

par HA•Jan 11th 2016

Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.\n\nThe lessons are well designed and clearly conveyed.

par SL•May 28th 2016

I like the breadth of coverage of this class. Each of the exercise is a gem in that I get to learn something new also. I would highly recommend this even to experience practitioner also.

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....

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....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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