Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Sourcing data

Data sourcing is one of the most preliminary steps in the life cycle of data. It includes activities such as data acquisition, cleaning, and organization. The following is a list of the specific activities that it involves:

  • Raw data delivery—push model versus pull (extract) model
  • Handling a variety of data formats (CSV, JSON, XML)
  • Detecting errors in the data that is delivered
  • Removing bad data
  • Data enrichment—filling the gaps in the data
  • Combining data with other datasets
  • Defining a data model
  • Transforming the raw data model into the defined model
  • Storing the data

Data formats

By its very nature, there is a wide variety of raw data. There are a variety of systems generating raw data for a variety...