Data Private

Predictive Analytics (DATA301)

4 days

Build predictive models in Python with regression, classification, time series forecasting, and clustering, then validate them and present the results.

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Course Overview

This course is designed for participants who need to move beyond descriptive reporting and build predictive models that are able to estimate likely outcomes from historical data. It covers the full predictive modeling lifecycle, from preparing and diagnosing data in Python through supervised and unsupervised modeling, model validation and evaluation, and deployment considerations, applied through hands-on labs and real-world case studies to business problems across finance, marketing, and operations.

Course Benefits

Explain how predictive analytics relates to data mining and machine learning, and distinguish supervised from unsupervised methods across the modeling workflow.
Evaluate a business problem and select the appropriate predictive technique, including regression, classification, clustering, and time series forecasting.
Prepare model-ready data in Python, then train, validate, and score models using industry-standard performance metrics.
Interpret model outputs, assess bias and ethical risk, and communicate findings and recommendations to technical and non-technical stakeholders.

Delivery Methods

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

Course Outline

Day 1: Python and Data Foundations
1. Python Foundations for Analytics: Introduction to Python syntax, variables, control flow, and functions using Jupyter notebooks for data work.
2. Working with Pandas DataFrames: Loading, exploring, filtering, and transforming tabular data using the Pandas library.
3. Cleaning and Combining Data: Techniques for identifying and repairing errors, handling missing values, and merging multiple tables.
Day 2: From Raw Data to Model-Ready
1. Exploring and Diagnosing Data: Data exploration, correlation analysis, and establishing prediction baselines before model training.
2. The Predictive Modeling Lifecycle: Overview of the end-to-end lifecycle, defining predictive analytics, data mining, and machine learning concepts.
3. Preparing Data for Machine Learning: Converting categorical variables, handling missing values, and scaling features for model readiness.
Day 3: Building and Evaluating Models
1. Regression Modeling (Mini-Case 1: Finance): Linear regression for predicting continuous outcomes like revenue, sales, or financial forecasts.
2. Classification Modeling (Mini-Case 2: Marketing): Logistic regression for binary outcomes like default, churn, or purchase.
3. Evaluating and Validating Models: Performance scoring, cross-validation, confusion reporting, and ethical considerations in model evaluation.
4. Improving Models: Ensembles and Tuning: Ensemble methods including decision trees, random forests, and parameter tuning via grid search.
Day 4: Forecasting, Segmentation, and Delivery
1. Foundational Time Series Forecasting: Forecasting sequential data using time-based patterns, seasonality, and trend decomposition.
2. Clustering: Unsupervised grouping methods to discover natural segments in customer, product, or geographic data.
3. Communicating and Deploying Results: Translating model findings into stakeholder presentations and understanding deployment considerations.

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:

Data Analysis Fundamentals or equivalent experience (proficiency with spreadsheet navigation, basic data manipulation, and descriptive statistics).

Experience in the following would be useful for this Data class:

Participants who have completed Financial Data Analytics will find the opening Python and data preparation sessions serve as a helpful review, while the predictive modeling content is substantially expanded and does not assume prior experience.

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