Predictive Analytics For Dummies, 2ed

In stock
Author : Bari
Price : INR 599
ISBN 13 : 9788126567935
Pages : 464
Type : Paperbound

Predictive Analytics For Dummies, 2e will help the you understand the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. You will learn how to incorporate algorithms through discovering data models, identifying similarities and relationships in your data, and how to predict the future through data classification. You will develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get stakeholder buy-in. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies.

 

Introduction  

 

Part 1: Getting Started with Predictive Analytics

Chapter 1: Entering the Arena

  • Exploring Predictive Analytics
  • Mining data
  • Highlighting the model
  • Adding Business Value
  • Endless opportunities
  • Empowering your organization
  • Starting a Predictive Analytic Project
  • Business knowledge
  • Data-science team and technology
  • The Data
  • Ongoing Predictive Analytics
  • Forming Your Predictive Analytics Team
  • Hiring experienced practitioners
  • Demonstrating commitment and curiosity
  • Surveying the Marketplace
  • Responding to big data
  • Working with big data

 

Chapter 2: Predictive Analytics in the Wild

  • Online Marketing and Retail
  • Recommender systems
  • Personalized shopping on the Internet
  • Implementing a Recommender System
  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommender systems
  • Target Marketing
  • Targeting using predictive modeling
  • Uplift modeling
  • Personalization
  • Online customer experience
  • Retargeting
  • Implementation
  • Optimizing using personalization
  • Similarities of Personalization and Recommendations
  • Content and Text Analytics

 

Chapter 3: Exploring Your Data Types and Associated Techniques

  • Recognizing Your Data Types
  • Structured and unstructured data
  • Static and streamed data
  • Identifying Data Categories
  • Attitudinal data
  • Behavioral data
  • Demographic data
  • Generating Predictive Analytics
  • Data-driven analytics
  • User-driven analytics
  • Connecting to Related Disciplines
  • Statistics
  • Data mining
  • Machine learning

 

Chapter 4: Complexities of Data

  • Finding Value in Your Data
  • Delving into your data
  • Data validity
  • Data variety
  • Constantly Changing Data
  • Data velocity
  • High volume of data
  • Complexities in Searching Your Data
  • Keyword-based search
  • Semantic-based search
  • Contextual search
  • Differentiating Business Intelligence from Big-Data Analytics
  • Exploration of Raw Data
  • Identifying data attributes
  • Exploring common data visualizations
  • Tabular visualizations
  • Word clouds
  • Flocking birds as a novel data representation
  • Graph charts
  • Common visualizations

 

Part 2: Incorporating Algorithms in Your Models

Chapter 5: Applying Models

  • Modeling Data
  • Models and simulation
  • Categorizing models
  • Describing and summarizing data
  • Making better business decisions
  • Healthcare Analytics Case Studies
  • Google Flu Trends
  • Cancer survivability predictors
  • Social and Marketing Analytics Case Studies
  • Target store predicts pregnant women
  • Twitter-based predictors of earthquakes
  • Twitter-based predictors of political campaign outcomes
  • Tweets as predictors for the stock market
  • Predicting variation of stock prices from news articles
  • Analyzing New York City's bicycle usage
  • Predictions and responses
  • Data compression
  • Prognostics and its Relation to Predictive Analytics
  • The Rise of Open Data

 

Chapter 6: Identifying Similarities in Data

  • Explaining Data Clustering
  • Converting Raw Data into a Matrix
  • Creating a matrix of terms in documents
  • Term selection
  • Identifying Groups in Your Data
  • K-means clustering algorithm
  • Clustering by nearest neighbors
  • Density-based algorithms
  • Finding Associations in Data Items
  • Applying Biologically Inspired Clustering Techniques
  • Birds flocking: Flock by Leader algorithm
  • Ant colonies

 

Chapter 7: Predicting the Future Using Data Classification

  • Explaining Data Classification
  • Introducing Data Classification to Your Business
  • Exploring the Data-Classification Process
  • Using Data Classification to Predict the Future
  • Decision trees
  • Algorithms for Generating Decision Trees
  • Support vector machine
  • Ensemble Methods to Boost Prediction Accuracy
  • Naïve Bayes classification algorithm
  • The Markov Model
  • Linear regression
  • Neural networks
  • Deep Learning

 

Part 3: Developing A Roadmap

Chapter 8: Convincing Your Management to Adopt Predictive Analytics

  • Making the Business Case
  • Gathering Support from Stakeholders
  • Presenting Your Proposal

