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  • Book Overview & Buying Practical Data Analysis
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Practical Data Analysis

Practical Data Analysis - Second Edition

By : Hector Cuesta, Dr. Sampath Kumar
3.5 (2)
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Practical Data Analysis

Practical Data Analysis

3.5 (2)
By: Hector Cuesta, Dr. Sampath Kumar
Buy this Book

Overview of this book

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.
Table of Contents (16 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started
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1. Getting Started
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Computer science
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Artificial intelligence
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Machine learning
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Statistics
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Mathematics
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Knowledge domain
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Data, information, and knowledge
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The data analysis process
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Quantitative versus qualitative data analysis
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Importance of data visualization
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What about big data?
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Quantified self
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Tools and toys for this book
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Summary
2
2. Preprocessing Data
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2. Preprocessing Data
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Data sources
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Data scrubbing
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Data formats
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Data reduction methods
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Getting started with OpenRefine
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Summary
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3. Getting to Grips with Visualization
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3. Getting to Grips with Visualization
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What is visualization?
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Working with web-based visualization
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Exploring scientific visualization
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Visualization in art
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The visualization life cycle
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Visualizing different types of data
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Getting started with D3.js
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Interaction and animation
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Data from social networks
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An overview of visual analytics
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Summary
4
4. Text Classification
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4. Text Classification
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Learning and classification
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Bayesian classification
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E-mail subject line tester
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The data
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The algorithm
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Classifier accuracy
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Summary
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5. Similarity-Based Image Retrieval
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5. Similarity-Based Image Retrieval
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Image similarity search
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Dynamic time warping
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Processing the image dataset
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Implementing DTW
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Analyzing the results
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Summary
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6. Simulation of Stock Prices
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6. Simulation of Stock Prices
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Financial time series
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Random Walk simulation
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Monte Carlo methods
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Generating random numbers
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Implementation in D3js
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Quantitative analyst
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Summary
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7. Predicting Gold Prices
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7. Predicting Gold Prices
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Working with time series data
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Smoothing time series
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Lineal regression
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The data - historical gold prices
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Nonlinear regressions
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Summary
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8. Working with Support Vector Machines
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8. Working with Support Vector Machines
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Understanding the multivariate dataset
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Dimensionality reduction
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Getting started with SVM
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Summary
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9. Modeling Infectious Diseases with Cellular Automata
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9. Modeling Infectious Diseases with Cellular Automata
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Introduction to epidemiology
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The epidemic models
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Modeling with Cellular Automaton
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Simulation of the SIRS model in CA with D3.js
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Summary
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10. Working with Social Graphs
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10. Working with Social Graphs
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Structure of a graph
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Social networks analysis
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Acquiring the Facebook graph
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Working with graphs using Gephi
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Statistical analysis
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Degree distribution
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Transforming GDF to JSON
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Graph visualization with D3.js
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Summary
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11. Working with Twitter Data
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11. Working with Twitter Data
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The anatomy of Twitter data
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Using OAuth to access Twitter API
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Getting started with Twython
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Summary
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12. Data Processing and Aggregation with MongoDB
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12. Data Processing and Aggregation with MongoDB
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Getting started with MongoDB
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Data preparation
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Group
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Aggregation framework
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Summary
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13. Working with MapReduce
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13. Working with MapReduce
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An overview of MapReduce
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Programming model
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Using MapReduce with MongoDB
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Filtering the input collection
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Grouping and aggregation
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Counting the most common words in tweets
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Summary
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14. Online Data Analysis with Jupyter and Wakari
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14. Online Data Analysis with Jupyter and Wakari
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Getting started with Wakari
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Getting started with IPython notebook
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Introduction to image processing with PIL
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Getting started with pandas
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Sharing your Notebook
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Summary
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15. Understanding Data Processing using Apache Spark
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15. Understanding Data Processing using Apache Spark
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Platform for data processing
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An introduction to the distributed file system
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An introduction to Apache Spark
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Summary

Data reduction methods

Many data scientists use large data size in volume for analysis, which takes a long time, though it is very difficult to analyze the data sometimes. In data analytics applications, if you use a large amount of data, it may produce redundant results. In order to overcome such difficulties, we can use data reduction methods.

Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. Reduced data size is very small in volume and comparatively original, hence, the storage efficiency will increase and at the same time we can minimize the data handling costs and will minimize the analysis time also.

We can use several types of data reduction methods, which are listed as follows:

  • Filtering and sampling

  • Binned algorithm

  • Dimensionality reduction

Filtering and sampling

In data reduction methods, filtering plays an important role. Filtering explains the process of detecting...

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