These free online courses in data analysis will help you understand problems that organisations face by exploring data in meaningful ways. With a strong understanding of data analysis, you will learn how to organise, interpret, structure, and present data, turning them into useful information necessary for making well-informed and efficient decisions....…Read More These free online courses in data analysis will help you understand problems that organisations face by exploring data in meaningful ways. With a strong understanding of data analysis, you will learn how to organise, interpret, structure, and present data, turning them into useful information necessary for making well-informed and efficient decisions. Read Less
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What is ‘data analysis’?
Wikipedia summarises data analysis as ‘the process of inspecting, cleansing, transforming and modelling data to discover useful information, informing conclusions and supporting decision-making’.
‘Data’ compromises any information about customers (how much they spend, what they spend on, when they spend and so on), industry trends (are some products not selling as well any more or what new technologies are arriving), business expenses (what you owe suppliers for inventory, when your bills are due and what fixed overheads you have every month) and economic indicators (such as exchange rates if you are an importer or exporter or changes to tax tables). Data analysts collect vast amounts of data like this and can drill down to a very granular level (for example, clients aged 25 to 30 spend an average of X amount on Y product in the 48 hours before Z major event). This helps business owners make better decisions, such as stocking more Y products and promoting them for 96 hours before next year’s Z event so those clients will spend X+ and the business will profit more.
What do data analysts do and what skills do they need?
Successful and sought-after data analysts don’t only interpret data; they also have to have a wide range of other skills to ensure their conclusions and recommendations are as correct as possible, such as:
Data cleaning and preparation: removing corrupt, irrelevant or out-of-date information from data sets
Data analysis and exploration: combing the resulting clean data for insights
Statistical knowledge: understanding whether something is a genuine trend, a coincidence or an anomaly based on statistical likelihood
Creating data visualisations: presenting the data in graphs, charts, maps or tables
Creating dashboards and reports: set up hourly, daily, weekly or monthly ways of quickly checking what the data is doing (e.g. number of sales or number of cancellations)
Writing and communication: distilling what the data is suggesting into written reports or verbal presentations to the managerial team or board of directors
Domain knowledge: the background information on the industry that is being analysed (e.g. FMCG retail, inbound travel or the housing market)
Problem solving: if the data is throwing up a potential issue for your organisation (such as declining new customers), can you suggest a solution (how to retain old customers or attract and retain new ones)
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