The field of data science is rapidly growing and attracting professionals from diverse backgrounds. One such group of professionals that have been transitioning into data science is experienced architects. While this may seem like an unlikely transition, the skills and knowledge that architects possess can be highly valuable in the field of data science.

  SQL (Structured Query Language) is a powerful tool for managing and manipulating large datasets, making it an essential skill for data scientists. While most data scientists are familiar with basic SQL queries, advanced SQL techniques can help you efficiently analyze and manipulate large and complex datasets. In this article, we will explore ten advanced SQL techniques that every data scientist should know.

Introduction A/B testing, also known as split testing, is a method of comparing two different versions of a webpage, app, or marketing campaign to determine which one performs better. It's a popular technique used by businesses to optimize their websites, increase conversion rates, and improve user engagement.

1. Twitter sentiment analysis and its importance for business analysis Sentiment analysis refers to identifying as well as classifying the sentiments that are expressed in the text source. It aims to analyze people's sentiments, attitudes, opinions, emotions, etc. towards elements such as products, individuals, topics, organizations, and services.

As a data scientist, you're likely familiar with the importance of using data to drive business decisions. But how can you apply this concept to the process of monetizing innovation? Madhavan Ramanujam's book, "Monetizing Innovation," provides a wealth of insights and strategies for doing just that.

I'm always looking for ways to improve my decision-making skills. That's why I picked up "Thinking in Bets" by Annie Duke. In this book, Duke, a former professional poker player and cognitive psychologist, applies the principles of poker to decision-making in all areas of life, including data science.

  In this article, I will describe how I went through the typical data analysis and transformation process and how I approached the well-known King Count House Sales data set. We will see how to fit and validate the linear regression model based on the house sales data, review and assess the four linear regression assumptions, predict and interpret the final results!