Introduction to Big Data
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.
Buy Now AED 204.99 + VAT
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- المدة 17 ساعات hours
- الطبع بواسطة University of California San Diego
-
Offered by
عن
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+.الوحدات
Getting to Know Us
-
1
Videos
-
2
Readings
1
Videos
- Welcome to the Big Data Specialization
2
Readings
- By the end of this course you will be able to...
- Optional: Watch this fun video about the San Diego Supercomputer Center!
Getting to Know You
-
1
Videos
-
1
Discussion
1
Discussions
- Let's Discuss: Why are you taking this class?
1
Videos
- Tell us about yourself and learn about your classmates
Why Big Data?
-
5
Videos
-
5
Readings
-
1
Discussion
1
Discussions
- Let's Discuss: What application area interests you?
5
Videos
- What launched the Big Data era?
- Applications: What makes big data valuable
- Example: Saving lives with Big Data
- Example: Using Big Data to Help Patients
- A Sentiment Analysis Success Story: Meltwater helping Danone
5
Readings
- Did you know?: 25 facts about big data
- Slides: What Launched the Big Data Era?
- Slides: Applications: What Makes Big Data Valuable?
- Slides: Saving Lives With Big Data
- Slides: Using Big Data to Help Patients
Big Data: Where Does It Come From?
-
8
Videos
-
1
Discussion
1
Discussions
- Let's discuss: Who are you providing data to?
8
Videos
- Getting Started: Where Does Big Data Come From?
- Machine-Generated Data: It's Everywhere and There's a Lot!
- Machine-Generated Data: Advantages
- Big Data Generated By People: The Unstructured Challenge
- Big Data Generated By People: How Is It Being Used?
- Organization-Generated Data: Structured but often siloed
- Organization-Generated Data: Benefits Come From Combining With Other Data Types
- The Key: Integrating Diverse Data
Check Your Knowledge
-
1
Readings
-
1
Assignment
1
Assignment
- Why Big Data and Where Did it Come From?
1
Readings
- Extra Resources
Supplementary Resources - Slides
-
7
Readings
7
Readings
- Slides: Machine-Generated Data: It's Everywhere and There's a Lot!
- Slides: Machine-Generated Data: Advantages
- Slides: Big Data Generated By People: The Unstructured Challenge
- Slides: Big Data Generated By People: How is it Being Used?
- Slides: Organization-Generated Big Data: Structured But Often Siloed
- Slides: Organizaton-Generated Big Data: Benefits
- Slides: The Key - Integrating Diverse Data
Characteristics of Big Data
-
7
Videos
-
2
Readings
7
Videos
- Getting Started: Characteristics Of Big Data
- Characteristics of Big Data - Volume
- Characteristics of Big Data - Variety
- Characteristics of Big Data - Velocity
- Characteristics of Big Data - Veracity
- Characteristics of Big Data - Valence
- The Sixth V: Value
2
Readings
- What does astronomical scale mean?
- A Small Definition of Big Data
Check Your Knowledge
-
2
Discussion
-
1
Assignment
1
Assignment
- V for the V's of Big Data
2
Discussions
- Practice: Writing Big Data questions
- Let's Discuss: Improving the Flamingo Game
Supplementary Resources - Slides
-
7
Readings
7
Readings
- Slides: Getting Started - Characteristics of Big Data
- Slides: Characteristics of Big Data - Volume
- Slides: Characteristics of Big Data - Variety
- Slides: Characteristics of Big Data - Velocity
- Slides: Characteristics of Big Data - Veracity
- Slides: Characteristics of Big Data - Value
- Slides: Characteristics of Big Data - Valence
Defining the Question
-
4
Videos
-
1
Readings
-
1
Discussion
1
Discussions
- Let's Discuss: Thinking more deeply about the Ps
4
Videos
- Data Science: Getting Value out of Big Data
- Building a Big Data Strategy
- How does big data science happen?: Five Components of Data Science
- Asking the Right Questions
1
Readings
- Five P's of Data Science
The Process of Data Analysis
-
7
Videos
-
1
Discussion
1
Discussions
- Let's Discuss: Building a Team
7
Videos
- Steps in the Data Science Process
- Step 1: Acquiring Data
- Step 2-A: Exploring Data
- Step 2-B: Pre-Processing Data
- Step 3: Analyzing Data
- Step 4: Communicating Results
- Step 5: Turning Insights into Action
Check Your Knowledge
-
1
Assignment
1
Assignment
- Data Science 101
Supplementary Resources - Slides
-
11
Readings
11
Readings
- Slides: Getting Value Out of Big Data
- Slides: Building a Big Data Strategy
- Slides: The Five P's of Data Science
- Slides: Asking the Right Questions
- Slides: Steps in the Data Science Process
- Slides: Step 1 - Acquiring Data
- Slides: Step 2A-Exploring Data
- Slides: Step 2B-Preprocessing Data
- Slides: Step 3-Data Analysis
- Slides: Step 4-Communicating Results
- Slides: Step 5-Turning Insights Into Action
Basic Scalable Computing Concepts
-
4
Videos
4
Videos
- Getting Started: Why worry about foundations?
- What is a Distributed File System?
- Scalable Computing over the Internet
- Programming Models for Big Data
Check Your Knowledge
-
1
Assignment
1
Assignment
- Foundations for Big Data
Supplementary Resources - Slides
-
4
Readings
4
Readings
- Slides: Getting Started-Why Worry About Foundations?
- Slides: What is a Distributed File System?
- Slides: Scalable Computing Over the Internet
- Slides: Programming Models for Big Data
Getting Started with Hadoop
-
9
Videos
-
2
Readings
9
Videos
- Hadoop: Why, Where and Who?
- The Hadoop Ecosystem: Welcome to the zoo!
- The Hadoop Distributed File System: A Storage System for Big Data
- YARN: A Resource Manager for Hadoop
- MapReduce: Simple Programming for Big Results
- When to Reconsider Hadoop?
- Cloud Computing: An Important Big Data Enabler
- Cloud Service Models: An Exploration of Choices
- Value From Hadoop and Pre-built Hadoop Images
2
Readings
- MapReduce in the Pasta Sauce Example
- Slides for Getting Started With Hadoop
Check Your Knowledge
-
1
PeerReview
-
1
Assignment
1
Assignment
- Intro to Hadoop
1
Peer Review
- Understand by Doing: MapReduce
Hands On: Hadoop setup
-
3
Readings
3
Readings
- Downloading and Installing Docker Desktop Instructions
- Downloading Hands-On Materials
- Basic terminal shell commands
Hands On: Running your First Application on Hadoop
-
2
Videos
-
2
Readings
2
Videos
- Starting Hadoop
- Run the WordCount program
2
Readings
- Starting Hadoop
- Run the WordCount program Instructions
Hands On: Check Your Knowledge
-
1
Discussion
-
1
Assignment
1
Assignment
- Running Hadoop MapReduce Programs Quiz
1
Discussions
- Let's Discuss: Map Reduce in your life
Hands On: Optional Materials
-
1
Readings
1
Readings
- How do I figure out how to run Hadoop MapReduce programs?
Auto Summary
"Introduction to Big Data" is a foundational course in Data Science & AI, led by Coursera. Tailored for beginners, it delves into the Big Data landscape, key concepts, and practical applications. Learners will explore Hadoop, understand the V's of Big Data, and apply a structured analysis process. No prior programming experience is required, but basic installation skills are needed. The course spans 1020 minutes with a Starter subscription option, ideal for those looking to integrate Big Data insights into their business or career.
Ilkay Altintas
Amarnath Gupta