What does the Predictive Analytics Mastery course cover?
Predictive Analytics Mastery is covered here in 8 modules: Introduction to Predictive Analytics: Definition and scope of predictive analytics, Data Science Fundamentals: Data types and structures, Data mining and machine learning, Machine Learning for Predictive Analytics: Model evaluation and selection and 5 more.
How do you approach Predictive Analytics Mastery step by step?
The work is sequenced in 8 stages. It starts with Introduction to Predictive Analytics: Definition and scope of predictive analytics, moves through data Science Fundamentals: Data types and structures, Data mining and machine learning and Machine Learning for Predictive Analytics: Model evaluation and selection, and ends at Case Studies and Real-World Applications: Predictive maintenance and quality control.
What is in Module 1 of the Predictive Analytics Mastery course?
Module 1 is Introduction to Predictive Analytics: Definition and scope of predictive analytics. It works through definition and scope of predictive analytics, history and evolution of predictive analytics, types of predictive analytics: descriptive, diagnostic, predictive, and prescriptive and 1 more. It sets the vocabulary the remaining 7 modules build on.
How is the Predictive Analytics Mastery course delivered?
The Predictive Analytics Mastery course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Predictive Analytics Mastery course cost?
The Predictive Analytics Mastery course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Forecasting Models in Science of Decision-Making, Sales Forecasting in Science of Decision-Making, Forecast Accuracy in Science of Decision-Making, The Data Science Leader's Course on Building Insurance.
More answers: what you get with every course, refund policy, all help answers.
Predictive Analytics Mastery: Amplifying Business Forecasting with Data Science and Machine Learning
Course Overview
This comprehensive course is designed to equip business professionals and data analysts with the skills and knowledge needed to amplify business forecasting using data science and machine learning techniques. Participants will learn how to harness the power of predictive analytics to drive informed decision-making and gain a competitive edge in the market.Course Objectives
- Understand the fundamentals of predictive analytics and its applications in business forecasting
- Learn data science and machine learning techniques for predictive modeling
- Develop skills in data preparation, visualization, and analysis
- Apply predictive analytics to real-world business problems
- Interpret and communicate results to stakeholders
Course Outline
Module 1. Introduction to Predictive Analytics: Definition and scope of predictive analytics
- Definition and scope of predictive analytics
- History and evolution of predictive analytics
- Types of predictive analytics: descriptive, diagnostic, predictive, and prescriptive
- Business applications of predictive analytics
Module 2. Data Science Fundamentals: Data types and structures, Data mining and machine learning
- Introduction to data science
- Data types and structures
- Data visualization and communication
- Data mining and machine learning
Module 3. Machine Learning for Predictive Analytics: Model evaluation and selection
- Introduction to machine learning
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Machine learning algorithms: linear regression, decision trees, random forests, and neural networks
- Model evaluation and selection
Module 4. Data Preparation and Visualization: Data cleaning and preprocessing
- Data cleaning and preprocessing
- Data transformation and feature engineering
- Data visualization: plots, charts, and heatmaps
- Interactive visualization tools: Tableau, Power BI, and D3.js
Module 5. Predictive Modeling with R and Python: Decision trees and random forests in R and Python
- Introduction to R and Python for predictive analytics
- Linear regression and logistic regression in R and Python
- Decision trees and random forests in R and Python
- Neural networks and deep learning in R and Python
Module 6. Time Series Forecasting: Introduction to time series analysis
- Introduction to time series analysis
- Time series decomposition: trend, seasonality, and residuals
- Exponential smoothing and ARIMA models
- Forecasting with machine learning: LSTM and Prophet
Module 7. Advanced Predictive Analytics Topics: Gradient boosting and XGBoost
- Ensemble methods: bagging, boosting, and stacking
- Gradient boosting and XGBoost
- Transfer learning and domain adaptation
- Explainable AI and model interpretability
Module 8. Case Studies and Real-World Applications: Predictive maintenance and quality control
- Predictive maintenance and quality control
- Demand forecasting and supply chain optimization
- Credit risk assessment and financial modeling
- Marketing analytics and customer segmentation
Course Features
- Interactive and engaging video lessons
- Comprehensive and up-to-date course materials
- Personalized support and feedback
- Hands-on projects and case studies
- Bite-sized lessons and flexible learning
- Lifetime access to course materials
- Gamification and progress tracking
- Community-driven discussion forum
- Expert instructors with industry experience
- Certificate of Completion issued by The Art of Service