Unit Content
Outcomes
- Select, collect, clean, and prepare real-world data for predictive analysis;
- Develop predictive models using a variety of techniques, including regression analysis, time series forecasting, and machine learning algorithms, to make accurate predictions based on historical data;
- Assess the accuracy and performance of predictive models through the use of appropriate evaluation metrics and validation techniques while adhering to professional and ethical practices;
- Solve real-world business problems utilizing predictive insights.
- Strategise to make informed decisions in complex and contested commercial environments.
Subject Content
- Introduction to Predictive Analytics
- Data Preparation for Predictive Modeling
- Regression Analysis for Predictive Modeling
- Classification and Decision Trees
- Time Series Forecasting
- Clustering and Segmentation
- Feature Selection and Engineering
- Model Evaluation and Validation
- Ensemble Methods in Predictive Analytics
- Text and Sentiment Analysis
- Predictive Analytics in Business and Marketing
- Real-World Applications and Case Studies
This course may be offered in the following formats
- Face to face on site
- E-learning (online)
- Intensive
- Extensive
Please consult your course prospectus or enquire about how and when this course will be offered next at Alphacrucis University College.
Assessment Methods
- Quiz (15%)
- Project (30%)
- Report (30%)
- Presentation (25%)
Prescribed Text
Delen, D. (2020). Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners (2nd ed.). Pearson FT Press.
Check with the instructor each semester before purchasing any textbooks
