Principles of Economics

The Economic problem is how best to allocate scarce resources. Economists study resources’ allocation, distribution, and utilisation to meet human needs. Land -> (rent) Labour -> (wages) Capital -> (interest) Entrepreneurship -> (profit) Choices What? – what goods are to be produced with scarce resources; clothes, food, armaments? How? – how best can the resources …

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Introduction to Accounting and Finance

Accounting Classification and recording of monetary transactions, The presentation and interpretation of the results of those transactions to assess performance over a period and the financial position at a given date, and The monetary projection of future activities arising from alternative planned courses of action Financial Accounting The preparation of financial information For shareholders and …

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Digital ethics

Consideration and judgement of what you should do Benefactor – do good No malice – do no harm Autonomy – individual control Justice – fairness of outcomes The major themes in digital ethics are privacy and confidentiality. Though also surveillance, with worries of a big brother society. Autonomy – To what extent are individuals in …

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Data Management and Exploratory Data Analysis

The scientific method – Question, research, hypothesis, experiment, analyse and conclusion The crisp method – Business understanding, data understanding, data preparation, modelling, evaluation and deployment Big data – volume, velocity, variety, veracity Reasons to use R: R is open use and free It is the language of statisticians You can combine R with Latex Text …

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Big Data

Introduction Big Data refers to the inability of traditional data architectures to efficiently handle the new datasets. Characteristics of Big Data that force new architectures are: Volume (size of the dataset) Variety (date from different sources) Velocity (rate of flow) Variability (the change in other characteristics) Descriptive analytics Data aggregation – such as grouping, sum, …

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Machine learning

Introduction The difference between traditional learning and machine learning is that knowledge/rules are swapped with labels. Traditional supervised learning, Deep learning and Unsupervised learning The Challenge with this sort of model is the overfitting, this may be caused by less representative training data or a smaller set of training data. Polynomial curve fitting Same as …

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