The Enterprise Data Scientist (EDS) course is a fast-paced practical introduction to the interdisciplinary field of data science, which is the study of how to use computer science, statistics and a scientific mindset to extract knowledge from data.

Enterprise Data Scientist

  • Upon completion, participants should be able to demonstrate each of the following:

    • Understand the landscape of machine learning possibilities

    • Ability to train a model using simple supervised learning algorithms

    • Ability to train and evaluate a model using training and/or test data

  • Supervised Machine Learning
    Improve productivity at work with automated systems and software powered by AI and machine learning. Enrol in Introduction to Machine Learning with CADS to embark on your journey in building intelligent applications.

     

    Unsupervised Machine Learning
    Get the computer to hunt for patterns in structure buried within a huge dataset with Unsupervised Machine Learning techniques and methodologies.

     

    Relational Database Design
    Advanced database management systems and methodologies give you the capability to design and develop real-world database applications in advanced SQL skills. 

     

    NoSQL Essentials
    NoSQL Essentials gives you the capability to design and develop real-world NoSQL databases with full control, and flexibility for agile system development.

     

    Big Data Analytics with Apache Spark
    Build and maintain applications with faster startup, better parallelism, and better CPU utilization. Gain an in-depth and comprehensive understanding of big data analytics and AI project from project initiation to project completion.

     

    Natural Language Processing (NLP)
    Master the practical approaches and hands-on programming to analyze texts to gather comprehensible data and develop actionable insights.

     

    Capstone
    The Capstone module allows participants to apply their knowledge of databases management, statistical tools, and data visualisation techniques to communicate data-driven insights. It is a critical step for participants to build their confidence in using their newly acquired data analysis skills in an insightful and impactful way to drive change in organisational.