Forecasting, Behavioral Analysis, and What-If Scenarios with Python (TTPS4883)

Course Objectives

Learning Objectives

This course is approximately 50% hands-on, combining expert lecture with real-world demonstrations and group discussions with machine-based practical labs and exercises.  

Working in a hands-on learning environment, guided by our expert team, attendees will learn to:

• Understand advanced concepts and techniques in forecasting, behavioral analysis, and what-if scenarios.
• Gain proficiency in applying Python libraries and tools for forecasting, behavioral analysis, and what-if scenario modeling.
• Develop forecasting models using time series analysis, regression, and machine learning algorithms.
• Analyze and interpret behavioral patterns through clustering, segmentation, and sentiment analysis. • Conduct what-if scenario analysis to evaluate potential outcomes and make informed decisions.
• Gain practical experience through hands-on labs and exercises using real-world datasets.

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional python, data science, AI / machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

Audience

This course is intended for data analysts, data scientists, business analysts, and professionals who want to leverage Python for forecasting, behavioral analysis, and what-if scenario analysis tasks. Participants should have a solid understanding of Python programming and basic data manipulation skills.

Pre-Requisites

In order to be successful in the course you should have:

• Basic understanding of any programming language: Familiarity with concepts like variables, loops, and functions would be beneficial, even if not in Python.
• Fundamental knowledge of Data Science: A general understanding of what data science is and why it's valuable would help provide context for the Python and data wrangling skills taught in this course.
• Comfort with basic Mathematical Concepts: As Python is heavily used in data analysis, a comfort level with basic math and statistics would be beneficial, though advanced mathematical skills are not necessary.

Take Before:

• TTPS4873      Fast Track to Python in Data Science (3 days)

Follow On Courses:

• TTPS4876      Next Level Python in Data Science (Intermediate) (5 days)
• TTML5506-P Machine Learning Essentials with Python (3 days)

Course Agenda

Day 1: Introduction to Forecasting

1. Overview of Forecasting

• Importance and applications of forecasting
• Types of forecasting problems

2. Time Series Analysis

• Introduction to time series data
• Handling time series data in Python
• Exploratory data analysis for time series

3. Forecasting Methods

• Moving averages
• Exponential smoothing methods
• ARIMA models
• Seasonal decomposition of time series

4. Regression-Based Forecasting

• Introduction to regression analysis
• Building regression models for forecasting
• Evaluating regression models

Day 2: Machine Learning-Based Forecasting

5. Machine Learning for Forecasting

• Introduction to machine learning algorithms for forecasting
• Feature engineering for forecasting
• Training and evaluating machine learning models

6. Ensemble Methods for Forecasting

• Bagging and random forests
• Boosting methods
• Stacking models

7. Neural Networks for Time Series Forecasting

• Introduction to neural networks
• Building and training neural network models for forecasting
• Time series forecasting with recurrent neural networks (RNNs) and LSTM networks

8. Evaluating and Improving Forecasting Models

• Performance metrics for forecasting
• Cross-validation and model evaluation techniques
• Techniques for model improvement and optimization

Day 3: Behavioral Analysis and What-If Scenarios

9. Introduction to Behavioral Analysis

• Understanding behavioral data
• Applications of behavioral analysis

10. Clustering and Segmentation

• Clustering techniques for behavioral analysis
• Segmentation of customers or users based on behavior
• Practical examples and case studies

11. Sentiment Analysis

• Introduction to sentiment analysis
• Text preprocessing techniques
• Sentiment analysis using Python libraries

12. Behavioral Pattern Recognition

• Analyzing sequential behavioral data
• Hidden Markov Models (HMMs) for behavior recognition
• Application of behavior recognition models

13. Introduction to What-If Scenarios

• Understanding what-if scenario analysis
• Identifying key variables and factors
• Creating scenarios and defining assumptions

14. Modeling What-If Scenarios in Python

• Implementing what-if scenarios using Python libraries
• Simulating different scenarios and outcomes
• Analyzing and evaluating scenario results

Course Materials

Setup Made Simple! Learning Experience Platform (LXP)

All applicable course software, digital courseware files or course notes, labs, data sets and solutions, live coaching support channels and rich extended learning and post training resources are provided for you in our “easy access, no install required” online Learning Experience Platform (LXP), remote lab and content environment. Access periods vary by course. We’ll collaborate with you to ensure your team is set up and ready to go well in advance of the class. Please inquire about set up details and options for your specific course of interest.