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

Natural language processing for data analysis

Natural language processing (NLP) is the ability of a machine to analyze and understand human language. Human language has a very high amount of complexity, which makes parsing and understanding it difficult. There is a great deal of context in spoken and written language. Machines work well with precise rules that are within the confines of good context. With that said, it is still possible to gain an insight into text analysis using NLP techniques. An excellent example of this is Twitter sentiment analysis. Based on the contents of tweets, using NLP, it is possible to determine whether the sentiments of the people are generally positive or negative as a group. Another great example is the successful application of NLP techniques in analyzing customer reviews of a product or service.

The ML algorithms explored so far in this chapter...