Data Visualisation

1. Roles of visualisation Communication – describing and explaining ideas, e.g. entertainment or learning.Confirmation – monitoring information streams, e.g. IoT sensor measurements are creating and testing hypotheses, e.g. cosmology simulations.Exploration – exploring and analysing information, e.g. public service data. 2. From data to wisdom Data – Symbols and signs represent something in the world. Information …

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Mathematics for Economics and Finance

1. Introduction Functions Linear, consumption Quadratic, consumption Cubic, costs Exponential, growth Logarithmic, production  Simultaneous equations Demand is Pd = a + bq Supply is Ps = j + kq 2. Application Contingent valuation is a method of valuing resources for which there is no marker, and therefore no observational unit price. This can be used …

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Introduction to Accounting and Finance

1. Introduction 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 2. Types of accounting Financial Accounting The preparation of …

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Leadership and change management

1. Leadership styles Wilfred Drath Leaders and followers no longer share the same goals This is due to the new collaborative workplace methods: direction, alignment and commitment Ann Cunliffe & Matthew Eriksen Relational leadership theory Leaders hold themselves in relation to others Many voices and expanded viewpoints Gary Hamel More data than ever before can …

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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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Statistical learning for Data Science

Multivariate Data Multivariate data are measurements or observations of P(>1) variables on each of n items/individuals. When P = 1, that is univariate data, when p = 2, that is bivariate data P is variables and n is observations The types of prediction Regression – to predict a quantitative response | Ridge regression, the LASSO …

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