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Computational Reasoning with Microsoft Excel
Jonathan Sim
Jonathan Sim
Through a series of fun and engaging hands-on activities in Microsoft Excel, 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 fundamental skills in Microsoft Excel for dealing with large amounts of data, and the ability to critically self-evaluate the way they apply these skills. 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 reason effectively in a variety of contexts.
Read more
Through a series of fun and engaging hands-on activities in Microsoft Excel, 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 fundamental skills in Microsoft Excel for dealing with large amounts of data, and the ability to critically self-evaluate the way they apply these skills. 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 reason effectively in a variety of contexts.
Read more
Through a series of fun and engaging hands-on activities in Microsoft Excel, 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 fundamental skills in Microsoft Excel for dealing with large amounts of data, and the ability to critically self-evaluate the way they apply these skills. 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 reason effectively in a variety of contexts.
What you'll learn
- Become familiar with the process of computational problem-solving
- Simplify and analyse complex problems and identify possible solutions.
- Communicate effectively with others who engage in similar ways of problem-solving.
-
Use Microsoft Excel to form persuasive arguments and prescriptions
-
Basic data preparation with useful formulae
-
Visualise data with Pivot tables
-
Automate processes using Visual Basic for Applications (VBA) coding
Register for this course and see more details by visiting:
OpenCourser.com/course/2daou4/computational
What's inside
Learning objectives
- Become familiar with the process of computational problem-solving
- Simplify and analyse complex problems and identify possible solutions.
- Communicate effectively with others who engage in similar ways of problem-solving.
- Become familiar with the process of computational problem-solving
- Simplify and analyse complex problems and identify possible solutions.
- 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.
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Syllabus
Lesson 1: Introduction to Computational Reasoning
Understand the computational problem-solving process, and able to clearly define objectives to solve problems.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches computational problem-solving, a valuable skill in many industries
Taught by Jonathan Sim, an experienced instructor
Examines data analysis and modelling techniques, which are highly relevant in business and research
Develops critical thinking and problem-solving skills, essential for personal and professional growth
Requires familiarity with Microsoft Excel, which may be a barrier for some learners
Focuses on the fundamentals of data analysis and modelling, which may not be in-depth enough for advanced practitioners
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Activities
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Computational Reasoning with Microsoft Excel.
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Career center
Learners who complete Computational Reasoning with Microsoft Excel will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts look at large sets of data to find trends. They analyze this data to answer business questions, identify problems, and make recommendations. This course covers the process of computational problem-solving, which can help Data Analysts to improve their skills in analyzing data and solving business problems. Additionally, the course covers Microsoft Excel, a tool that is often used by Data Analysts to manage and analyze data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make recommendations about investments and financial decisions. This course covers data analysis techniques that can help Financial Analysts make better use of data to make more informed decisions. Additionally, Microsoft Excel is a tool that is often used by Financial Analysts to manage and analyze data.
Business Analyst
Business Analyst
Business Analysts study the needs of an organization and design solutions to help the organization achieve its goals. This course covers the process of computational problem-solving, which can help Business Analysts understand the needs of an organization and then come up with solutions to meet those needs. Additionally, Microsoft Excel is a tool that is often used by Business Analysts to track progress and measure results.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems that arise in business and industry. This course covers mathematical and analytical techniques that are used by Operations Research Analysts to solve problems. Additionally, Microsoft Excel is a tool that is often used by Operations Research Analysts to manage and analyze data.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand customer needs and preferences. This course covers data analysis techniques that can help Market Researchers better understand and interpret data. Additionally, Microsoft Excel is a tool that is often used by Market Researchers to manage and analyze data.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course covers the process of computational problem-solving, which can help Software Engineers better understand the needs of users when designing and developing software systems. Additionally, Microsoft Excel is a tool that is often used by Software Engineers to manage and analyze data.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course covers data analysis techniques that can help Statisticians better understand and interpret data. Additionally, Microsoft Excel is a tool that is often used by Statisticians to manage and analyze data.
