Certified Predictive Analytics Professional

Course details

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.

Vskills Predictive analytics certificate course will help you to learn 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.

Why should one take Predictive Analytics Certification?
This Course is intended for professional and graduates wanting to excel in their chosen areas. It is also well suited for those who want to learn key predictive analytics and modeling concepts, common tools and algorithms, and most importantly business applications of predictive analytics.

The online training course itself is software independent and will be using one or several of these software tools in examples: Excel, Minitab, R, and RapidMiner.

Predictive Analytics & Big Data

• Predictive Analytics and Big Data
• Predictive Analytics Evolution

Process & Application

• Applications of Predictive Analytics
• Predictive Analytics Process and Technology
• Practice: Identifying the Type of Analytics

Key Statistical Concepts

• Importance of Statistics in Analytics
• Overview of Probability Theory
• Using Statistics in Predictive Analytics

Correlation & Regression

• Introduction
• Practice: Applying Statistical Approaches

Data Collection & Exploration

• Data Needs and Sources
• Data Collection and Exploration

Data Mining, Data Distributions, & Hypothesis Testing

• Data Mining and Analytics
• Data Distributions and Hypothesis Testing
• Practice: Data Mining Methods

Data Preprocessing

• Pre-processing Data

Data Reduction & Exploratory Data Analysis (EDA)

• Data Reduction with PCA and Factor Analysis
• Tools for Exploratory Data Analysis (EDA)
• Practice: Using PCA for Feature Selection

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

• A/B Testing
• Naïve Bayes and Bayesian Belief Networks
• Support Vector Machines
• Practice: Applying Predictive Approaches

Artificial Neural Networks

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

Clustering Techniques

• Using Clustering Techniques
• K-Means Clustering
• Hierarchical Clustering and DBSCAN

Linear and Logical Regression

• Linear Regression
• Logistic Regression
• Practice: Linear Regression Inference

Text Mining & Social Network Analysis

• Text Mining
• Social Network and Media Analytics

Time Series Modeling

• Introduction to Time Series
• Time Series Forecasting Models
• Practice: Time Series Modeling Concepts

Machine Learning, Propensity Score, & Segmentation Modeling

• Machine Learning
• Propensity Score and Segmentation Modeling

Random Forests & Uplift Models

• Random Forests
• Uplift Models
• Practice: Advanced Predictive Tools

Model Life Cycle Management

• Introducing Model Life Cycle Management

Model Development, Validation, & Evaluation

• Model Building
• Model Considerations and Validation
• Model Evaluation
• Practice: Classification Model Performance

Updated on 01 February, 2019