Book Image

Practical Data Analysis

By : Hector Cuesta
Book Image

Practical Data Analysis

By: Hector Cuesta

Overview of this book

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.
Table of Contents (24 chapters)
Practical Data Analysis
About the Author
About the Reviewers

Importance of data visualization

The goal of the data visualization is to expose something new about the underlying patterns and relationships contained within the data. The visualization not only needs to look good but also meaningful in order to help organizations make better decisions. Visualization is an easy way to jump into a complex dataset (small or big) to describe and explore the data efficiently.

Many kinds of data visualizations are available such as bar chart, histogram, line chart, pie chart, heat maps, frequency Wordle (as shown in the following figure) and so on, for one variable, two variables, and many variables in one, two, or three dimensions.

Data visualization is an important part of our data analysis process because it is a fast and easy way to do an exploratory data analysis through summarizing their main characteristics with a visual graph.

The goals of exploratory data analysis are listed as follows:

  • Detection of data errors

  • Checking of assumptions

  • Finding hidden patterns (such as tendency)

  • Preliminary selection of appropriate models

  • Determining relationships between the variables

We will get into more detail about data visualization and exploratory data analysis in Chapter 3, Data Visualization.