Data Private

Introduction to Predictive Analytics (DATA301)

3 days

Data

Learn to build, train, and validate models that identify patterns and forecast outcomes using regression, classification, clustering, and time series methods.

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Course Overview

Learn to build, train, and validate models that identify patterns and forecast outcomes using regression, classification, clustering, and time series methods. This course covers the full predictive modeling lifecycle, from data preparation and feature engineering through model selection, training, validation, and interpretation. Includes hands-on case studies across finance, operations, and marketing.

Course Benefits

Define core concepts in predictive analytics, data mining, and machine learning, and articulate how predictive methods extend beyond descriptive and diagnostic analysis.
Assess organizational problems and select appropriate modeling techniques, including linear regression, logistic regression, decision trees, and time series models.
Prepare data for modeling by handling missing values, encoding categorical variables, engineering features, and splitting data into training and test sets.
Build, train, and validate predictive models using tools such as Python (scikit-learn), R, or comparable platforms.
Interpret model outputs, including coefficients, accuracy metrics, feature importance, and residual analysis, and present findings to non-technical stakeholders.
Evaluate the ethical implications, potential biases, and organizational risks associated with deploying predictive models.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Data class:

Familiarity with basic statistics and at least one data programming language (R or Python).

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