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. 

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. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We can work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.  

Day 1: Introduction to Forecasting 

Overview of Forecasting 

• Importance and applications of forecasting 
• Types of forecasting problems 

Time Series Analysis 

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

Forecasting Methods 

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

Regression-Based Forecasting 

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

Day 2: Machine Learning-Based Forecasting 

Machine Learning for Forecasting 

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

Ensemble Methods for Forecasting 

• Bagging and random forests 
• Boosting methods 
• Stacking models 

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 

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 

Introduction to Behavioral Analysis 

• Understanding behavioral data 
• Applications of behavioral analysis 

Clustering and Segmentation 

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

Sentiment Analysis 

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

Behavioral Pattern Recognition 

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

Introduction to What-If Scenarios 

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

Modeling What-If Scenarios in Python 

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