We make numerical calculations when the analytical solutions are not available. For example, if we flip a fair coin a large number of times, then we know “analytically” that the outcome will be heads or tails roughly equal the number of times (50% each). For complex systems, a numerical solution…


Before pursuing a career in data science, I was an aerosol researcher (mainly computational research). Although I entered a new world of data science, I found that the concepts (such as random numbers, distributions, modeling) and the best practices of scientific computing are the same across two disciplines. I picked…


Background

The use of distributed computing is nearly inevitable when the data size is large (for example, >10M rows in an ETL or ML modeling). If you have access to a Spark environment through technologies such as JupyterHub or DataBricks, then PySpark could be a good option when working with large…


As data scientists, we often help businesses by finding meaningful insights in the data. This could include predicting a valuable business indicator so that the decision-makers can take a certain decision. Well.. that’s the theory, but sometimes it is true. What is not true is that the decision-makers take our…


As data scientists, we write a lot of code to work with data. However, we do not necessarily write code for productizing (software-izing) the output, and sometimes rely on our respective organization’s IT or engineering team. This scenario could be challenging because of different priorities within the company. On the…

Anshuman Lall

Data Science and AI Advisor

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