Data Science and Data Engineering: general introduction

Over the past years I have written many dozens of articles about Data Analytics and Data Engineering, from my personal experience. I wrote randomly when I had time. I now proceeded to organize these articles by topics, with the intention of writing a book in the future (that would be my second book). Here is the first category: General introduction. I believe these materials can be interesting to managers and professionals who need basic understanding of Data Quality, Data Lakes, Data Analytics, Data Science and the general feeling of what this is about. Enjoy.

General introduction

• What makes Data Quality so difficult
• Practical AIOps: 5 use cases: the comprehensive design of advanced analytics for IT infrastructure support (Service Desk)
• Data Lake article series: Why Analysts need Data Lakes?, Data Lake: simple usage example and The Data Lake at Sopra Steria: why and when one should build a data lake
• Are people fair? How I used statistics to determine if users were honest (expected distribution, Monte Carlo simulation, Central Limit Theorem, oversampling)
• Don’t trust Data Science. Ask the people: This article is somewhat more technical, but it reveals how you should talk to people before trusting the tools. It is a story how a conversation with a domain expert led to discovery of a bug in popular Data Science library, scikit-learn.?