Big Data Reviews

  • Analyzing Big Data in less time with Google BigQuery

    Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees.

    In this webinar, we will:
    - Build several highly-effective analytics solutions with Google BigQuery
    - Provide a clear road map of BigQuery capabilities
    - Explain how to quickly find answers and examples online
    - Share how to best evaluate BigQuery for your use cases
    - Answer your questions about BigQuery
    75927 Views
  • Real Time Race Analysis | How Big Data Is Used At The Tour de France

    As cycling modernises, NTT are providing real time data for each pro cyclist at the Tour de France for fans and teams to analyse. Dan went behind the scenes at the #TdF2019 to find out how it works, and to see if he could beat the algorithm to correctly predict the stage winner....

    In association with NTT.

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    What kind of data would you like to see in pro racing? Do you think the NTT computers can correctly predict the winner each day? Let us know your thoughts on how big data is used in cycling, in the comments below 👇

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    Watch more on GCN...
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    Music - licensed by Epidemic Sound:
    Camera Shutter 6 - SFX Producer
    Don't You Wanna Know (Instrumental Version) - Staffan Carlen
    I Want a Change (Instrumental Version) - The Big Let Down
    In a Forward Motion - Frank Jonsson
    What a Way (Instrumental Version) - OTE

    Photos: © Velo Collection (TDW) / Getty Images & © Bettiniphoto / http://www.bettiniphoto.net/

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    Welcome to the Global Cycling Network | Inside cycling

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    47873 Views
  • Big Data Analytics using Python and Apache Spark | Machine Learning Tutorial

    Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions.


    Section 1: Introduction
    Lecture 1
    About the course
    08:42
    Lecture 2
    About V2 Maestros
    01:39
    Lecture 3
    Resource Bundle
    Article
    Section 2: Overview
    Lecture 4
    Hadoop Overview
    10:06
    Lecture 5
    HDFS Architecture
    14:46
    Lecture 6
    Map Reduce - How it works
    17:24
    Lecture 7
    Map Reduce - Example
    16:46
    Lecture 8
    Hadoop Stack
    06:27
    Lecture 9
    What is Spark?
    14:03
    Lecture 10
    Spark Architecture - Part 1
    13:23
    Lecture 11
    Spark Architecture - Part 2
    13:25
    Lecture 12
    Installing Spark and Setting up for Python
    12:05
    Quiz 1
    Hadoop and Spark Architecture
    5 questions
    Section 3: Programming with Spark
    Lecture 13
    Spark Transformations
    11:33
    Lecture 14
    Spark Actions
    15:04
    Lecture 15
    Advanced Spark Programming
    10:10
    Lecture 16
    Python - Spark Programming examples 1
    16:11
    Lecture 17
    Python - Spark Programming Examples 2
    17:18
    Quiz 2
    Data Engineering with Spark
    5 questions
    Lecture 18
    PRACTICE Exercise : Spark Operations
    Article
    Section 4: Spark SQL
    Lecture 19
    Spark SQL Overview
    10:03
    Lecture 20
    Python - Spark SQL Examples
    16:16
    Quiz 3
    Spark SQL
    2 questions
    Lecture 21
    PRACTICE Exercise : Spark SQL
    Article
    Section 5: Spark Streaming
    Lecture 22
    Streaming with Apache Spark
    15:53
    Lecture 23
    Python - Spark Streaming examples
    17:47
    Quiz 4
    Spark Streaming
    3 questions
    Section 6: Real time Data Science
    Lecture 24
    Basic Elements of Data Science
    11:51
    Lecture 25
    The Dataset
    10:44
    Lecture 26
    Learning from relationships
    12:55
    Lecture 27
    Modeling and Prediction
    09:31
    Lecture 28
    Data Science Use Cases
    07:47
    Lecture 29
    Types of Analytics
    12:08
    Lecture 30
    Types of Learning
    17:16
    Lecture 31
    Doing Data Science in real time with Spark
    07:39
    Quiz 5
    Spark Data Science
    5 questions
    Section 7: Machine Learning with Spark
    Lecture 32
    Spark Machine Learning
    12:18
    Lecture 33
    Analyzing Results and Errors
    13:46
    Lecture 34
    Linear Regression
    19:00
    Lecture 35
    Spark Use Case : Linear Regression
    18:33
    Lecture 36
    Decision Trees
    10:42
    Lecture 37
    Spark Use Case : Decision Trees Classification
    14:58
    Lecture 38
    Principal Component Analysis
    07:28
    Lecture 39
    Random Forests Classification
    10:31
    Lecture 40
    Python Use Case : Random Forests & PCA
    13:16
    Lecture 41
    Text Preprocessing with TF-IDF
    14:53
    Lecture 42
    Naive Bayes Classification
    19:21
    Lecture 43
    Spark Use Case : Naive Bayes & TF-IDF
    07:26
    Lecture 44
    K-Means Clustering
    11:53
    Lecture 45
    Spark Use Case : K-Means
    14:26
    Lecture 46
    Recommendation Engines
    11:55
    Lecture 47
    Spark Use Case : Collaborative Filtering
    06:34
    Lecture 48
    Real Time Twitter Data Sentiment Analysis
    10:11
    Quiz 6
    Spark Machine Learning Algorithms
    4 questions
    Lecture 49
    PRACTICE Exercise : Spark Clustering
    Article
    Lecture 50
    PRACTICE Exercise : Spark Classification
    Article
    Section 8: Conclusion
    Lecture 51
    Closing Remarks
    01:56
    Lecture 52
    BONUS Lecture : Other courses you should check out
    Article
    144041 Views
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