Module #1 Introduction to Data-Driven Innovation Overview of the importance of data-driven innovation and predictive modeling in business
Module #2 Predictive Modeling Fundamentals Basic concepts of predictive modeling, including types of models and evaluation metrics
Module #3 Data Preprocessing for Modeling Importance of data preprocessing, handling missing values, and feature scaling
Module #4 Supervised Learning Algorithms Overview of supervised learning algorithms, including regression and classification
Module #5 Unsupervised Learning Algorithms Overview of unsupervised learning algorithms, including clustering and dimensionality reduction
Module #6 Model Evaluation and Selection Techniques for evaluating and selecting the best predictive model
Module #7 Data Visualization for Insights Using data visualization to gain insights and communicate results
Module #8 Introduction to Python for Data Science Overview of Python libraries and tools for data science, including Pandas and Scikit-learn
Module #9 Working with Big Data Techniques for working with large datasets, including data storage and processing
Module #10 Predictive Modeling in Business Applications of predictive modeling in business, including customer churn prediction and credit risk assessment
Module #11 Decision Trees and Random Forests In-depth look at decision trees and random forests, including implementation in Python
Module #12 Support Vector Machines In-depth look at support vector machines, including implementation in Python
Module #13 Neural Networks for Predictive Modeling Introduction to neural networks for predictive modeling, including implementation in Python
Module #14 Ensemble Methods Techniques for combining multiple models, including bagging and boosting
Module #15 Time Series Forecasting Predictive modeling for time series data, including ARIMA and Prophet
Module #16 Natural Language Processing for Text Analysis Using NLP for text analysis, including sentiment analysis and topic modeling
Module #17 Case Study:Predictive Modeling in Healthcare Real-world application of predictive modeling in healthcare, including disease diagnosis and treatment outcomes
Module #18 Case Study:Predictive Modeling in Marketing Real-world application of predictive modeling in marketing, including customer segmentation and lead scoring
Module #19 Model Deployment and Maintenance Deploying and maintaining predictive models in production, including model monitoring and updates
Module #20 Ethical Considerations in Predictive Modeling Ethical considerations in predictive modeling, including bias and fairness
Module #21 Advanced Topics in Predictive Modeling Advanced topics, including gradient boosting and transfer learning
Module #22 Predictive Modeling in Industry 4.0 Applications of predictive modeling in Industry 4.0, including Predictive Maintenance and Quality Control
Module #23 Predictive Modeling in Finance Applications of predictive modeling in finance, including credit risk assessment and portfolio optimization
Module #24 Predictive Modeling in Retail Applications of predictive modeling in retail, including demand forecasting and customer segmentation
Module #25 Predictive Modeling in Sports Applications of predictive modeling in sports, including player performance prediction and game outcome forecasting
Module #26 Predictive Modeling in Environmental Sustainability Applications of predictive modeling in environmental sustainability, including climate modeling and renewable energy forecasting
Module #27 Predictive Modeling in Transportation Applications of predictive modeling in transportation, including traffic flow prediction and route optimization
Module #28 Predictive Modeling in Energy Applications of predictive modeling in energy, including energy demand forecasting and grid management
Module #29 Capstone Project:Developing a Predictive Model Students work on a capstone project to develop a predictive model using real-world data
Module #30 Course Wrap-Up & Conclusion Planning next steps in Data-Driven Innovation with Predictive Modeling career
