Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Practical Data Analysis
  • Table Of Contents Toc
Practical Data Analysis

Practical Data Analysis - Second Edition

By : Hector Cuesta, Dr. Sampath Kumar
3.5 (2)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Getting Started
Icon
1. Getting Started
Icon
Computer science
Icon
Artificial intelligence
Icon
Machine learning
Icon
Statistics
Icon
Mathematics
Icon
Knowledge domain
Icon
Data, information, and knowledge
Icon
The data analysis process
Icon
Quantitative versus qualitative data analysis
Icon
Importance of data visualization
Icon
What about big data?
Icon
Quantified self
Icon
Tools and toys for this book
Icon
Summary
2
2. Preprocessing Data
chevron up
Icon
2. Preprocessing Data
Icon
Data sources
Icon
Data scrubbing
Icon
Data formats
Icon
Data reduction methods
Icon
Getting started with OpenRefine
Icon
Summary
3
3. Getting to Grips with Visualization
Icon
3. Getting to Grips with Visualization
Icon
What is visualization?
Icon
Working with web-based visualization
Icon
Exploring scientific visualization
Icon
Visualization in art
Icon
The visualization life cycle
Icon
Visualizing different types of data
Icon
Getting started with D3.js
Icon
Interaction and animation
Icon
Data from social networks
Icon
An overview of visual analytics
Icon
Summary
4
4. Text Classification
Icon
4. Text Classification
Icon
Learning and classification
Icon
Bayesian classification
Icon
E-mail subject line tester
Icon
The data
Icon
The algorithm
Icon
Classifier accuracy
Icon
Summary
5
5. Similarity-Based Image Retrieval
Icon
5. Similarity-Based Image Retrieval
Icon
Image similarity search
Icon
Dynamic time warping
Icon
Processing the image dataset
Icon
Implementing DTW
Icon
Analyzing the results
Icon
Summary
6
6. Simulation of Stock Prices
Icon
6. Simulation of Stock Prices
Icon
Financial time series
Icon
Random Walk simulation
Icon
Monte Carlo methods
Icon
Generating random numbers
Icon
Implementation in D3js
Icon
Quantitative analyst
Icon
Summary
7
7. Predicting Gold Prices
Icon
7. Predicting Gold Prices
Icon
Working with time series data
Icon
Smoothing time series
Icon
Lineal regression
Icon
The data - historical gold prices
Icon
Nonlinear regressions
Icon
Summary
8
8. Working with Support Vector Machines
Icon
8. Working with Support Vector Machines
Icon
Understanding the multivariate dataset
Icon
Dimensionality reduction
Icon
Getting started with SVM
Icon
Summary
9
9. Modeling Infectious Diseases with Cellular Automata
Icon
9. Modeling Infectious Diseases with Cellular Automata
Icon
Introduction to epidemiology
Icon
The epidemic models
Icon
Modeling with Cellular Automaton
Icon
Simulation of the SIRS model in CA with D3.js
Icon
Summary
10
10. Working with Social Graphs
Icon
10. Working with Social Graphs
Icon
Structure of a graph
Icon
Social networks analysis
Icon
Acquiring the Facebook graph
Icon
Working with graphs using Gephi
Icon
Statistical analysis
Icon
Degree distribution
Icon
Transforming GDF to JSON
Icon
Graph visualization with D3.js
Icon
Summary
11
11. Working with Twitter Data
Icon
11. Working with Twitter Data
Icon
The anatomy of Twitter data
Icon
Using OAuth to access Twitter API
Icon
Getting started with Twython
Icon
Summary
12
12. Data Processing and Aggregation with MongoDB
Icon
12. Data Processing and Aggregation with MongoDB
Icon
Getting started with MongoDB
Icon
Data preparation
Icon
Group
Icon
Aggregation framework
Icon
Summary
13
13. Working with MapReduce
Icon
13. Working with MapReduce
Icon
An overview of MapReduce
Icon
Programming model
Icon
Using MapReduce with MongoDB
Icon
Filtering the input collection
Icon
Grouping and aggregation
Icon
Counting the most common words in tweets
Icon
Summary
14
14. Online Data Analysis with Jupyter and Wakari
Icon
14. Online Data Analysis with Jupyter and Wakari
Icon
Getting started with Wakari
Icon
Getting started with IPython notebook
Icon
Introduction to image processing with PIL
Icon
Getting started with pandas
Icon
Sharing your Notebook
Icon
Summary
15
15. Understanding Data Processing using Apache Spark
Icon
15. Understanding Data Processing using Apache Spark
Icon
Platform for data processing
Icon
An introduction to the distributed file system
Icon
An introduction to Apache Spark
Icon
Summary

Data scrubbing

Scrubbing data, also called data cleansing, is the process of correcting or removing data in a dataset that is incorrect, inaccurate, incomplete, improperly formatted, or duplicated.

The result of the data analysis process not only depends on the algorithms, it depends on the quality of the data. That's why the next step after obtaining the data, is data scrubbing. In order to avoid dirty data, our dataset should possess the following characteristics:

  • Correct

  • Completeness

  • Accuracy

  • Consistency

  • Uniformity

Dirty data can be detected by applying some simple statistical data validation and also by parsing the texts or deleting duplicate values. Missing or sparse data can lead you to highly misleading results.

Statistical methods

In this method, we need some context about the problem (knowledge domain) to find values that are unexpected and thus erroneous, even if the data type matches but the values are out of the range. This can be resolved by setting the values to an average or mean value...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Practical Data Analysis
End of Section 2
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon