SEC-101: Data and Information Fundamentals

Course Syllabus

Course Description

Data is the most valuable asset in modern information systems. It plays a critical role in supporting business operations, ranging from user facilitation to management decision-making. In many cases, data can be sensitive and may expose personal matters.

As future engineers in AI and Computer Engineering field, students must have a comprehensive understanding of data and information from every perspective. In this competency, you will learn about the types, characteristics, and properties of data and information. Additionally, you will gain an overview of data acquisition and cleaning processes.

This competency is designed to prepare students for working extensively with various types of data and information in the future.

General Information

Competency Code SEC-101
Competency Name Data and Information Fundamentals
Competency Credits 2
Competency Duration 3 Weeks (~8.5 Hours Per Week = 26 Hours in Total)
Instructor Dr. Charnon Pattiyanon <charnon@cmkl.ac.th>

Assessing Skills

  1. [SEC-101:00010] Evaluate the relation of data acquisition, preparation, transformation and cleansing - Successful students must understand the types, characteristics, and properties of data and information, and are able to evaluate the relation between data acquisition, preparation, transformation, and cleansing comprehensively.
  2. [SEC-101:00020] Design a proper process of data acquisition, preparation, transformation, and cleansing - Successful students must be able to design a proper process of data acquisition, preparation, transformation, and cleansing by integrating techniques, tools, and guidelines we have learned in the lecture.

Class Schedule and Topics

Week Lecture Topic Lab/Practical Session Topic
Week 1 Lecture 1: Introduction to Data and Information
  • Data Types: Quantitative and Qualitative
  • Data Representation
  • DIKW Model
  • Characteristics of Data
[Lecture 1 (PDF)]
  • Lab 1: Data and Information Analysis (Types, Representation, Sensitivity)
  • Assessment Announcement
Week 2 Lecture 2: Data Cleaning and Preprocessing
  • Introduction to Data Cleaning and Preprocessing
  • Python Libraries for Data Cleaning and Preprocessing
  • Understanding Data Quality Issues
  • Handling Missing Data
[Lecture 2 (PDF)] 		Lab 2: Handling Missing Data
Week 3 Lecture 2: Data Cleaning and Preprocessing
  • Dealing with Outliers
  • Data Normalization and Scaling
[Lecture 2 (PDF)] 		Lab 3: Dealing with Outliers, Data Normalization and Scaling

Assessment and Submission Guideline

This competency requires each student to submit one deliverables, which is:

  1. A Final Report: This is the final document summarizing the details of your assessment. Please refer to the report template provided below.

To support students throughout this competency, the following documents are provided:

Grading Rubric

Final Report Grading Rubric

The final report accounts for 100% of your total score, equivalent to 200 points out of 200 points (based on two assessing skills). Each section in the report carries a different score weight. Please refer to the Assessment Instruction for the detailed score distribution of each section.