Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Data Manipulation at Scale: Systems and Algorithms online course by Coursera is the first step in the Data Science at Scale Specialization series. The University of Washington offers the certification course in collaboration with Coursera.
The Data Manipulation at Scale: Systems and Algorithms online certification course aims to teach the landscape of relevant systems to the students and how these systems are evaluated along with the principles on which they rely. Students will also learn the context of data science as well as the history, skills and challenges that come while working with data. Moreover, students will get hands-on experience in learning how to structure a data science project.
Additionally, the Data Manipulation at Scale: Systems and Algorithms class programme is a self-paced learning course so that students never have to worry about missing a class. At the same time, the course is 100% online so that they can learn from the comfort of their home. The students will be awarded a shareable certificate after completing the course, which can be shared on professional platforms.
The Highlights
- Flexible deadlines
- Self-Paced course
- 100% Online
- Shareable Certificate
- Hands-on Project
- Offered by University of Washington
- Approx 20 hours to complete
Programme Offerings
- online learning
- Flexible Deadlines
- Graded Assignments
- Shareable Certificate
- peer feedback
- Self-paced learning
- Pre-recorded course videos
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | UW WashingtonCoursera |
Data Manipulation at Scale: Systems and Algorithms :
Particulars | Amount |
Fee for 1 month | Rs. 1,676/- |
Fee for 3 months | Rs. 3,369/- |
Fee for 6 months | Rs. 5,029/- |
What you will learn
Upon completion of the Coursera Data Manipulation at Scale: Systems and Algorithms live course you will have:
- Ability to describe recurring patterns, challenges, as well as approaches associated with data science projects and understanding what makes them different from projects in other related fields
- Skills to use and identify programming models associated with scalable data manipulation that include relational algebra, MapReduce, and other data flow models
- Knowledge to describe the landscape of specialized Big Data systems for arrays, graphs and streams
- In-depth knowledge of using database technology adapted for large-scale analytics such as parallel query processing, concepts driving similar databases, and in-database analytics
Who it is for
Admission Details
To apply for the Data Manipulation at Scale: Systems and Algorithms certification programme, follow these steps:
Step 1. Visit https://www.coursera.org/
Step 2. Find ‘Data Manipulation at Scale: Systems and Algorithms online course’ on the website.
Step 3. Now, click on ‘Enroll for FREE’ option.
Step 4. Sign up to Coursera to complete the enrolment process.
Application Details
To join the Coursera Data Manipulation at Scale: Systems and Algorithms course, sign in with your Email ID and password. Opt for the desirable enrolment type to get access to the course content.
The Syllabus
Videos
- 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
Readings
- Supplementary: Three-Course Reading List
- Supplementary: Resources for Learning Python
- Supplementary: Class Virtual Machine
- Supplementary: Github Instructions
Programming Assignment
- Twitter Sentiment Analysis
Videos
- 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
Programming Assignment
- SQL for Data Science Assignment
Videos
- 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
Programming Assignment
- Thinking in MapReduce
- 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
Videos
- 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