Brief Information
- Name :?Data Manipulation at Scale: Systems and Algorithms
- Lecturer 😕Bill Howe
- Duration: 2016-01-04 ~ 02-08 (4 weeks)
- Course : The 1st (1/4) course of Data Science at Scale Specialization? in Coursera
- Syllabus
- Record
- Certificate
- Learning outcome
- Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields.
- Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models.
- Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics
- Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends.
- “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
- Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
Syllabus
Week 1
Data Science Context and Concepts
- Lesson 1: Examples and the Diversity of Data Science
- Lesson 2: Working Definitions of Data Science
- Lesson 3: Characterizing this Course
- Lesson 4: Related Topics
- Lesson 5 : Course Logistics
- Assignment 1: Twitter Sentiment Analysis
- Assignment: Twitter Sentiment Analysis
Week 2
Relational Databases and the Relational Algebra
- Lesson 6: Principles of Data Manipulation and Management
- Lesson 7: Relational Algebra
- Lesson 8: SQL for Data Science
- Lesson 9: Key Principles of Relational Databases
- Assignment 2: SQL
- Assignment: SQL for Data Science Assignment
Week 3
MapReduce and Parallel Dataflow Programming
- Lesson 10: Reasoning about Scale
- Lesson 11: The MapReduce Programming Model
- Lesson 12: Algorithms in MapReduce
- Lesson 13: Parallel Databases vs. MapReduce
- Assignment 3: MapReduce
- Assignment: Thinking in MapReduce
Week 4
NoSQL: Systems and Concepts
Graph Analytics
- Lesson 14: What problems do NoSQL systems aim to solve?
- Lesson 15: Early key-value systems and key concepts
- Lesson 16: Document Stores and Extensible Record Stores
- Lesson 17: Extended NoSQL Systems
- Lesson 18: Pig: Programming with Relational Algebra
- Lesson 19: Pig Analytics
- Lesson 20: Spark
- Lesson 21: Structural Tasks
- Lesson 22: Traversal Tasks
- Lesson 23: Pattern Matching Tasks and Graph Query
- Lesson 24: Recursive Queries
- Lesson 24: Representations and Algorithms
Summary
Week 1
Data Science Context and Concepts
Lesson 1: Examples and the Diversity of Data Science
Lesson 2: Working Definitions of Data Science
Lesson 3: Characterizing this Course
Lesson 4: Related Topics
Lesson 5 : Course Logistics
Assignment 1: Twitter Sentiment Analysis
Week 2
Relational Databases and the Relational Algebra
Lesson 6: Principles of Data Manipulation and Management
Lesson 7: Relational Algebra
Lesson 8: SQL for Data Science
Lesson 9: Key Principles of Relational Databases
Assignment 2: SQL
Week 3
MapReduce and Parallel Dataflow Programming
Lesson 10: Reasoning about Scale
Lesson 11: The MapReduce Programming Model
Lesson 12: Algorithms in MapReduce
Lesson 13: Parallel Databases vs. MapReduce
Assignment 3: MapReduce
Week 4
NoSQL: Systems and Concepts and?Graph Analytics
Lesson 14: What problems do NoSQL systems aim to solve?
Lesson 15: Early key-value systems and key concepts
Lesson 16: Document Stores and Extensible Record Stores
Lesson 17: Extended NoSQL Systems
Lesson 18: Pig: Programming with Relational Algebra
Lesson 19: Pig Analytics
Lesson 20: Spark
Lesson 21: Structural Tasks
Lesson 22: Traversal Tasks
Lesson 23: Pattern Matching Tasks and Graph Query
Lesson 24: Recursive Queries
Lesson 24: Representations and Algorithms
Reading Materials on the Course
- Those are to be listed soon.
Questions
- Where?to get twitterstream.py, which is used in the first assignment?