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: 1 day
- Delivery Methods: Virtual
| Date | Time | Price | Option |
|---|---|---|---|
| 12/16/2024 | 10:00 AM - 06:00 PM CT | $2,395.00 |
For questions call: (469) 721-6100
Why choose
TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
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
- 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
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.
