Course: Predictive Modelling and Analytics

$519.09 incl. vat

duration: 22 hours |

Language: English (US) |

access duration: 180 days |

In Onbeperkt Leren


In this online Predictive Modeling and Analytics course you discover the possibilities and advantages of Predictive Modeling and Analytics. In the course we discuss the possibilities of predictive analytics in combination with Big Data. You learn how predictive analytics is used within different industries and which processes and roles are involved.

In addition, we will continue with the possibilities of descriptive, predictive and prescriptive analytics. We focus on the techniques used for predictive analytics, what the possibilities are of data clustering, which different regression models are available and what the possibilities are of text mining and social networks analyzes.

Finally, we will continue with the possibilities of time series modeling, the possibilities of machine learning, which tools you can use for it and which methods are used for machine learning. We also consider the different decision phases of the life cycle management model and the different processes of the predictive analytics model.

Among topics this course covers are data mining, distribution models, hypothesis testing, the K-Nearest Neighbor (k-NN) algorithm, artificial neural network modeling, A / B testing, Bayesian Networks, support vector machine (SVM), K-Means. , DBSCAN, regression models, random forests, uplift models and far more.


After successful completion you are familiar with the possibilities of predictive analytics, modeling and its benefits.


No specific prior knowledge is required, general knowledge about statistics is an advantage.

Target audience

Business Analyst, Marketeer, Manager, Data analist


Predictive Modelling and Analytics

22 hours

Predictive Analytics & Big Data

Predictive analytics uses techniques, such as statistics and machine learning, to build predictive models, often using big data to test and validate these models. Explore key features of predictive analytics and big data.

Process & Application

For any organization, predictive analytics is quickly becoming a key component for organizational success. Discover the application of predictive analytics to various industries, and explore the process and roles involved.

Key Statistical Concepts

Predictive analytics involves a wide range of statistical tools and methods that allow an analyst to build a powerful predictive model. Explore the importance of statistics and probability theory in predictive analytics.

Correlation & Regression

Predictive analytics involves widely accepted tools and techniques that enable organizations to make informed decisions regarding potential future events. Explore how correlation and regression are employed in predictive analytics.

Data Collection & Exploration

Data Collection & Exploration

Data Mining, Data Distributions, & Hypothesis Testing

Purposeful information can be extracted from large data sets to determine what has, could, or should happen. Explore descriptive, predictive, and prescriptive analytics, including data mining, distribution models, and hypothesis testing.

Data Preprocessing

Predictive analytics delivers the greatest value when the data being modeled is relevant to the business goals. Explore the preprocessing phase of data collection to provide the best predictive model.

Data Reduction & Exploratory Data Analysis (EDA)

With predictive analytics, relevant data

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

Choosing the appropriate technique to deliver confident predictions can be challenging for analysts. Explore algorithms used for predictive analytics, including the K-Nearest Neighbor (k-NN) algorithm and artificial neural network modeling.

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

At the core of predictive analytics lie the models used to make predictions after the data has been collected and preprocessed. Explore predictive techniques, including A/B testing, Bayesian Networks, and the support vector machine (SVM).

Clustering Techniques

The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Explore how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

Linear and Logical Regression

Regression modeling investigates relationships between dependent and independent variables and is heavily relied upon for predictive analytics and data mining applications. Explore both the linear and logistic regression models.

Text Mining & Social Network Analysis

Text Mining & Social Network Analysis

Time Series Modeling

Time series modeling is a common forecasting method, such as making stock market predictions. It has made its way into many varied applications, including inventory management and healthcare. Explore the features of time series modeling.

Machine Learning, Propensity Score, & Segmentation Modeling

Both supervised and unsupervised machine learning techniques are at the forefront of the predictive analytics and data mining industry. Discover machine learning features and tools, and explore propensity scoring and segmentation modeling.

Random Forests & Uplift Models

Nestled within machine learning are ensemble techniques that enable the combination of multiple models to reduce prediction error and improve forecasting ability. Explore machine learning methods, including random forests and uplift models.

Model Development, Validation, & Evaluation

Analytic model management ensures that models are not only superior to alternatives, but they also meet or exceed current business needs. Explore the process of building, validating, and evaluating a predictive analytics model.

Model Life Cycle Management

Analyst must continuously manage analytical models, such as monitoring performance over time and interacting with various stakeholders. Explore the operational decision making stages of model life cycle management.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 


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