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DAT510 - Foundations of Data

(SNHU-DAT510.AP1)
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1

Introduction

  • About This eBook
  • Foreword
2

Introduction to Analytics

  • What’s in a Name?
  • Why the Sudden Popularity of Analytics and Data Science?
  • The Application Areas of Analytics
  • The Main Challenges of Analytics
  • A Longitudinal View of Analytics
  • A Simple Taxonomy for Analytics
  • The Cutting Edge of Analytics: IBM Watson
  • Summary
  • References
3

Introduction to Predictive Analytics and Data Mining

  • What Is Data Mining?
  • What Data Mining Is Not
  • The Most Common Data Mining Applications
  • What Kinds of Patterns Can Data Mining Discover?
  • Popular Data Mining Tools
  • The Dark Side of Data Mining: Privacy Concerns
  • Summary
  • References
4

Standardized Processes for Predictive Analytics

  • The Knowledge Discovery in Databases (KDD) Process
  • Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • SEMMA
  • SEMMA Versus CRISP-DM
  • Six Sigma for Data Mining
  • Which Methodology Is Best?
  • Summary
  • References
5

Data and Methods for Predictive Analytics

  • The Nature of Data in Data Analytics
  • Preprocessing of Data for Analytics
  • Data Mining Methods
  • Prediction
  • Classification
  • Decision Trees
  • Cluster Analysis for Data Mining
  • k-Means Clustering Algorithm
  • Association
  • Apriori Algorithm
  • Data Mining and Predictive Analytics Misconceptions and Realities
  • Summary
  • References
6

Algorithms for Predictive Analytics

  • Naive Bayes
  • Nearest Neighbor
  • Similarity Measure: The Distance Metric
  • Artificial Neural Networks
  • Support Vector Machines
  • Linear Regression
  • Logistic Regression
  • Time-Series Forecasting
  • Summary
  • References
7

Advanced Topics in Predictive Modeling

  • Model Ensembles
  • Bias–Variance Trade-off in Predictive Analytics
  • Imbalanced Data Problems in Predictive Analytics
  • Explainability of Machine Learning Models for Predictive Analytics
  • Summary
  • References
8

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Natural Language Processing
  • Text Mining Applications
  • The Text Mining Process
  • Text Mining Tools
  • Topic Modeling
  • Sentiment Analysis
  • Summary
  • References
9

Big Data for Predictive Analytics

  • Where Does Big Data Come From?
  • The Vs That Define Big Data
  • Fundamental Concepts of Big Data
  • The Business Problems That Big Data Analytics Addresses
  • Big Data Technologies
  • Data Scientists
  • Big Data and Stream Analytics
  • Data Stream Mining
  • Summary
  • References
10

Deep Learning and Cognitive Computing

  • Introduction to Deep Learning
  • Basics of “Shallow” Neural Networks
  • Elements of an Artificial Neural Network
  • Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Networks and Long Short-Term Memory Networks
  • Computer Frameworks for Implementation of Deep Learning
  • Cognitive Computing
  • Summary
  • References
A

Appendix A: KNIME and the Landscape of Tools for Business Analytics and Data Science

  • Project Constraints: Time and Money
  • The Learning Curve
  • The KNIME Community
  • Correctness and Flexibility
  • Extensive Coverage of Data Science Techniques
  • Data Science in the Enterprise
  • Summary and Conclusions
  • Acknowledgment
B

Appendix B: Videos

  • Introduction to Predictive Analytics
  • Introduction to Predictive Analytics and Data Mining
  • The Data Mining Process
  • Data and Methods in Data Mining
  • Data Mining Algorithms
  • Text Analytics and Text Mining
  • Big Data Analytics
  • Predictive Analytics Best Practices
  • Summary

1

Introduction to Analytics

  • Reading Data from a CSV File
  • Extracting Data with Database Queries
  • Consolidating Data from Multiple Sources
  • Handling Irregular and Unusable Data
  • Correcting Data Formats
  • De-duplicating Data
  • Handling Textual Data
  • Loading Data into a Database
  • Loading Data into a DataFrame
2

Introduction to Predictive Analytics and Data Mining

  • Creating a Decision Tree in Python
  • Creating a Decision Tree in KNIME
  • Examining Data
  • Exploring the Underlying Distribution of Data
3

Standardized Processes for Predictive Analytics

  • Handling Missing Values
  • Analyzing Data Using Histograms
4

Data and Methods for Predictive Analytics

  • Running k-Means Clustering Algorithm in KNIME
5

Algorithms for Predictive Analytics

  • Using the k-Nearest Neighbor Algorithm
  • Using ANN and SVM for Prediction Type Analytics Problems
  • Implementing Linear Regression in Python
  • Implementing Linear Regression Model in KNIME
  • Analyzing Data Using Box Plots and Violin Plots
  • Analyzing Data Using Scatter Plots and Line Plots
  • Analyzing Data Using Bar Charts
  • Analyzing Data Using HeatMaps
  • Encoding Data
  • Discretizing Variable
  • Splitting and Removing Features
  • Performing Dimensionality Reduction
  • Applying Transformation Functions to a Dataset
6

Advanced Topics in Predictive Modeling

  • Showcasing Better Practices With a Customer Churn Analysis
  • Training a Linear Regression Model
  • Training Regression Trees and Ensemble Models
  • Tuning Regression Models
  • Evaluating Regression Models
  • Building an ML Pipeline
7

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Performing Topic Modeling
  • Performing Sentiment Analysis
  • Training a Logistic Regression Model
  • Training a k-NN Model
  • Training an SVM Classification Model
  • Training a Naïve Bayes Model
  • Training Classification Decision Trees and Ensemble Models
  • Training a k-Means Clustering Model
  • Training a Hierarchical Clustering Model
  • Tuning Clustering Models
  • Evaluating Clustering Models

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