Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult. These teams may not have the budget, skills, or IT support needed to successfully implement a data management application. In this series, I will explain a principled approach to home-grown data management that has a low technical barrier to entry and is platform-agnostic: the Data Library.
A Comparison of Tableau and Power BI, the two Top Leaders in the BI Market
Why you should forget loops and embrace vectorization for Data Science
Insights to Agile Methodologies for Software Development
Greedy Algorithm and Dynamic Programming
Cloudera vs Hortonworks vs MapR: Comparing Hadoop Distributions
Introduction: Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.
Introduction: Certain skill sets suit certain positions better than others, and this is why the path to data science is not uniform and can be via a diverse range of fields such as statistics, computer science and other scientific disciplines. This series aims to present 3 aspects of ‘How to become a Data Scientist’ starting