Skip to main content

KMUTNB: KNIME102 Advanced Data Analytics with KNIME for Auditors

Advanced Data Analytics with KNIME for Auditors


KMUTNB
Enroll Now

About This Course

In the dynamic field of auditing, the integration of data analytics is rapidly becoming indispensable. The "Advanced Data Analytics with KNIME for Auditors" course is designed to equip professionals in the auditing and finance sectors with the skills to leverage data analytics in their work, using the powerful KNIME software. This comprehensive course combines theoretical knowledge with practical, hands-on training, ensuring that participants are ready to apply advanced data analytics techniques in real-world auditing scenarios.

Participants will:

    Understand the Role of Data Analytics in Auditing: Delve into how data analytics is transforming the auditing landscape, enhancing efficiency, accuracy, and reliability.
    Master KNIME Analytics Platform: Gain in-depth knowledge and hands-on experience with KNIME, a leading open-source tool for data-driven innovation.
    Data Preparation and Cleaning: Learn techniques for preparing and cleaning data to ensure the integrity and accuracy of audit processes.
    Advanced Data Analysis Techniques: Explore advanced methods such as predictive modeling, anomaly detection, and time series analysis relevant to auditing tasks.
    Data Visualization and Interpretation: Develop skills in visualizing data and interpreting results to make informed auditing decisions.
    Real-World Applications and Case Studies: Apply your learning to real-world auditing scenarios, simulating the challenges faced by modern auditors.
    Ethical Considerations and Compliance: Understand the ethical implications of data analytics in auditing, including privacy concerns and regulatory compliance.

Requirements

Add information about the skills and knowledge students need to take this course.

Course Staff

Phayung Meesad

Staff Member #1

Biography of Assoc.Prof.Dr.Phayung Meesad

Phayung Meesad holds a Ph.D. in Philosophy in Electrical Engineering from Oklahoma State Water University in the USA. They also have a Master's degree in Electrical Engineering from the same university and a Bachelor's degree in Technical Program (Electrical Engineering) from King Mongkut's University of Technology North Bangkok. Phayung Meesad has taught subjects such as Data Mining, Advanced Data Mining, Fuzzy System and Neural Network, Machine Learning, Algorithms for Information and Data Science, Advanced Research Methodology, Big Data Analytics, and more. He has also held various administrative roles in the Faculty of Information Technology and Digital Information at KMUTNB, including Associate Dean for Administration from 2004 to 2008, Associate Dean for Academic and Research Affairs from 2008 to 2012, and Dean from 2012 to 2016. Currently, he serves as the Director of Central Library and a full-time faculty member of Department of Information Technology Managememnt (ITM), Faculty of Information Technology and Digigtal Innovation (ITD), KMUTNB.

Course Staff Image #2

Staff Member #1

Biography of Asst.Prof.Dr.Watchareewan Jitsakul

Dr.Wathareewan Jitsakul is an Assistant Professor at King Mongkut's University of Technology North Bangkok with extensive knowledge and experience in Artificial Neural Networks, Artificial Intelligence, and Data Mining. Renowned for contributions to the field of Artificial Neural Networks, especially in the realm of travel time prediction using multi-layer feed forward networks. Actively involved in academic conferences and meetings at both national and international levels. Her research interests are relared to Artificial Neural Networks, Machine Learning, Predictive Modeling, and Data Mining.

Frequently Asked Questions

What web browser should I use?

The Open edX platform works best with current versions of Chrome, Edge, Firefox, or Safari.

See our list of supported browsers for the most up-to-date information.

Question #2

Your answer would be displayed here.

  1. Course Number

    KNIME102

  2. Classes Start

Enroll