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

Getting started with IPython Notebook

The IPython Notebook (NB) is a web interface for our python code. NB is based in a JSON format, sharable and portable in .pynb file format.

To start with a blank notebook, we will click on the New Notebook button. In the following screenshot, we can see how to change the name by clicking on the Untitled0 label, and then we will rename the notebook:

The NB will have access to all resources (text files, images, and so on) in the path. We can upload the text files, images, and other content to the Wakari platform by clicking on the Upload icon (see the arrow in the following screenshot), then we will select the files, and finally we will click on the Upload Files button as shown in the following screenshot:

Finally, we will click on the play icon (see the arrow in the following screenshot) to run the code of our NB. We will get a numbered output for each of our input codes as visible in the following screenshot. We may code several lines in the same input...