Big Data Specialization

Start Date: 02/23/2020

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/big-data

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About Course

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions. ********* Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Course Syllabus

Introduction to Big Data
Big Data Modeling and Management Systems
Big Data Integration and Processing
Machine Learning With Big Data

Deep Learning Specialization on Coursera

Course Introduction

Unlock Value in Massive Datasets. Learn fundamental big data methods in six straightforward courses. Big Data Specialization In the first part of this Specialization we will learn key concepts of Big Data Analysis, Machine Learning, and Data Structures. You will learn how to implement algorithms that process huge datasets and extract meaningful information. In the second part of the Specialization we will focus on the high-level algorithms that are used in traditional Big Data analysis. These algorithms are essential to the data scientists and data professionals. Machine learning is a hot topic today. We will learn about the algorithms and tools used in machine learning research. We will use Python to implement some of these high-level topics and you will learn the ropes of Python programming in Python. We will also use the Pandas Data Analysis library to visualize the data and learn how to use the command line interface. We will use the Scikit-Learn machine learning framework to train and evaluate models. We will use the Scikit-Learn packages to run machine learning tasks and see the results stream through to users. We will look at the high-level concepts and language design decisions that are made by machine learning algorithms.Machine Learning High-Level Machine Learning Data Structures Machine Learning and Data Models BIM Application Suite The Capstone is the culminating project in the specialization. It helps you to apply the knowledge and skills acquired in the previous courses and the competencies demonstrated in the specialization to solve a real world engineering problem. This course is part of the

Course Tag

Big Data Neo4j Mongodb Apache Spark

Related Wiki Topic

Article Example
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.
Big data There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners did not favour it.
Big data Big data can be used to improve training and understanding competitors, using sport sensors. It is also possible to predict winners in a match using big data analytics.
Big data MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled "Big Data Solution Offering". The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.
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.".
Big data Big data can be described by the following characteristics:
Big data The Workshops on Algorithms for Modern Massive Data Sets (MMDS) bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to discuss algorithmic challenges of big data.
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."
Big data Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."
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 There has been some work done in Sampling algorithms for big data. A theoretical formulation for sampling Twitter data has been developed.
Big data Big data often poses the same challenges as small data; and adding more data does not solve problems of bias, but may emphasize other problems. In particular data sources such as Twitter are not representative of the overall population, and results drawn from such sources may then lead to wrong conclusions. Google Translate—which is based on big data statistical analysis of text—does a good job at translating web pages. However, results from specialized domains may be dramatically skewed.
Big data The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation, but does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome. Below are some examples of initiatives the governmental big data space.
Big data Encrypted search and cluster formation in big data was demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at "Tackling the challenges of Big Data" by MIT Computer Science and Artificial Intelligence Laboratory and Dr. Amir Esmailpour at UNH Research Group investigated the key features of big data as formation of clusters and their interconnections. They focused on the security of big data and the actual orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology. Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.
Big data Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.
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.