Data analytics is an interdisciplinary field that aims to draw meaningful insight underlying data using various techniques based on theories from many branches including mathematics, probability, statistics, optimization, and computer science. Challenging applications are not limited to Electrical Engineering but are also found in various domains such as Biomedical Engineering, Financial engineering, Agriculture, Atmospheric sciences, etc. Our graduate program in data analytics aims to introduce essential techniques and tools used for students who are upon successfully completing this program, will be able to

 

– Understand natural characteristics of data collected with the presence of randomness; clean up and manage the data.

– Formulate quantitative models to address research or practical questions in real-world applications.

– Apply appropriate quantitative and statistical methods to make inference or prediction driven by data analysis.

– Provide critical evaluation of the statistical methods available in various domains.

– Communicate key knowledge and interpret information learned from the data to broad audience.

Why studying Data Analytics at EECU ?

  1. Various available courses across many disciplines. Participants can enroll relevant courses of interest from many schools, ranging from mathematics, applied statistics, or computer science.
  2. Comprehensive foundation on mathematics and analysis. The key ingredient behind the success of statistical learning tools is from theories supporting statistical models and methods. We focus on developing analytical skills for participants to be able to
  3. Exposure to diverse applications. Participants can explore research projects across various fields including renewable energy application, multimedia processing, embedded and control systems, biomedical engineering, telecommunication network.

 

Highlights of research in this area include:

– Time series forecasting in renew energy applications.

– Statistical models of biological signals such as ECG, EEG, EKG, fMRI for learning causality or abnormality in the data.

– Acoustic echo cancellation in hands-free communications.

– Acoustic feedback cancellation and Non-linear compression for hearing-aid devices.

– Noise reduction techniques for speech enhancement.

– A dentist-drill noise reduction technique.

– Hand-writing character recognition

รายวิชาที่เปิดสอนใน data analysis

2102502    สัญญาณสุ่มและระบบRandom Signals and Systems

2102576    การแปลงสัญญาณและงานประยุกต์ Signal Transforms and Applications

2102676    กรรมวิธีสัญญาณภาพดิจิทัลDigital Image Processing

2102875    กรรมวิธีสัญญาณวิดีโอเชิงเลขDigital Video Processing

2102876    การประมวลสัญญาณแบบปรับตัวได้ Adaptive Signal Processing

2102571    การสื่อสารสื่อประสมMultimedia Communication              

2102642    คอมพิวเตอร์วิชันและอิเล็กทรอนิกส์วีดิทัศน์ Computer Vision and Video Electronics

2102611    เครื่องมือทางการแพทย์ Medical Instrumentation

2102531    การหาแบบจำลองของระบบ System Identification

2102575    การอนุมานและการจำลองเชิงสถิติ Statistical Inference and Modeling

2110743    การเรียนรู้ด้วยเครื่องMachine Learning

2102505    เทคนิคการออปติไมซ์เบื้องต้นIntroduction to Optimization Techniques

2102732    การหาค่าเหมาะที่สุดเชิงคอนเวกซ์และการประยุกต์ทางวิศวกรรมConvex Optimization and Engineering Applications