Best Analyse des données Courses

1. Introduction to Data Analysis

Don’t know what data analysis really is, but keen to find out? This clear and concise course has good examples of data analysis tools and how this skill can help transform a business.

2. Mastering Data Analysis Using Microsoft Excel: Formulas and Pivot Tables 

Knowing your way around MS Excel is necessary to effectively analyse massive volumes of complex data. This is a practical short course full of valuable tips to help you.

3. Introduction to Data Analysis Using Microsoft Power BI

Are you working with data sets that are too large for MS Excel to handle? Then, it is time to learn Microsoft Power BI (Business Intelligence) through this comprehensive short course.

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.

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• Data cleaning and preparation: removing corrupt, irrelevant or out-of-date information from data sets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)