About this course: Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
Created by: University of California, San Diego
Taught by: Ilkay Altintas, Chief Data Science Officer
San Diego Supercomputer Center
Taught by: Amarnath Gupta, Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)
| Basic Info | Course 1 of 6 in the Big Data Specialization |
| Commitment | 3 weeks of study, 5-6 hours/week |
| Language | English, Subtitles: Persian |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Welcome
Welcome to the Big Data Specialization! We're excited for you to get to know us and we're looking forward to learning about you!
2 videos, 2 readings
- Video: Welcome to the Big Data Specialization
- Reading: By the end of this course you will be able to...
- Reading: Optional: Watch this fun video about the San Diego Supercomputer Center!
- Video: Tell us about yourself and learn about your classmates
- Discussion Prompt: Let's Discuss: Why are you taking this class?
Big Data: Why and Where
Data -- it's been around (even digitally) for a while. What makes data "big" and where does this big data come from?
13 videos, 13 readings
- Video: What launched the Big Data era?
- Video: Applications: What makes big data valuable
- Discussion Prompt: Let's Discuss: What application area interests you?
- Video: Example: Saving lives with Big Data
- Video: Example: Using Big Data to Help Patients
- Video: A Sentiment Analysis Success Story: Meltwater helping Danone
- Reading: Did you know?: 25 facts about big data
- Reading: Slides: What Launched the Big Data Era?
- Reading: Slides: Applications: What Makes Big Data Valuable?
- Reading: Slides: Saving Lives With Big Data
- Reading: Slides: Using Big Data to Help Patients
- Video: Getting Started: Where Does Big Data Come From?
- Video: Machine-Generated Data: It's Everywhere and There's a Lot!
- Video: Machine-Generated Data: Advantages
- Video: Big Data Generated By People: The Unstructured Challenge
- Video: Big Data Generated By People: How Is It Being Used?
- Video: Organization-Generated Data: Structured but often siloed
- Video: Organization-Generated Data: Benefits Come From Combining With Other Data Types
- Video: The Key: Integrating Diverse Data
- Discussion Prompt: Let's discuss: Who are you providing data to?
- Reading: Extra Resources
- Reading: Slides: Machine-Generated Data: It's Everywhere and There's a Lot!
- Reading: Slides: Machine-Generated Data: Advantages
- Reading: Slides: Big Data Generated By People: The Unstructured Challenge
- Reading: Slides: Big Data Generated By People: How is it Being Used?
- Reading: Slides: Organization-Generated Big Data: Structured But Often Siloed
- Reading: Slides: Organizaton-Generated Big Data: Benefits
- Reading: Slides: The Key - Integrating Diverse Data
Graded: Why Big Data and Where Did it Come From?
WEEK 2
Characteristics of Big Data and Dimensions of Scalability
You may have heard of the "Big Vs". We'll give examples and descriptions of the commonly discussed 5. But, we want to propose a 6th V and we'll ask you to practice writing Big Data questions targeting this V -- value.
7 videos, 9 readings
- Video: Getting Started: Characteristics Of Big Data
- Video: Characteristics of Big Data - Volume
- Reading: What does astronomical scale mean?
- Video: Characteristics of Big Data - Variety
- Video: Characteristics of Big Data - Velocity
- Video: Characteristics of Big Data - Veracity
- Video: Characteristics of Big Data - Valence
- Video: The Sixth V: Value
- Reading: A Small Definition of Big Data
- Discussion Prompt: Practice: Writing Big Data questions
- Discussion Prompt: Let's Discuss: Improving the Flamingo Game
- Reading: Slides: Getting Started - Characteristics of Big Data
- Reading: Slides: Characteristics of Big Data - Volume
- Reading: Slides: Characteristics of Big Data - Variety
- Reading: Slides: Characteristics of Big Data - Velocity
- Reading: Slides: Characteristics of Big Data - Veracity
- Reading: Slides: Characteristics of Big Data - Value
- Reading: Slides: Characteristics of Big Data - Valence
Graded: V for the V's of Big Data
Data Science: Getting Value out of Big Data
We love science and we love computing, don't get us wrong. But the reality is we care about Big Data because it can bring value to our companies, our lives, and the world. In this module we'll introduce a 5 step process for approaching data science problems.