Data Scientist
Data Scientist
Data Scientists use data to build models and make predictions. This course covers data analysis techniques that can help Data Scientists build better models and make more accurate predictions. Additionally, Microsoft Excel is a tool that is often used by Data Scientists to manage and analyze data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial markets. This course covers mathematical and statistical techniques that are used by Quantitative Analysts to analyze financial markets. Additionally, Microsoft Excel is a tool that is often used by Quantitative Analysts to manage and analyze data.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course covers mathematical and statistical techniques that are used by Actuaries to assess risk and uncertainty. Additionally, Microsoft Excel is a tool that is often used by Actuaries to manage and analyze data.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and maintain computer systems. This course covers the process of computational problem-solving, which can help Computer Scientists better understand the needs of users when designing and developing computer systems. Additionally, Microsoft Excel is a tool that is often used by Computer Scientists to manage and analyze data.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks. This course covers risk management techniques that can help Risk Managers better identify, assess, and mitigate risks. Additionally, Microsoft Excel is a tool that is often used by Risk Managers to manage and analyze data.
Portfolio Manager
Portfolio Manager
Portfolio Managers manage and invest funds for clients. This course covers data analysis techniques that can help Portfolio Managers make better use of data to make more informed decisions.
Investment Analyst
Investment Analyst
Investment Analysts research and make recommendations on investments. This course covers data analysis techniques that can help Investment Analysts make better use of data to make more informed decisions.
Financial Planner
Financial Planner
Financial Planners help clients plan for their financial future. This course covers data analysis techniques that can help Financial Planners make better use of data to make more informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/2daou4/computational
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computational Reasoning with Microsoft Excel.
Save
This comprehensive guide covers all aspects of Microsoft Excel, including data analysis, visualization, automation, and VBA programming. It is an excellent reference for learning the advanced features of Excel.
Covers advanced VBA programming techniques for Excel. It valuable resource for experienced VBA programmers who want to extend their skills and knowledge.
Shares the experiences and insights of two experienced data scientists. It discusses the thought processes, tools, and techniques used in data science, providing valuable guidance for aspiring data scientists.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Combines Excel and VBA programming to provide a powerful toolkit for data analysis. It covers data cleaning, transformation, visualization, and statistical analysis, making it a valuable resource for automating data-driven tasks.
Save
Provides a comprehensive overview of deep learning, including the theory, algorithms, and applications of deep learning models. It valuable reference for understanding the state-of-the-art in deep learning.
Provides a comprehensive overview of reinforcement learning, including the theory, algorithms, and applications of reinforcement learning models. It valuable reference for understanding the state-of-the-art in reinforcement learning.
Provides a comprehensive overview of the mathematical foundations of machine learning, including linear algebra, calculus, probability, and statistics. It valuable reference for understanding the mathematical underpinnings of machine learning algorithms.
Provides a comprehensive overview of natural language processing using the Python programming language. It covers a wide range of topics, including text preprocessing, machine learning for NLP, and deep learning for NLP.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, feature detection, object recognition, and scene understanding.
Provides a practical introduction to data science, including data mining, machine learning, and statistical analysis. It valuable resource for understanding the techniques used in data-driven decision-making.
Provides a step-by-step introduction to VBA programming in Excel. It valuable resource for learning the basics of VBA and automating tasks in Excel.
Provides a comprehensive overview of social network analysis, including the concepts, methods, and applications of network science. It is an excellent resource for understanding the dynamics of social networks.
Provides a practical introduction to machine learning using the R programming language. It covers data preparation, model building, and model evaluation, making it a valuable resource for understanding the principles of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2daou4/computational
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Effort
3 to 5 hours per week for 8 weeks
Level
Elementary
Via
edX
Institution
The National University of Singapore
Instructor
Jonathan Sim
Language
English
Transcript
English
Good to know
Teaches computational problem-solving, a valuable skill in many industries
Taught by Jonathan Sim, an experienced instructor
Examines data analysis and modelling techniques, which are highly relevant in business and research
Develops critical thinking and problem-solving skills, essential for personal and professional growth
Requires familiarity with Microsoft Excel, which may be a barrier for some learners
Focuses on the fundamentals of data analysis and modelling, which may not be in-depth enough for advanced practitioners
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