Big Data - Capstone Project

Start Date: 09/20/2020

Course Type: Common Course

Course Link:

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About Course

Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.

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Course Introduction

Big Data - Capstone Project In the capstone you will analyse how data from different sources are combined to form meaningful insights. You will practice interpreting data from different perspectives. You will use tools and techniques to create insights from the different perspectives. You will also discuss what insights might be gained from different perspectives. You will create a situation in which to analyze your own dataset and decide on your own dataset analysis method.Introduction Analytical techniques Data compression Decision making Business Strategies for Disrupting Healthcare In this course, you will learn best practices for analyzing, developing and implementing a healthcare sector strategy. You will learn how to build a strong business case for a particular strategy and identify key sectors that will benefit from the strategy. You will also learn how to market your position based on your analysis. You will learn to identify and evaluate health care strategies by size, risk, and revenue. You will also learn how to build a competitive edge in the health care business. This course is aligned to the course specialization you completed in the specialization on Analysis of Healthcare Services. In this course, you will use the tools we’ve provided to you to analyze the health care sector and develop a plan for implementation. By the end of this course, you will be able to: - Develop a health care strategy -market your strategy -develop a competitive advantage in the health care business

Course Tag

Big Data Neo4j Knime Splunk

Related Wiki Topic

Article Example
Big Data to Knowledge Big Data to Knowledge (BD2K) is a project of the National Institutes of Health for knowledge extraction from big data.
Big data The European Commission is funding the 2-year-long Big Data Public Private Forum through their Seventh Framework Program to engage companies, academics and other stakeholders in discussing big data issues. The project aims to define a strategy in terms of research and innovation to guide supporting actions from the European Commission in the successful implementation of the big data economy. Outcomes of this project will be used as input for Horizon 2020, their next framework program.
Capstone (cryptography) Capstone is the name of a United States government long-term project to develop cryptography standards for public and government use. Capstone was authorized by the Computer Security Act of 1987 and was driven by the NIST and the NSA; the project began in 1993. The initiative involved four standard algorithms: a data encryption algorithm called Skipjack, along with the Clipper chip that included the Skipjack algorithm, a digital signature algorithm, DSA, a hash function, SHA-1, and a key exchange protocol. Capstone's first implementation was in the Fortezza PCMCIA card. All Capstone components were designed to provide 80-bit security.
Capstone course A capstone course, also known as capstone unit serves as the culminating and usually integrative experience of an educational program. A capstone course, unit, module or subject in the higher education context may also be referred to as a capstone experience, senior seminar (in the U.S.), or final year project or dissertation (more common in the U.K.).
Capstone Program In 2006, the FAA integrated the Alaskan Capstone project into the national Automatic Dependent Surveillance – Broadcast (ADS–B) program.
Big data Big data analysis is often shallow compared to analysis of smaller data sets. In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data preprocessing.
Big data In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in usability of big data, through automated filtering of non-useful data and correlations.
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Capstone Publishers Capstone imprints contain fiction and nonfiction titles. Capstone also has digital products (myON, Capstone Interactive Library, CapstoneKids FactHound and PebbleGo) and services (CollectionWiz and Library Processing).
Big data Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, ranging from a few dozen terabytes to many petabytes of data.
Big data Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the 2016 U.S. Presidential Election with varying degrees of success. Forbes predicted "If you believe in "Big Data" analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.".
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Capstone Program During FY 1999, the Alaskan Region's "Capstone" Program tied together three of the nine principal elements identified in the "Joint Government/Industry Roadmap for Free Flight Operational Enhancements" with two safety initiatives from the March 1995 NTSB Alaska Safety Study. Operational enhancements included in Project Capstone are:
Big data Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information. A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management (PHM).
Big data In March 2012, The White House announced a national "Big Data Initiative" that consisted of six Federal departments and agencies committing more than $200 million to big data research projects.
Big data Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers". What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."