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

Installing and running mlpy

According with the official website, mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems. It is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability, and efficiency. Mlpy is multiplatform, it works with Python 2 and 3, and it is open source. Mlpy is distributed under the GNU General Public License Version 3.

We need the following requirements:

  • GCC

  • Numpy 1.7

  • SciPy 0.12

  • GSL 1.11

Installing and running mlpy on Ubuntu

First, we need to download the latest version for Linux from

Unzip and run the following command from the terminal:

$ sudo python3 install


The installation requires GSL 1.11 or greater. We can install the library from Ubuntu Software Center. We just need to look and install the GNU Scientific Library (GSL) development package.

To check whether everything is installed correctly, just open a Python shell and execute:

>>> import mlpy

Installing and running mlpy on Windows

First, we need to download the latest version for Windows from

Then execute the mlpy-3.5.0.win32-py3.2.exe file and follow the wizard as shown in the following screenshot:


The GSL library is precompiled (by Visual Studio Express 2008) and included in mlpy.

To check whether everything is installed correctly, just open a Python shell and execute the following command:

>>> import mlpy