 

Chapter 9: Preparing Data

  • Listing the Business Objectives
  • Processing Your Data
  • Identifying the data
  • Cleaning the data
  • Generating any derived data
  • Reducing the dimensionality of your data
  • Applying principal component analysis
  • Leveraging singular value decomposition
  • Working with Features
  • Structuring Your Data
  • Extracting, transforming and loading your data
  • Keeping the data up to date
  • Outlining testing and test data

 

Chapter 10: Building a Predictive Model

  • Getting Started
  • Defining your business objectives
  • Preparing your data
  • Choosing an algorithm
  • Developing and Testing the Model
  • Going Live with the Model

 

Chapter 11: Visualization of Analytical Results

  • Visualization as a Predictive Tool
  • Evaluating Your Visualization
  • Visualizing Your Model's Analytical Results
  • Visualizing hidden groupings in your data
  • Visualizing data classification results
  • Visualizing outliers in your data
  • Visualization of Decision Trees
  • Visualizing predictions
  • Novel Visualization in Predictive Analytics
  • Big Data Visualization Tools
  • Tableau
  • Google Charts
  • Plotly
  • Infogram

 

Part 4: Programming Predictive Analytics

Chapter 12: Creating Basic Prediction Examples

  • Installing the Software Packages
  • Installing Python
  • Installing the machine-learning module
  • Installing the dependencies
  • Preparing the Data
  • Making Predictions Using Classification Algorithms
  • Creating a supervised learning model with SVM
  • Creating a supervised learning model with logistic regression
  • Creating a supervised learning model with random forest
  • Comparing the classification models

 

Chapter 13: Creating Basic Examples of Unsupervised Predictions

  • Getting the Sample Dataset
  • Using Clustering Algorithms to Make Predictions
  • Comparing clustering models
  • Creating an unsupervised learning model with K-means
  • Creating an unsupervised learning model with DBSCAN
  • Creating an unsupervised learning model with mean shift

 

Chapter 14: Predictive Modeling with R

  • Programming in R
  • Installing R
  • Installing RStudio
  • Getting familiar with the environment
  • Learning just a bit of R
  • Making Predictions Using R
  • Predicting using regression
  • Using classification to predict
  • Classification by random forest

 

Chapter 15: Avoiding Analysis Traps

  • Data Challenges
  • Outlining the limitations of the data
  • Dealing with extreme cases (outliers)
  • Data smoothing
  • Curve fitting
  • Keeping the assumptions to a minimum
  • Analysis Challenges

 

Part 5: Executing Big Data

Chapter 16: Targeting Big Data

  • Major Technological Trends in Predictive Analytics
  • Exploring predictive analytics as a service
  • Aggregating distributed data for analysis
  • Real-time data-driven analytics
  • Applying Open-Source Tools to Big Data
  • Apache Hadoop
  • Apache Spark

 

Chapter 17: Getting Ready for Enterprise Analytics

  • Analytics as a Service
  • Google Analytics
  • IBM Watson
  • Microsoft Revolution R Enterprise
  • Preparing for a Proof-of-Value of Predictive Analytics Prototype
  • Prototyping for predictive analytics
  • Testing your predictive analytics model

 

Part 6: The Part of Tens

Chapter 18: Ten Reasons to Implement

  • Predictive Analytics

 

Chapter 19: Ten Steps to Build a Predictive Analytic Model

 

Index

Dr. Anasse Bari has over five years of large-scale software architecture experience in designing and implementing software systems under different platforms. He is a Fulbright scholar, a software engineer, and a data mining expert. Dr. Bari works for the World Bank Group in Washington DC.

 

Mohamed Chaouchi has conducted extensive research using predictive analytics and data mining in both the health and financial domains. He holds a patent for a data-mining platform to analyze cancer development. His technical expertise includes service-oriented architecture, web services, and application security. 

 

Tommy Jung is a software engineer who has been developing software applications for over 15 years. He has worked extensively on creating analytic's tools for natural language processing in his days at a speech recognition company and then turned his focus to the web analytics, A/B testing, and stock analysis. He has worked at several Fortune 500 companies.

 

 

Our primary target audience is managers and marketers of all levels who want to increase returns on their investments and improve customers' response rates. IT professionals who want to extend their expertise to predictive analytics will also find the information in this book valuable.

 

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