Data Mining and Business Strategies


Data Mining and Business Strategies

Start Immediately

  • Study Hours

    Study Hours

    150 Hours

  • Total Fee

    Total Fee


  • Delivery



  • Modality


    Self Paced

Programme Overview

This module offers an in-depth understanding of Data Mining and Strategic Management techniques for improving business decision-making. Through an examination of data mining and machine learning terminology and techniques, students will be introduced to data blending and wrangling concepts applicable to formulating a strategic data management plan. The challenge is to select the appropriate method. Students will learn to use and design data mining-based solutions to solve “real-time” business problems. 


Students will explore the strategic planning concept for using decision support systems or hybrid platforms when wrangling NoSQL and text data. The coursework will explore data mining concepts for blending unstructured data sets or working with semi-structured data using the Extract, Load, Transform (ELT) process. Also, students will briefly examine automated processes, like Extract Transformation and Load (ETL) or loading data sets as use cases. This course will theorize on data optimisation techniques for using machine learning and artificial intelligence for deep learning of the data. 


Finally, students will conduct statistical data modeling used in predictive and descriptive analysis such as classification trees, segmentation, clustering, and perform basic exploratory data analysis with Excel to create data analytical presentation projects.

Learning Outcomes

  1. Understand common Data Mining concepts: NoSQL, Big Data, and wrangling 
  2. Explore data mining and analyse how machine learning work. 
  3. Analyse several data analysis techniques for advanced data mining. 
  4. Determine how Classification Trees help in structuring the data. 
  5. Perform Cluster analysis and define Association Rules for data sets 

Get Started

Enrol now and get started with your studies right away.