Download Practical Data Analysis by Hector Cuesta, Dr. Sampath Kumar PDF

By Hector Cuesta, Dr. Sampath Kumar

Transform, version, and visualize your information via hands-on initiatives, constructed in open resource tools

Overview

  • Explore find out how to examine your facts in quite a few cutting edge methods and switch them into insight
  • Learn to take advantage of the D3.js visualization device for exploratory info analysis
  • Understand how one can paintings with graphs and social information analysis
  • Discover how you can practice complicated question innovations and run MapReduce on MongoDB

In Detail

Plenty of small companies face substantial quantities of knowledge yet lack the inner abilities to aid quantitative research. figuring out how you can harness the facility of knowledge research utilizing the newest open resource know-how can cause them to offering greater customer support, the visualization of purchaser wishes, or perhaps the power to procure clean insights in regards to the functionality of earlier items. sensible info research is a e-book excellent for domestic and small enterprise clients who are looking to slice and cube the information they've got available with minimal hassle.

Practical info research is a hands-on advisor to knowing the character of your info and switch it into perception. it's going to introduce you to using computer studying recommendations, social networks analytics, and econometrics to assist your consumers get insights concerning the pool of knowledge they've got handy. acting information practise and processing over numerous varieties of info akin to textual content, pictures, graphs, records, and time sequence can be covered.

Practical facts research offers an in depth exploration of the present paintings in info research via self-contained tasks. First you are going to discover the fundamentals of knowledge education and transformation via OpenRefine. then you definately gets begun with exploratory information research utilizing the D3js visualization framework. additionally, you will be brought to a couple of the laptop studying concepts comparable to, class, regression, and clusterization via sensible tasks comparable to junk mail class, predicting gold costs, and discovering clusters on your fb acquaintances' community. you are going to clear up difficulties in textual content class, simulation, time sequence forecast, social media, and MapReduce via targeted tasks. eventually you'll paintings with quite a lot of Twitter info utilizing MapReduce to accomplish a sentiment research carried out in Python and MongoDB.

Practical information research encompasses a blend of conscientiously chosen algorithms and knowledge scrubbing that permits you to show your info into insight.

What you'll study from this book

  • Work with info to get significant effects out of your facts research projects
  • Visualize your facts to discover tendencies and correlations
  • Build your individual picture similarity seek engine
  • Learn easy methods to forecast numerical values from time sequence data
  • Create an interactive visualization to your social media graph
  • Explore the MapReduce framework in MongoDB
  • Create interactive simulations with D3js

Approach

Practical information research is a realistic, step by step advisor to empower small companies to control and research your information and extract invaluable info from the data

Who this booklet is written for

This e-book is for builders, small enterprise clients, and analysts who are looking to enforce information research and visualization for his or her corporation in a pragmatic method. you would like no previous event with info research or facts processing; even though, simple wisdom of programming, information, and linear algebra is assumed.

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Practical Data Analysis

Rework, version, and visualize your info via hands-on initiatives, constructed in open resource instruments assessment discover how you can research your info in numerous cutting edge methods and switch them into perception discover ways to use the D3. js visualization instrument for exploratory facts research know how to paintings with graphs and social info research observe the best way to practice complicated question options and run MapReduce on MongoDB intimately lots of small companies face significant quantities of knowledge yet lack the inner talents to aid quantitative research.

Extra resources for Practical Data Analysis

Sample text

Data for qualitative study may include written texts (for example, documents or email) and/or audible and visual data (for example, digital images or sounds). In Chapter 11, Sentiment Analysis of Twitter Data, we present a sentiment analysis from Twitter data as an example of qualitative analysis. 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.

Next we need to specify the data type (dtype). Finally, we will define the comma as the delimiter. csv" ,skip_header=1 ,dtype=None ,delimiter=',') 3. Then just print the result. print(data) Output: id: id , id: 001 id: 002 id: 003 id: 006 . . typeTwo: typeTwo, name: name, type: type , typeTwo: Poison, name: Bulbasaur, type: Grass , typeTwo: Poison, name: Ivysaur, type: Grass , typeTwo: Poison, name: Venusaur, type: Grass , typeTwo: Flying, name: Charizard, type: Fire JSON JSON is a common format to exchange data.

Print(data) Output: id: id , id: 001 id: 002 id: 003 id: 006 . . typeTwo: typeTwo, name: name, type: type , typeTwo: Poison, name: Bulbasaur, type: Grass , typeTwo: Poison, name: Ivysaur, type: Grass , typeTwo: Poison, name: Venusaur, type: Grass , typeTwo: Flying, name: Charizard, type: Fire JSON JSON is a common format to exchange data. Although it is derived from JavaScript, Python provides us with a library to parse JSON. json) look as follows: [ { "id": " 001", "typeTwo": " Poison", "name": " Bulbasaur", "type": " Grass" }, { [ 39 ] Working with Data "id": " 002", "typeTwo": " Poison", "name": " Ivysaur", "type": " Grass" }, { "id": " 003", "typeTwo": " Poison", "name": " Venusaur", "type": " Grass" }, .

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