Computational Reasoning (edX)

Computational Reasoning (edX)
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Computational Reasoning (edX)
This module aims to enhance learners’ managerial abilities by equipping them with computational tools and philosophical critical reasoning skills, thus empowering them to make well-informed decisions based on data and to critically evaluate solutions.

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Through a series of fun and engaging hands-on activities, this module aims to equip the learner with the ability to thoughtfully apply computational tools when solving complex real-world problems. This module aims to impart to the learner, the ability to critically self-evaluate the way they apply these tools, and thus be able to reason effectively in a variety of contexts. They will learn to identify problems and design solutions, while also developing a critical awareness of the merits and limits of their methods, thereby empowering them to make better-informed decisions and to articulate the reasons for those decisions.



What you'll learn

- Familiar with the process of computational problem-solving

- Simplify and analyse complex problems and identify possible solutions.

- Using computational tools to form persuasive arguments and prescriptions.

- Understand the rudimentary concepts of algorithm design

- Be familiar with the processes involved in creating algorithms to solve problems.

- Appreciate the importance of failure as an essential component in problem-solving.

- Be aware of the usefulness and limitations of various computational tools.

- Communicate effectively with others who engage in similar ways of problem-solving.


Syllabus


Lesson 1: Introduction to Computational Reasoning

Understand the computational problem-solving process, and able to clearly define objectives to solve problems.

Understand the obstacles that make it difficult to develop good computational solutions


Lesson 2: What’s Going On and Why? Understanding the Situation and Identifying Problems Using Data Analysis

Effectively use the various tools of Microsoft Excel to analyse data.

Identify patterns or breaks in patterns to better understand and describe what is going on in the dataset, and to identify possible causes to problems.

Distinguish between direct and proxy measures, with the awareness of the problems inherent in using proxy measures.


Lesson 3: How to Effectively Reason with Data

Identify assumptions underlying proxy measures and evaluate the strength of these assumptions.

Formulate clear and unambiguous hypotheses based on data and evaluate the strengths of these hypotheses.


Lesson 4: Anyone Can Model: The Fundamentals of Modelling

Read and comprehend conditionals and nested conditionals in order to organise and sort data on a large scale

Create accurate classification models based on the processes of pattern recognition and abstraction.

Appreciate the difficulties in developing abstract models, and identify shortcomings of such models.


Lesson 5: Social Network Analysis: What’s Going on in the Neighbourhood?

Develop a firm understanding of the concepts of loops and nested loops

Develop a nuanced understanding of the notion of “importance” in a social network through the concepts of degree centrality and betweenness centrality.


Lesson 6: Greedy Methods: How to Solve Problems in a Fast and Systematic Manner

Articulate Greedy Rules when attempting to solve problems via the optimisation-approach.

Evaluate different Greedy Rules to prescribe effective solutions


Lesson 7: A Fun Introduction to Coding with VBA

Basic knowledge of VBA to automatically navigate around a spreadsheet and manipulate cells and data.

Apply conditionals in VBA to process rows of information and generate output.

Competently debug errors in VBA.


Lesson 8: Let’s Up Our VBA Game!

Apply loops in VBA to process rows of information and generate output.

Formulate precise conditionals through the exercise of pattern recognition to solve more complex problems.



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Course Auditing
125.00 EUR

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