Washington University - Data Manipulation at Scale: Systems and Algorithms
- Offered byCoursera
- Public/Government Institute
Data Manipulation at Scale: Systems and Algorithms at Coursera Overview
Duration | 20 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Manipulation at Scale: Systems and Algorithms at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 4 in the Data Science at Scale Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 20 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Data Manipulation at Scale: Systems and Algorithms at Coursera Course details
- 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
Data Manipulation at Scale: Systems and Algorithms at Coursera Curriculum
Data Science Context and Concepts
Appetite Whetting: Politics
Appetite Whetting: Extreme Weather
Appetite Whetting: Digital Humanities
Appetite Whetting: Bibliometrics
Appetite Whetting: Food, Music, Public Health
Appetite Whetting: Public Health cont'd, Earthquakes, Legal
Characterizing Data Science
Characterizing Data Science, cont'd
Distinguishing Data Science from Related Topics
Four Dimensions of Data Science
Tools vs. Abstractions
Desktop Scale vs. Cloud Scale
Hackers vs. Analysts
Structs vs. Stats
Structs vs. Stats cont'd
A Fourth Paradigm of Science
Data-Intensive Science Examples
Big Data and the 3 Vs
Big Data Definitions
Big Data Sources
Course Logistics
Twitter Assignment: Getting Started
Supplementary: Three-Course Reading List
Supplementary: Resources for Learning Python
Supplementary: Class Virtual Machine
Supplementary: Github Instructions
Relational Databases and the Relational Algebra
Data Models, Terminology
From Data Models to Databases
Pre-Relational Databases
Motivating Relational Databases
Relational Databases: Key Ideas
Algebraic Optimization Overview
Relational Algebra Overview
Relational Algebra Operators: Union, Difference, Selection
Relational Algebra Operators: Projection, Cross Product
Relational Algebra Operators: Cross Product cont'd, Join
Relational Algebra Operators: Outer Join
Relational Algebra Operators: Theta-Join
From SQL to RA
Thinking in RA: Logical Query Plans
Practical SQL: Binning Timeseries
Practical SQL: Genomic Intervals
User-Defined Functions
Support for User-Defined Functions
Optimization: Physical Query Plans
Optimization: Choosing Physical Plans
Declarative Languages
Declarative Languages: More Examples
Views: Logical Data Independence
Indexes
MapReduce and Parallel Dataflow Programming
What Does Scalable Mean?
A Sketch of Algorithmic Complexity
A Sketch of Data-Parallel Algorithms
"Pleasingly Parallel" Algorithms
More General Distributed Algorithms
MapReduce Abstraction
MapReduce Data Model
Map and Reduce Functions
MapReduce Simple Example
MapReduce Simple Example cont'd
MapReduce Example: Word Length Histogram
MapReduce Examples: Inverted Index, Join
Relational Join: Map Phase
Relational Join: Reduce Phase
Simple Social Network Analysis: Counting Friends
Matrix Multiply Overview
Matrix Multiply Illustrated
Shared Nothing Computing
MapReduce Implementation
MapReduce Phases
A Design Space for Large-Scale Data Systems
Parallel and Distributed Query Processing
Teradata Example, MR Extensions
RDBMS vs. MapReduce: Features
RDBMS vs. Hadoop: Grep
RDBMS vs. Hadoop: Select, Aggregate, Join
NoSQL: Systems and Concepts
NoSQL Context and Roadmap
NoSQL Roundup
Relaxing Consistency Guarantees
Two-Phase Commit and Consensus Protocols
Eventual Consistency
CAP Theorem
Types of NoSQL Systems
ACID, Major Impact Systems
Memcached: Consistent Hashing
Consistent Hashing, cont'd
DynamoDB: Vector Clocks
Vector Clocks, cont'd
CouchDB Overview
CouchB Views
BigTable Overview
BigTable Implementation
HBase, Megastore
Spanner
Spanner cont'd, Google Systems
MapReduce-based Systems
Bringing Back Joins
NoSQL Rebuttal
Almost SQL: Pig
Pig Architecture and Performance
Data Model
Load, Filter, Group
Group, Distinct, Foreach, Flatten
CoGroup, Join
Join Algorithms
Skew
Other Commands
Evaluation Walkthrough
Review
Context
Spark Examples
RDDs, Benefits
Graph Overview
Structural Analysis
Degree Histograms, Structure of the Web
Connectivity and Centrality
PageRank
PageRank in more Detail
Traversal Tasks: Spanning Trees and Circuits
Traversal Tasks: Maximum Flow
Pattern Matching
Querying Edge Tables
Relational Algebra and Datalog for Graphs
Querying Hybrid Graph/Relational Data
Graph Query Example: NSA
Graph Query Example: Recursion
Evaluation of Recursive Programs
Recursive Queries in MapReduce
The End-Game Problem
Representation: Edge Table, Adjacency List
Representation: Adjacency Matrix
PageRank in MapReduce
PageRank in Pregel
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