Predictive Analytics E-Learning Training

Order this amazing, award-winning Predictive Analytics E-Learning Training course online, 1 year 24/7 access to extensive interactive videos, speeches, hands-on tasks and progress monitoring. After the course you will receive a certificate of attendance.

Predictive analytics can be a huge discriminator for business decision-making. Its application in marketing and sales, finance, HR, risk management and security, and business strategy might help in driving revenues, reducing costs, and providing competitive advantage to businesses. This path will introduce predictive analytics, key tools, and their application at the intermediate to advanced level to a wide variety of business and technical users at all levels in the organization. These courses are meant to deliver some key predictive analytics and modeling concepts, describe common tools and algorithms, and most importantly business applications of predictive analytics. The training itself is software independent and will be using one or several of these software tools in examples: Excel, Minitab, R, and RapidMiner.

Course content

Predictive Analytics & Big Data

Course: 38 Minutes

• Course Introduction
• What is Predictive Analytics?
• Shedding Light with Predictive Analytics
• Features of Predictive Analytics Models
• Big Data
• Big Data Considerations and Sources

Process & Application

Course: 59 Minutes

• Course Introduction 
• Sales, Marketing, and Operations
• Banking and Insurance
• Technology and Healthcare
• Government and Crime Prevention

Key Statistical Concepts

Course: 1 Hour, 2 Minutes

• Course Introduction
• Predictive Analytics and Statistics
• Types of Data
• Data Measurement Scales
• Descriptive vs. Inferential Statistics

Correlation & Regression

Course: 34 Minutes

• Course Introduction
• Overview of Correlation
• Correlation and Predictive Analytics
• Correlation and Causation
• Statistical Significance of Correlation
• Introduction to Regression Analysis
• Best Fit and Residual Analysis
• Logistic Regression for Predictive Analytics

Data Collection & Exploration

Course: 47 Minutes

• Course Introduction
• Choosing Predictive Data
• Timing and Quantity of Data
• Common Data Sources
• Extract, Transform, and Load Data
• Data Warehousing and Data Marts
• Relational Database Management System and Hadoop

Data Mining, Data Distributions, & Hypothesis Testing

Course: 43 Minutes

• Course Introduction
• Descriptive Data Analytics
• Prescriptive Data Analytics
• What Is Data Mining?
• Data Mining Concepts and Techniques
• Methods for Data Mining

Data Preprocessing

Course: 31 Minutes

• Course Introduction
• The Need to Clean Messy Data
• Outlier Identification and Handling
• Transforming, Normalizing, and Scaling Data
• Variable Partitioning
• Dummy Variables and Variable Removal
• Approaches for Handling Missing Data
• Imputation for Continuous Data

Data Reduction & Exploratory Data Analysis (EDA)

Course: 45 Minutes

• Course Introduction
• Dimension Reduction
• Principal Component Analysis for Numerical Data
• Information Theory Approach to Feature Selection
• Chi-square Feature Selection Method
• Wrapper Data Reduction Method
• Factor Analysis

K-Nearest Neighbor (k-NN) & Artificial Neural Networks

Course: 45 Minutes

• Course Introduction
• Overview of the k-NN Algorithm
• Distance and Weight Measures for Numeric Attributes
• Proximity Measures for Non-numeric Attributes
• Implementing the k-NN Algorithm

A/B Testing, Bayesian Networks, and Support Vector Machine

Course: 47 Minutes

• Course Introduction
• Overview of A/B Testing
• A/B Testing Features
• Implementing A/B Testing

Clustering Techniques

Course: 41 Minutes

• Course Introduction
• Introduction to Clustering
• Types of Clustering Techniques
• Proximity Measures for Clustering

Linear and Logistic Regression

Course: 49 Minutes

• Course Introduction
• Linear Regression Overview
• Sum of Squared Errors
• Ordinary Least Squares (OLS)
• Drawing Inferences

Text Mining & Social Network Analysis

Course: 55 Minutes

• Course Introduction
• Overview of Text Mining
• Assigning within Document Predictor Variables
• Text Normalization
• Assigning across Document Predictor Variables
• Term Frequency and Inverse Document Frequency
• Sentiment Analysis
• Text Mining Applications

Time Series Modeling

Course: 38 Minutes

• Course Introduction
• Time Series Overview
• Stationary and Nonstationary Data Series
• Time Series Decomposition

Machine Learning, Propensity Score, & Segmentation Modeling

Course: 54 Minutes

• Course Introduction
• Machine Learning Overview
• Machine Learning Tools and Process
• Deep Learning
• Supervised vs. Unsupervised Methods
• Ensemble Techniques for Machine Learning
• Ensemble Performance Considerations and Metrics

Random Forests & Uplift Models

Course: 40 Minutes

• Course Introduction
• Random Forest Overview
• Decision Tree Characteristics
• Random Forest Model Error Measurement
• Random Forest Model Concepts

Model Life Cycle Management

Course: 36 Minutes

• Course Introduction
• Understanding Business Objectives and Data
• Model Development and Deployment
• Model Deployment Planning
• Stakeholder Management
• User Training and Model Documentation
• Model Recalibration and Maintenance
• Business Validation and Benchmarks

Model Development, Validation, & Evaluation

Course: 1 Hour, 1 Minute

• Course Introduction
• Model Building Process and Data Discovery
• Data Cleaning and Preparation
• Data Preprocessing and Model Building