We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and Data Science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine Learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, Artificial Intelligence (AI) allows us to reason under uncertainty and find optimal solutions for business challenges. The material was especially designed for Business Analytics degrees with a focus on Data Science and can also be used for Machine Learning or Artificial Intelligence classes. Data Science languages such as R provide a platform to access the state-of-the-art methods for analysing business data.

Data Analytics for Business: AI-ML-PBI-SQL-R PDF

The list of books you might like