Michael R. Brzustowicz [Michael R. Brzustowicz] - Data Science with Java
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Data Science with Java: summary, description and annotation
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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 todays 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.
Youll 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, youll 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
Michael R. Brzustowicz [Michael R. Brzustowicz]: author's other books
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![Alexey Grigorev [Alexey Grigorev] - Mastering Java for Data Science](/uploads/posts/book/119620/thumbs/alexey-grigorev-alexey-grigorev-mastering-java.jpg)
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All datasets are stored under src/main/resources/datasets. While Java class codes are stored under src/main/java, user resources are stored under src/main/resources. In general, we use the JAR loader functionality to retrieve contents of a file directly from the JAR, not from the filesystem.
Anscombes quartet is a set .
Table A-1. Anscombes quartet data| x1 | y1 | x2 | y2 | x3 | y3 | x4 | y4 |
|---|---|---|---|---|---|---|---|
| 10.0 | 8.04 | 10.0 | 9.14 | 10.0 | 7.46 | 8.0 | 6.58 |
| 8.0 | 6.95 | 8.0 | 8.14 | 8.0 | 6.77 | 8.0 | 5.76 |
| 13.0 | 7.58 | 13.0 | 8.74 | 13.0 | 12.74 | 8.0 | 7.71 |
| 9.0 | 8.81 | 9.0 | 8.77 | 9.0 | 7.11 | 8.0 | 8.84 |
| 11.0 | 8.33 | 11.0 | 9.26 | 11.0 | 7.81 | 8.0 | 8.47 |
| 14.0 | 9.96 | 14.0 | 8.10 | 14.0 | 8.84 | 8.0 | 7.04 |
| 6.0 | 7.24 | 6.0 | 6.13 | 6.0 | 6.08 | 8.0 | 5.25 |
| 4.0 | 4.26 | 4.0 | 3.10 | 4.0 | 5.39 | 19.0 | 12.50 |
| 12.0 | 10.84 | 12.0 | 9.13 | 12.0 | 8.15 | 8.0 | 5.56 |
| 7.0 | 4.82 | 7.0 | 7.26 | 7.0 | 6.42 | 8.0 | 7.91 |
| 5.0 | 5.68 | 5.0 | 4.74 | 5.0 | 5.73 | 8.0 | 6.89 |
We can easily hardcode the data as static members of the class:
publicclassAnscombe{publicstaticfinaldouble[]x1={10.0,8.0,13.0,9.0,11.0,14.0,6.0,4.0,12.0,7.0,5.0};publicstaticfinaldouble[]y1={8.04,6.95,7.58,8.81,8.33,9.96,7.24,4.26,10.84,4.82,5.68};publicstaticfinaldouble[]x2={10.0,8.0,13.0,9.0,11.0,14.0,6.0,4.0,12.0,7.0,5.0};publicstaticfinaldouble[]y2={9.14,8.14,8.74,8.77,9.26,8.10,6.13,3.10,9.13,7.26,4.74};publicstaticfinaldouble[]x3={10.0,8.0,13.0,9.0,11.0,14.0,6.0,4.0,12.0,7.0,5.0};publicstaticfinaldouble[]y3={7.46,6.77,12.74,7.11,7.81,8.84,6.08,5.39,8.15,6.42,5.73};publicstaticfinaldouble[]x4={8.0,8.0,8.0,8.0,8.0,8.0,8.0,19.0,8.0,8.0,8.0};publicstaticfinaldouble[]y4={6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89};}Then we can call any array:
double[]x1=Anscombe.x1;This is the sentiment-labeled dataset from https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences. Download three files and place them in src/main/resources/datasets/sentiment. They contain data from IMDb, Yelp, and Amazon. There is a single sentence and then a tab-delimited 0 or 1 corresponding to respective negative or positive sentiment. Not all sentences have a corresponding label.
IMDb has 1,000 sentences, with 500 positive (1) and 500 negative (0). Yelp has 3,729 sentences, with 500 positive (1) and 500 negative (0). Amazon has 15,004 sentences, with 500 positive (1) and 500 negative (0):
publicclassSentiment{privatefinalList<String>documents=newArrayList<>();privatefinalList<Integer>sentiments=newArrayList<>();privatestaticfinalStringIMDB_RESOURCE="/datasets/sentiment/imdb_labelled.txt";privatestaticfinalStringYELP_RESOURCE="/datasets/sentiment/yelp_labelled.txt";privatestaticfinalStringAMZN_RESOURCE="/datasets/sentiment/amazon_cells_labelled.txt";publicenumDataSource{IMDB,YELP,Font size:
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