Course description
The world of business is rapidly evolving, and data has become a critical resource for making informed decisions. Predictive analytics is a field of study that uses statistical and machine learning techniques to analyze data and make predictions about future outcomes. Predictive analytics can be applied to a range of business problems, including customer segmentation, fraud detection, and supply chain optimization.
In this 5-day course on Predictive Analytics for Business, participants will gain a deep understanding of predictive analytics and its applications in the business world. They will learn the key concepts and techniques of predictive modeling, data preparation, and feature engineering, as well as supervised and unsupervised learning algorithms. The course will also cover advanced topics such as deep learning algorithms and deploying predictive models in a business setting. Participants will leave the course with practical skills and knowledge to apply predictive analytics in their work and drive better business outcomes.
Upcoming start dates
Upcoming start dates
Suitability - Who should attend?
The course on Predictive Analytics for Business is designed for professionals who are interested in leveraging data to drive better business outcomes.
This includes business analysts, data analysts, data scientists, marketing professionals, and managers who want to make data-driven decisions. The course is also suitable for individuals who are interested in transitioning to a career in data analytics or data science. No prior experience in predictive analytics is required, but a basic understanding of statistics and programming is recommended.
Outcome / Qualification etc.
- Understand the principles and techniques of predictive analytics: Participants will gain a comprehensive understanding of predictive analytics and how it can be applied to business problems. They will learn the key steps involved in building a predictive model, including data preparation, feature engineering, and model selection.
- Learn to apply predictive modeling techniques to business problems: Participants will learn how to use supervised and unsupervised learning algorithms to solve real-world business problems. They will learn how to evaluate the performance of their models and choose the best one for a given problem.
- Develop practical skills in data preparation and feature engineering: Participants will learn how to prepare data for predictive modeling, including handling missing values, outliers, and imbalanced data. They will also learn how to create new features using feature engineering techniques.
- Gain experience in deploying predictive models in a business setting: Participants will learn how to deploy predictive models in a business setting and how to evaluate their performance. They will gain practical experience in using predictive analytics to make informed business decisions, such as customer segmentation and fraud detection.
Training Course Content
Day 1
Introduction to Predictive Analytics for Business
- Overview of predictive analytics and its applications in the business world
- Exploring the different types of predictive models
- Understanding the data requirements and data sources for predictive analytics
- Discussing the key steps involved in building a predictive model
Day 2
Data Preparation and Feature Engineering
- Understanding the importance of data quality in predictive modelling
- Exploring different techniques for data cleaning and feature engineering
- Handling missing values, outliers, and imbalanced data
- Feature selection techniques and methods for creating new features
Day 3
Supervised Learning Techniques
- Introduction to supervised learning algorithms such as linear regression, logistic regression, decision trees, and random forests
- Understanding the assumptions and limitations of each algorithm
- Evaluating the performance of the models using metrics such as accuracy, precision, and recall
- Tuning the models to improve their performance
Day 4
Unsupervised Learning Techniques
- Introduction to unsupervised learning algorithms such as clustering, dimensionality reduction, and association rules
- Understanding the assumptions and limitations of each algorithm
- Evaluating the performance of the models using metrics such as silhouette score, inertia, and lift
- Applying unsupervised learning to business problems such as customer segmentation and market basket analysis
Day 5
Advanced Topics in Predictive Analytics for Business
- Feature scaling and normalization techniques
- Ensemble methods such as bagging, boosting, and stacking
- Introduction to deep learning algorithms for predictive analytics
- Deploying predictive models in a business setting
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