ENGINEERING ECONOMY

Course Description

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ENGINEERING ECONOMY

Evaluation Tools (Active Term)

Course outcomes

1. 01 Recognize the fundamental concepts of engineering economy
2. 02 Apply engineering economy factors to account for the time value of money
3. 03 Analyze Service, revenue, mutually exclusive and independent alternatives
4. 04 Perform economic analysis of alternatives using present worth, annual worth, future worth and rate of return methods.
5. 05 Evaluate projects using rate-of -return and benefit/cost ratio methods
6. 06 Decide the level of activity necessary or the value of a parameter to breakeven.
7. 07 Combine the effects of inflation into an economic analysis whenever necessary

Course Syllabus

Reference Books & Course Materials

1. 01 Leland Blank and Anthony Tarquin "Basic of Engineering Economy" ,Mc Graw Hill, 1st edition, 2008.
2. 02 Leland Blank and Anthony Tarquin “Engineering Economy”, Mc Graw Hill, sixth edition, 2005.
3. 03 William G. Sullivan, Elin M. Wicks and James T. Luxhoj “Engineering Economy”, Thirteenth edition, Pearson Prentice-Hall, 2006.

Learning Outcomes

1. L01 Recognize the fundamental concepts of engineering economy
2. L02 Apply engineering economy factors to account for the time value of money
3. L03 Analyze Service, revenue, mutually exclusive and independent alternatives
4. L04 Perform economic analysis of alternatives using present worth, annual worth, future worth and rate of return methods.
5. L05 Evaluate projects using rate-of -return and benefit/cost ratio methods
6. L06 Decide the level of activity necessary or the value of a parameter to breakeven.
7. L07 Combine the effects of inflation into an economic analysis whenever necessary

Program Outcomes

1. P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix