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

Forecasting, Behavioral Analysis, and What-If Scenarios with Python is an advanced three-day course that combines the power of forecasting, behavioral analysis, and what-if scenario analysis using Python. The course equips data analysts, data scientists, and business professionals with the skills and techniques required to analyze historical data, identify behavioral patterns, forecast future trends, and conduct what-if scenario analysis to evaluate potential outcomes.Working in a hands-on learning environment led by out expert practitioner, you’ll explore advanced Python libraries and techniques for forecasting, behavioral analysis, and what-if scenario modeling. The course covers advanced forecasting methods such as time series analysis, regression-based forecasting, and machine learning-based forecasting. Participants will also learn how to analyze behavioral patterns through clustering, segmentation, and sentiment analysis. In addition, the course introduces what-if scenarios, enabling participants to simulate and evaluate different scenarios to make informed decisions.

  • Price: $2,395.00
  • Duration: 3 days
  • Delivery Methods: Virtual
Date Time Price Option
Please contact us at info@toptalentlearning.com or 469-721-6100 for this course schedule.
For questions call: (469) 721-6100
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  • Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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Course Topics / Agenda

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

  1. Overview of Forecasting
  • Importance and applications of forecasting
  • Types of forecasting problems
  1. Time Series Analysis
  • Introduction to time series data
  • Handling time series data in Python
  • Exploratory data analysis for time series
  1. Forecasting Methods
  • Moving averages
  • Exponential smoothing methods
  • ARIMA models
  • Seasonal decomposition of time series
  1. Regression-Based Forecasting
  • Introduction to regression analysis
  • Building regression models for forecasting
  • Evaluating regression models

Day 2: Machine Learning-Based Forecasting

  1. Machine Learning for Forecasting
  • Introduction to machine learning algorithms for forecasting
  • Feature engineering for forecasting
  • Training and evaluating machine learning models
  1. Ensemble Methods for Forecasting
  • Bagging and random forests
  • Boosting methods
  • Stacking models
  1. 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
  1. 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

  1. Introduction to Behavioral Analysis
  • Understanding behavioral data
  • Applications of behavioral analysis
  1. Clustering and Segmentation
  • Clustering techniques for behavioral analysis
  • Segmentation of customers or users based on behavior
  • Practical examples and case studies
  1. Sentiment Analysis
  • Introduction to sentiment analysis
  • Text preprocessing techniques
  • Sentiment analysis using Python libraries
  1. Behavioral Pattern Recognition
  • Analyzing sequential behavioral data
  • Hidden Markov Models (HMMs) for behavior recognition
  • Application of behavior recognition models
  1. Introduction to What-If Scenarios
  • Understanding what-if scenario analysis
  • Identifying key variables and factors
  • Creating scenarios and defining assumptions
  1. Modeling What-If Scenarios in Python
  • Implementing what-if scenarios using Python libraries
  • Simulating different scenarios and outcomes
  • Analyzing and evaluating scenario results

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.

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.

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.

Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.

Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.

You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.

If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.

You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.