Data-Driven Decisions Without Code: Analytics and Machine Learning with RapidMiner
About This Course
Organisations today increasingly rely on data to improve decision-making, optimise operations, identify trends, and gain a competitive advantage. At the same time, modern analytics and machine learning tools are becoming more accessible to non-technical professionals through visual no-code platforms. This course introduces learners to practical data analytics and machine learning concepts using RapidMiner, without requiring any programming background. Designed for adult learners and working professionals, the course focuses on developing practical understanding and hands-on experience in building analytics workflows through RapidMiner’s intuitive drag-and-drop environment. Learners will learn how data can be prepared, explored, visualised, and analysed to support data-driven decision making across a variety of business and organisational contexts. The course covers fundamental concepts in data analytics and machine learning, including data preparation, classification, prediction, clustering, and model evaluation. Through guided demonstrations and practical exercises, learners will develop simple machine learning workflows and learn how analytical models can be applied to real-world problems such as customer segmentation, sales forecasting, employee attrition analysis, and customer churn prediction. In addition to technical workflows, the course also discusses the opportunities, limitations, and ethical considerations associated with Artificial Intelligence (AI)-driven analytics, including data quality, bias, interpretability, and responsible use of AI technologies. Several coursework and hands-on will help learners to experience decision-making based on data, using machine learning.
What You'll Learn
· Understand the fundamentals of data analytics and machine learning and explain how they support data-driven decision making in modern organizations.
· Identify different types of analytical and machine learning tasks including classification, prediction, clustering, and trend analysis.
· Prepare and preprocess datasets for analysis by handling missing values, cleaning data, and selecting relevant features.
· Design no-code analytics workflows using RapidMiner through visual drag-and-drop processes and workflow-based analytics techniques.
· Develop basic machine learning models without programming using common techniques such as decision trees, regression, and clustering.
· Visualise, interpret, and communicate analytical results to support business insights and decision making.
· Evaluate the performance of machine learning models using practical validation and performance measurement techniques.
· Learn how to use RapidMiner for data analytics and machine learning.
· Apply practical analytics and machine learning techniques to real-world business scenarios through guided hands-on exercises and case-based activities.
Entry Requirements
· A Diploma or above
· Basic Computing skills (e.g. how to use Excel)