A01=Michael Brzustowicz
advanced mapreduce
Age Group_Uncategorized
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apache commons math library
architect data systems
Author_Michael Brzustowicz
automatic-update
big data
bigstring
building data science applications
Category1=Non-Fiction
Category=UMW
Category=UMX
Category=UNA
Category=UNF
Category=UYZM
COP=United States
creating data science applications
csv
data analysis
data input
data modeling
data models
data output
data science
databases
Delivery_Delivery within 10-20 working days
distributed algorithims
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eq_computing
eq_isMigrated=2
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ETL
hadoop
hadoop mapreduce
hbase
hdfs
hive
java
java for data analysis
java for data science
javadb mongodb
jdbc
json
Language_English
learning and prediction
linear algebra
machine learning
matrices
mysql
object-oriented
PA=Available
pig
Price_€50 to €100
PS=Active
relational databases
simplejson
softlaunch
spark
sql
statistical learning
statistics
tsv
vectors
Product details
- ISBN 9781491934111
- Weight: 416g
- Dimensions: 178 x 234mm
- Publication Date: 18 Jul 2017
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.
You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications.
- Examine methods for obtaining, cleaning, and arranging data into its purest form
- Understand the matrix structure that your data should take
- Learn basic concepts for testing the origin and validity of data
- Transform your data into stable and usable numerical values
- Understand supervised and unsupervised learning algorithms, and methods for evaluating their success
- Get up and running with MapReduce, using customized components suitable for data science algorithms
