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Big Data Specialization

Unlock Value in Massive Datasets

Earn a Certificate

  • Specialization via Coursera and University of California, San Diego
  • $444 for 7 months
  • 4 courses + capstone project
18 Reviews
Rating based on 18 student reviews.

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Big Data
★★☆☆☆ (18 Reviews)
Learn fundamental big data methods in six straightforward courses.
Credential Type
7 months

In this Specialization, you will develop a robust set of skills that will allow you to process, analyze, and extract meaningful information from large amounts of complex data. You will develop talking knowledge, and practical execution knowledge, for the Hadoop platform, it's architecture and major elements of the ecosystem. Through hands-on instruction and assignments, you will develop working knowledge of tools, such as Spark, Pig, and Hive, and strategies for processing massive datasets using the map/reduce framework. You will be exposed to these tools and strategies as they might apply in particular to analyzing big data. You will become proficient in carrying out scalable basic analysis and comfortable enough to apply advanced analytics, predictive modeling, or graph analysis to problems in your domain. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned by tuning and scaling your own analysis, building your own model, applying tools in new ways, or some other similar kind of effort, to analyze big data in the whatever area of your choice.This class is designed for non-programmers familiar with SQL and desire big-data skill, and for programmers who are new to big data, or new to big data analytics.

Incentives & Benefits

When you complete this Specialization, you will have a robust set of skills that are in high demand across dozens of modern industries. You will be able to extract meaningful information from large amounts of complex data, using powerful tools such as Splunk and Hadoop, and you’ll be able to build customized applications to analyze and visualize data from various sources.

What You'll Learn

  • Process, analyze, and interpret massive and complex data
  • Use common Big Data technologies, including Splunk and Apache Hadoop
  • Build data tools, visualizations, and dashboards

Recommended Background

    ★★★☆☆ (35) 3 weeks 19th Aug, 2019
    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!<br /> <br /> At the end of this course, you will be able to:<br /> <br /> * 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. <br /> <br /> * 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.<br /> <br /> * Get value out of Big Data by using a 5-step process to structure your analysis. <br /> <br /> * Identify what are and what are not big data problems and be able to recast big data problems as data science questions.<br /> <br /> * Provide an explanation of the architectural components and programming models used for scalable big data analysis.<br /> <br /> * 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.<br /> <br /> * Install and run a program using Hadoop!<br /> <br /> 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. <br /> <br /> Hardware Requirements:<br /> (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. <br /> <br /> Software Requirements:<br /> 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+.
    ★★☆☆☆ (25) 5 weeks 19th Aug, 2019
    This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis process.
    ★☆☆☆☆ (10) 5 weeks 13th Jun, 2016
    *********<br /> A new, improved version of the Big Data Specialization will become available on June 6! As such, enrollment for this course and all courses in this original Big Data Specialization will close on June 6. <br /> <br /> The original Big Data Specialization will continue to run until September 2016, when the Capstone will be offered for learners in this version of the Specialization.<br /> <br /> If you are in the middle of the Specialization and have purchased the entire original Big Data Specialization before June 6, Coursera will reach out to you to offer you the option of staying in the original Specialization or taking the new version. <br /> <br /> If you are just getting started on this Specialization, we recommend that you wait until June 6 to enroll in the new version. <br /> <br /> *********<br /> <br /> <br /> This course is for novice programmers or business people who'd like to understand more advanced tools used to wrangle and analyze big data. In this course you will be guided in basic approaches to querying and exploring data using higher level tools built on top of a Hadoop Platform. You will be walked through query interfaces, environments, and the canonical situations for tools like HBASE, HIVE, Pig, as well as more general tools like Spark-SQL. After this course you will be able to identify the kinds of analysis you can get of big data and how to interpret these results.
    ★★☆☆☆ (13) 4 weeks 26th Aug, 2019
    Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.<br /> <br /> At the end of the course, you will be able to:<br /> • Design an approach to leverage data using the steps in the machine learning process.<br /> • Apply machine learning techniques to explore and prepare data for modeling.<br /> • Identify the type of machine learning problem in order to apply the appropriate set of techniques.<br /> • Construct models that learn from data using widely available open source tools.<br /> • Analyze big data problems using scalable machine learning algorithms on Spark.<br /> <br /> Software Requirements: <br /> Cloudera VM, KNIME, Spark
    ★★★☆☆ (6) 4 weeks 26th Aug, 2019
    Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data.<br /> <br /> After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

    18 Reviews.

    Mostafa Abd Elfattah
    completed this credential in Feb 2015.

    Felice Pollano
    Felice Pollano
    Senior architect and developer
    Field of study
    Partially Completed this credential.

    Interesting and good, even if not full satisfying for a software engineer

    Darrall Henderson
    Darrall Henderson
    Field of study
    Operations research
    Doctor of Philosophy
    completed this credential in May 2016.

    Good content - very poor execution, evaluation, and practical excercises

    More reviews
    18 ratings
    18 reviews

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