Overview | Information for Prospective Students | Information for Current Students

DSA5201 DSML Industrial Consulting and Applications Project

DSA5201

Objectives

Students will study different practical issues of data science through this course. They will work on a data-science / machine learning / A.I related project to enhance knowledge and computing skills for deriving actionable insights from data through machine learning. Project topics include classification of text document, commercial fraud detection solutions, movie recommendation system, image recognition, computer vision, identification of biomarkers for cancer, and diagnosis of rare disease, A/B test and AI quality control, in which application of machine learning techniques is vital.

 

Through this course, students will:

  • learn how to use and interact with open source data science/machine learning tools; 
  • showcase the knowledge and skills acquired in DSA5101 and other data science courses; and
  • appreciate the transformation from data to insight and to policy-making.

Prerequisites

DSA5101 Introduction to Big Data for Industry

To register for DSA5201, students must:

  • Find a relevant company or a NUS/A*Star research institute that is willing to host you as an intern with the following conditions.
    • Minimum 20 hours per week for stipulated minimum period of 6 weeks.
    • For overseas internship projects, do provide sufficient information about host company.
  • Self-sourced internship is subjected to course coordinator’s approval.  After the project proposal is approved by the course coordinator, the student will register for the course himself/herself during the course registration exercise. The project proposal must reach the course coordinator by end of Instructional Week 1 of the semester/special term.
  • You may start the proposed internship even before your course registration, but prior approval from the course coordinator is required.

Important: Students who withdraw from DSA5201 midway (with ‘W’ grade) without valid reasons, or who fail the course, will not be allowed to retake it. In such cases, students will be required to take a replacement course, and the applicable course fees may apply.

  • Students are required to submit a bi-weekly report on their progress into Canvas. (Please check with your course coordinator on the report format.)
  • You are also required to submit a final report and give an oral presentation in the last week of the semester/special term.
  • Assessment is based on the student’s performance during the attachment, final report and oral project presentation. 
  • At the end of your internship or the semester/special term, your company supervisor will be given an assessment form to rate your performance.
  • The student’s oral presentation will be evaluated and it is recommended that students discuss the following points:
    1. What is/are the objective(s) / task(s) that you are asked to perform for the internship;
    2. The equipment, software/tools etc used to help you achieve your tasks;
    3. What have you achieved at the end of the internship;
    4. Any other observation(s) made.
  • The final grade (either Satisfactory “CS” or Unsatisfactory “CU”) will be given. There will be no final examination for this course.

The MSc in Data Science and Machine Learning (MDSML) programme welcomes collaboration with industry partners who wish to offer internship opportunities to our students.


If your organisation is interested in offering internship positions or learning more about our internship course, please contact the MDSML Programme Office at AskMDSML@nus.edu.sg.


We look forward to exploring collaboration opportunities with you.