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

Algorithm selections

Each ML algorithm has its own strengths and weaknesses. Selecting an appropriate machine algorithm and tuning the model requires a fair amount of experience working with these algorithms, however, the following factors also play a significant role in applying these techniques effectively:

  • Asking the right question: A great deal of effort is generally required in formulating the right question.
  • Understanding the business domain: Having a good understanding of the relevant business domain and context is equally important to build good models.
  • Understanding data: Ultimately, the data is used to train the model. If the data is not understood correctly or the data quality is poor, the built model is unlikely to be effective.

All of the preceding aspects outlined are somewhat interdependent and a mastery of all of these is a prerequisite to selecting the appropriate...