11 videos, 12 readings
- Video: Data Science: Getting Value out of Big Data
- Video: Building a Big Data Strategy
- Video: How does big data science happen?: Five Components of Data Science
- Reading: Five P's of Data Science
- Discussion Prompt: Let's Discuss: Thinking more deeply about the Ps
- Video: Asking the Right Questions
- Video: Steps in the Data Science Process
- Video: Step 1: Acquiring Data
- Video: Step 2-A: Exploring Data
- Video: Step 2-B: Pre-Processing Data
- Video: Step 3: Analyzing Data
- Video: Step 4: Communicating Results
- Video: Step 5: Turning Insights into Action
- Discussion Prompt: Let's Discuss: Building a Team
- Reading: Slides: Getting Value Out of Big Data
- Reading: Slides: Building a Big Data Strategy
- Reading: Slides: The Five P's of Data Science
- Reading: Slides: Asking the Right Questions
- Reading: Slides: Steps in the Data Science Process
- Reading: Slides: Step 1 - Acquiring Data
- Reading: Slides: Step 2A-Exploring Data
- Reading: Slides: Step 2B-Preprocessing Data
- Reading: Slides: Step 3-Data Analysis
- Reading: Slides: Step 4-Communicating Results
- Reading: Slides: Step 5-Turning Insights Into Action
Graded: Data Science 101
WEEK 3
Foundations for Big Data Systems and Programming
Big Data requires new programming frameworks and systems. For this course, we don't programming knowledge or experience -- but we do want to give you a grounding in some of the key concepts.
4 videos, 4 readings
- Video: Getting Started: Why worry about foundations?
- Video: What is a Distributed File System?
- Video: Scalable Computing over the Internet
- Video: Programming Models for Big Data
- Reading: Slides: Getting Started-Why Worry About Foundations?
- Reading: Slides: What is a Distributed File System?
- Reading: Slides: Scalable Computing Over the Internet
- Reading: Slides: Programming Models for Big Data
Graded: Foundations for Big Data
Systems: Getting Started with Hadoop
Let's look at some details of Hadoop and MapReduce. Then we'll go "hands on" and actually perform a simple MapReduce task in the Cloudera VM. Pay attention - as we'll guide you in "learning by doing" in diagramming a MapReduce task as a Peer Review.
11 videos, 8 readings
- Video: Hadoop: Why, Where and Who?
- Video: The Hadoop Ecosystem: Welcome to the zoo!
- Video: The Hadoop Distributed File System: A Storage System for Big Data
- Video: YARN: A Resource Manager for Hadoop
- Video: MapReduce: Simple Programming for Big Results
- Reading: MapReduce in the Pasta Sauce Example
- Video: When to Reconsider Hadoop?
- Video: Cloud Computing: An Important Big Data Enabler
- Video: Cloud Service Models: An Exploration of Choices
- Video: Value From Hadoop and Pre-built Hadoop Images
- Reading: Slides for Getting Started With Hadoop
- Reading: Downloading and Installing the Cloudera VM Instructions (Mac)
- Reading: Downloading and Installing the Cloudera VM Instructions (Windows)
- Reading: FAQ
- Reading: Copy your data into the Hadoop Distributed File System (HDFS) Instructions
- Video: Copy your data into the Hadoop Distributed File System (HDFS)
- Reading: Run the WordCount program Instructions
- Video: Run the WordCount program
- Discussion Prompt: Let's Discuss: Map Reduce in your life
- Reading: How do I figure out how to run Hadoop MapReduce programs?
Graded: Intro to Hadoop
Graded: Understand by Doing: MapReduce
Graded: Running Hadoop MapReduce Programs Quiz
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
University of California, San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Pricing
| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
Ratings and Reviews
The materials are very well organized and apt for the title.
Awesome course, the content is well organized and it gives you all you need to know.
Short, but structured and simple way to introduce newbies to Big Data world.
Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.
© 2017 Coursera Inc. All rights reserved.
great