Quantitative Methods with Modeling and Simulations
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Course Description
Course Description
This course introduces quantitative techniques and computational tools for decision-making and problem-solving, with an emphasis on mathematical modeling and simulation. Topics include statistical analysis, regression, optimization, linear programming, and dynamic systems modeling. The course integrates Python programming to enhance students' ability to implement algorithms, analyze data, and build computational models. Through hands-on projects, students will learn to use Python libraries such as NumPy, pandas, matplotlib, and scipy for data analysis and simulation. This practical approach bridges theoretical foundations with real-world applications, equipping students with essential skills for systematic problem-solving in modern professional environments.
Course Content
Course Content
CHAPTER 1: Introduction to Quantitative Methods
CHAPTER 1: Introduction to Quantitative Methods
Learning Outcome
Learning Outcome
Recognize the role of quantitative methods in understanding and interpreting data in various fields.
Articulate the significance of quantitative analysis in informed decision-making and problem-solving.
Discuss the foundational principles of quantitative methods and their practical applications in real-world contexts.
Lectures
Lectures
Lecture 01 - Patterns and Numbers in Natures
Lecture 02 - The Fibonacci Sequence and Golden Ratio
Lecture 03 - Mathematics for our World
Quizzes:
Quizzes:
Quiz No. 01 - Take the Quiz!
Quiz No. 02 - Take the Quiz!
Activities:
Activities:
Activity No. 01:
Activity No. 02:
CHAPTER 2: Statistical Analysis 1
CHAPTER 2: Statistical Analysis 1
Learning Outcome
Learning Outcome
Differentiate between t-test, ANOVA, and Chi-square tests based on their purpose, assumptions, and application scenarios.
Interpret the results of t-test, ANOVA, and Chi-square analyses to draw meaningful conclusions from data.
Apply appropriate statistical tests (t-test, ANOVA, or Chi-square) to analyze and evaluate datasets in practical problem-solving situations.
Lectures
Lectures
Lecture 01 - Patterns and Numbers in Natures
Lecture 02 - The Fibonacci Sequence and Golden Ratio
Lecture 03 - Mathematics for our World
Quizzes:
Quizzes:
Quiz No. 01 - Take the Quiz!
Quiz No. 02 - Take the Quiz!
Activities:
Activities:
Activity No. 01:
Activity No. 02:
CHAPTER 2: Statistical Analysis 2
CHAPTER 2: Statistical Analysis 2
Learning Outcome
Learning Outcome
Distinguish between correlation and regression, including their purposes and the types of relationships they analyze.
Interpret the correlation coefficient and regression equation to assess the strength, direction, and nature of relationships between variables.
Apply correlation and regression techniques to analyze data and predict outcomes in real-world scenarios.
Lectures
Lectures
Lecture 01 - Patterns and Numbers in Natures
Lecture 02 - The Fibonacci Sequence and Golden Ratio
Lecture 03 - Mathematics for our World
Quizzes:
Quizzes:
Quiz No. 01 - Take the Quiz!
Quiz No. 02 - Take the Quiz!
Activities:
Activities:
Activity No. 01:
Activity No. 02:
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