Using R for Big Data with Spark
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Torrent File Content (21 files)
Using R for Big Data with Spark
11. Modeling with Gaussian Generalized Linear Models.mp4 -
11. Modeling with Gaussian Generalized Linear Models.mp4 -
57.41 MB
10. Data Basics.mp4 -
41.85 MB
1. Welcome to the Course.mp4 -
24.74 MB
12. Modeling with Binomial Generalized Linear Models.mp4 -
49.93 MB
13. Naive Bayes and K-Means Modeling.mp4 -
50.68 MB
14. Bigger Data and S3.mp4 -
39.59 MB
15. Accessing S3 Spark Dataframes.mp4 -
26.92 MB
16. SparkR Dataframe Operations.mp4 -
56.95 MB
17. SparkSQL.mp4 -
30.83 MB
18. Brief Look at HDFS.mp4 -
56.8 MB
19. Brief Look at Databricks Community Edition.mp4 -
43.26 MB
2. About the Author.mp4 -
4.97 MB
20. Wrap Up and Thank You.mp4 -
4.65 MB
3. How To Access Your Working Files.mp4 -
4.18 MB
4. Creating an AWS Launching Instance.mp4 -
49.24 MB
5. Connecting to AWS Instance using SSH.mp4 -
37.6 MB
6. Connecting to AWS Instance using PuTTY.mp4 -
48.13 MB
7. Starting Spark Clusters Part 1.mp4 -
48.1 MB
8. Starting Spark Clusters Part 2.mp4 -
50.52 MB
9. Terminate Your Clusters.mp4 -
5.14 MB
Torrent downloaded from www.DNoid.me - Demonoid.txt -
56 bytes
Description
Miscellaneous
Data analysts familiar with R will learn to leverage the power of Spark, distributed computing and cloud storage in this course that shows you how to use your R skills in a big data environment.
You'll learn to create Spark clusters on the Amazon Web Services (AWS) platform; perform cluster based data modeling using Gaussian generalized linear models, binomial generalized linear models, Naive Bayes, and K-means modeling; access data from S3 Spark DataFrames and other formats like CSV, Json, and HDFS; and do cluster based data manipulation operations with tools like SparkR and SparkSQL. By course end, you'll be capable of working with massive data sets not possible on a single computer. This hands-on class requires each learner to set-up their own extremely low-cost, easily terminated AWS account.
Discover how to use your R skills in a big data distributed cloud computing cluster environment
Gain hands-on experience setting up Spark clusters on Amazon's AWS cloud services platform
Understand how to control a cloud instance on AWS using SSH or PuTTY
Explore basic distributed modeling techniques like GLM, Naive Bayes, and K-means
Learn to do cloud based data manipulation and processing using SparkR and SparkSQL
Understand how to access data from the CSV, Json, HDFS, and S3 formats
Manuel Amunategui is a data science practitioner, consultant, teacher, and author with 16+ years of data science experience. A former quantitative analyst for a Wall Street brokerage firm, he now serves as the lead data scientist for Providence Health & Services in Portland, Oregon. In his free time, Manuel does competitive data modeling on Kaggle.com, CrowdANALYTIX.com, Datascience.net, and DrivenData.org.
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