Predictive Analytics E-Learning Ausbildung

Bestellen Sie diesen großartigen, preisgekrönten Predictive Analytics E-Learning-Schulungs kurs online, 1 Jahr rund um die Uhr Zugriff auf umfangreiche interaktive Videos, Reden, praktische Aufgaben und Fortschrittsüberwachung. Nach dem Kurs erhalten Sie eine Teilnahmebescheinigung.

Kursinhalt

Predictive Analytics & Big Data

Course: 38 Minutes

• Course Introduction--Not StartedActions
• 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--Not StartedActions
• 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