
Import datasets into SAP Analytics Cloud, including training and test data, naming the file cardiograms. Dataset includes age, gender, height, weight, blood pressure, cholesterol, glucose, smoking, activity, and cardiovascular disease.
Import cardiovascular disease data into SAP Analytics Cloud and build the classifier by selecting classification, setting the binary target, and training while monitoring predictive power and graphs.
Analyze classification performance using the confusion matrix and key metrics such as accuracy, precision, recall, specificity, and F1 score for binary outcomes.
Explore global performance indicators for predictive analytics, including predictive power (0–100%), prediction confidence above 95%, influencers’ contributions, and metrics like lift, sensitivity, specificity, and density.
Leverage the Titanic dataset to train a classifier on features such as class, gender, age, and ticket details, then predict survival on unseen data using SAP Analytics Cloud.
Train a classifier on the Titanic data in SAP Analytics Cloud, selecting classification and training, then evaluate robustness with performance metrics and the confusion matrix on unseen data.
Understand the basics of Regression (y=mx+c), know the applications of Regression techniques to answer business questions
Import and prepare a housing dataset in SAP Analytics Cloud for regression, loading latitude, longitude, rooms, bedrooms, population, income, ocean proximity, and the median house value as target.
Explore regression metrics in Predictive Analytics on SAP Analytics Cloud, including RMSE, prediction vs observed, confidence intervals, and variable contributions to assess model robustness.
Explore time series data and how to forecast future demand using bicycle rentals as a case study. Identify trends, cycles, and unexplained fluctuations.
Import a two-column time series dataset in ascending time order to show the trend of daily bicycle hires; validate for missing values across about 3600 days.
Analyze time series dataset to reveal trends and cycles, note a dip during the outbreak, and prepare data by enforcing ISO 8601 dates, sorting, and excluding missing values for forecasting.
The course is ideal for Business Managers and Business Analysts who use SAP Analytics Cloud for data-driven processes. It focuses on Predictive Data Analytics, equipping you with specialized knowledge and highly sought-after skills. You will learn to transform data sets into valuable insights, enabling better data-driven decision-making.
Course Overview
Predictive Analytics Tools and Models: Organizations can now utilize historical and current data to reliably forecast future trends and customer behaviors. The course covers the application of predictive analytics to enhance decision-making processes across various business functions.
SAP Cloud Analytic Tools: You will learn to effectively manipulate, analyze, and interpret data using SAP Cloud analytic tools to improve overall business performance. This includes hands-on experience with SAP Analytics Cloud features, ensuring practical knowledge that can be applied immediately in your professional role.
Applications of Predictive Analytics
- Supply Chain Management: Optimize operations by predicting demand and managing inventory more effectively.
- Marketing and Promotional Planning: Increase revenue and sales by targeting customers who are likely to make a purchase, and plan promotions more strategically.
- Customer Retention: Utilize predictive models to identify at-risk customers and develop strategies to retain them.
- Product Management: Optimize product portfolios by understanding market trends and customer preferences.
Predictive analytics provides insights into when and why demand may occur and offers what-if analysis to influence customer demands and needs. The course covers a variety of statistical techniques, including data mining, predictive modeling, and machine learning, to analyze current and historical data for future predictions.
Learning Outcomes
By the end of the course, you will be able to:
- Build Classification Models: Develop models to categorize data and make predictions based on classification.
- Build Regression Models: Create models that predict continuous outcomes based on input variables.
- Build Time Series Models: Analyze and forecast data points over time to identify trends and patterns.
- Interpret Metrics: Understand and interpret key metrics for classification, regression, and time series models.
- Apply Models to Unseen Data: Use trained models to make predictions on new, unseen datasets, ensuring practical, real-world application.
Disclaimer
The instructor of this course is not associated with SAP SE or any of its affiliates. The course content is independently developed and is not endorsed by SAP.
This course offers a comprehensive approach to mastering predictive analytics with SAP Analytics Cloud, empowering you to drive better business outcomes through informed, data-driven decisions.