Hands-On Data Analysis with Scala : Perform Data Collection, Processing, Manipulation, and Visualization with Scala.
This book will help you perform effective data analysis with Scala using practical examples. You will come across different challenges and their effective solutions for a variety of data processing tasks - be it data exploration, data manipulation, or real-time data analysis using Apache Spark.
Saved in:
| Online Access: |
Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing, Limited,
2019
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Scala and Data Analysis Life Cycle; Chapter 1: Scala Overview; Getting started with Scala; Running Scala code online; Scastie; ScalaFiddle; Installing Scala on your computer; Installing command-line tools; Installing IDE; Overview of object-oriented and functional programming; Object-oriented programming using Scala; Functional programming using Scala; Scala case classes and the collection API; Scala case classes; Scala collection API; Array; List; Map
- Overview of Scala libraries for data analysisApache Spark; Breeze; Breeze-viz; DeepLearning; Epic; Saddle; Scalalab; Smile; Vegas; Summary; Chapter 2: Data Analysis Life Cycle; Data journey; Sourcing data; Data formats; XML; JSON; CSV; Understanding data; Using statistical methods for data exploration; Using Scala; Other Scala tools; Using data visualization for data exploration; Using the vegas-viz library for data visualization; Other libraries for data visualization; Using ML to learn from data; Setting up Smile; Running Smile; Creating a data pipeline; Summary; Chapter 3: Data Ingestion
- Data extractionPull-oriented data extraction; Push-oriented data delivery; Data staging; Why is the staging important?; Cleaning and normalizing; Enriching; Organizing and storing; Summary; Chapter 4: Data Exploration and Visualization; Sampling data; Selecting the sample; Selecting samples using Saddle; Performing ad hoc analysis; Finding a relationship between data elements; Visualizing data; Vegas viz for data visualization; Spark Notebook for data visualization; Downloading and installing Spark Notebook; Creating a Spark Notebook with simple visuals; More charts with Spark Notebook
- Box plotHistogram; Bubble chart; Summary; Chapter 5: Applying Statistics and Hypothesis Testing; Basics of statistics; Summary level statistics; Correlation statistics; Vector level statistics; Random data generation; Pseudorandom numbers; Random numbers with normal distribution; Random numbers with Poisson distribution; Hypothesis testing; Summary; Section 2: Advanced Data Analysis and Machine Learning; Chapter 6: Introduction to Spark for Distributed Data Analysis; Spark setup and overview; Spark core concepts; Spark Datasets and DataFrames; Sourcing data using Spark; Parquet file format
- Avro file formatSpark JDBC integration; Using Spark to explore data; Summary; Chapter 7: Traditional Machine Learning for Data Analysis; ML overview; Characteristics of ML; Categories or types of ML; Decision trees; Implementing decision trees; Decision tree algorithms; Implementing decision tree algorithms in our example; Evaluating the results; Using our model with a decision tree; Random forest; Random forest algorithms; Ridge and lasso regression; Characteristics of ridge regression; Characteristics of lasso regression; k-means cluster analysis
