Introduction to Big Data
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+.
Course Syllabus
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!
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?
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.
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.
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.
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.
Course presenter
Ilkay Altintas
Chief Data Science Officer
Amarnath Gupta
Director, Advanced Query Processing Lab
FAQ
What is Coursera?
Coursera is an online education provider that offers online courses, popularly known as MOOCs or Massive Open Online Courses, from top universities around the world. Currently it has over 200 partners from 48 countries. These partners include Universities such as Stanford, Duke, Penn, Princeton, Michigan, Peking, and HEC Paris. Coursera has also started partnering with companies like IBM, Google, and PwC — these companies are also launching courses on Coursera.
Coursera is the most popular MOOC provider in the world based on the number of students (over 45 million learners) and has an active catalog of 3,800+ online courses.
As well as these individual courses and 16 online degrees, Coursera offers 400 groups of courses known as Specializations, MasterTracks, and Professional Certificates.
How do Coursera courses work?
Coursera is an online education provider that offers online courses, popularly known as MOOCs or Massive Open Online Courses, from top universities around the world. Currently, Coursera boasts an active catalog of more than 3800 online courses created by these partner institutions.
Coursera courses consist of pre-recorded video lectures that you can watch on a weekly schedule or when it’s convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Generally speaking, Coursera courses are free to audit (i.e. watch videos) but if you want to earn a course Certificate, you will need to pay. An investigation by Class Central discovered 1150+ Coursera courses that include assessments for free although other courses have the assessments behind a paywall.
Many of the Coursera courses are part of Coursera Specializations, a microcredential offered by Coursera. Specializations consist of a sequence of courses and for some Specializations the last course is a Capstone project. If learners earn a certificate for each course in a Specialization, they will receive a Specialization certificate. Specializations are usually geared towards in-demand skills in business and technology. You can take single courses or the whole specialization.
Other courses are grouped into MasterTracks and Professional Certificates. Coursera has launched over 400 microcredentials.
How does a Coursera Subscription work?
Some courses, particularly those in Coursera Specializations, are available on a subscription basis. When you sign up, the first 7 days are free, then you are charged monthly. Check the course or Specialization page to find the cost in US dollars or your local currency. You may also need to check the exchange rate to calculate the cost. The faster you learn, the less you will pay to complete the course or Specialization, in one-month increments.
If you want to complete one or some courses in a Specialization but not the whole Specialization, you need to cancel your subscription after receiving your certificate.
When signing up for Specializations, you cannot select the audit for free option, only a 7-day free trial. To access the materials for free, you need to sign up from the individual course pages. Click on the Learn more link beside the words at the bottom of each course description, available on some Specialization pages. (You can choose to take this course only.)
Coursera Plus is a single payment that gives access to around 3000 courses and Specializations for 12 months.