Tag: Data Science

Excel + Office Scripts

If you have worked with Excel automation in the past, you may have painful memories working with complex Excel functions or VBA codes. Recently I realized newer versions of Excel support Office Scripts, which changes my impression of Excel automation completely. Some highlights of Excel + Office Scripts: ✔️ Can be run on the cloud,… Continue reading Excel + Office Scripts

Makefile

A small titbit to share today, the Makefile. A Makefile can be used to define sets of commonly used commands to save time and to ensure the commands run in the correct order with needed pre-requisites. For example, you can define a list of build-related commands under a target called “build”. Then next time you… Continue reading Makefile

Rocket League reinforcement learning-trained bot

As a passionate gamer, I have been reading about the Rocket League Nexto cheat situation with keen interest (https://kotaku.com/rocket-league-machine-learning-cheating-nexto-bot-1849980593). For those unfamiliar with games, Rocket League is a competitive online game where players control cars to play football. Someone has built a bot trained via reinforcement learning, and offered it as a cheating solution to… Continue reading Rocket League reinforcement learning-trained bot

Timeline of LLM

“Classic quant signals might work, but you can’t explain them; ChatGPT might not work, but it can explain itself. In a sense this is the opposite of a classic “black box” machine-learning investment algorithm.” This is an interesting take by Matt Levine, on the ability of ChatGPT to be an asset manager (https://www.bloomberg.com/opinion/articles/2023-01-26/chatgpt-is-not-much-of-a-pitch-robot). One of… Continue reading Timeline of LLM

Things they didn’t teach you in software engineering

Whenever you feel disillusioned about the mismatch in promises between what you were taught in university/bootcamp versus what you actually worked on in a job. I recommend reading this article, https://vadimkravcenko.com/shorts/things-they-didnt-teach-you/. It is written for software engineering, but many points mentioned apply to data science/analytics as well.

Data science solutions – Build vs buy

Many data scientists are working in companies with less advanced technology infrastructure. But these companies still wish to solve their business problems with the help of data science / analytics. At this point usually a question arises, “Should we build or buy a solution to solve this problem with data science?”. As nicely pointed out… Continue reading Data science solutions – Build vs buy

Being a data scientist in consulting

A very accurate and vivid description of being a data scientist in a consulting context. I can definitely say that I have experienced this myself, and it does take a while to get used to presenting comfortably (therefore confidently). But like it or not, the holy grail in data science has always been about connecting… Continue reading Being a data scientist in consulting

Delegating to chatGPT & Stable Difussion

This week, I am jumping on the bandwagon by delegating the task of creating my LinkedIn post to chatGPT (for text) and Stable Diffusion (for image) with the following prompts. ⌨ chatGPT : Can you write an opinion article, discussing your opinion on this topic, “In the future, do you see a shift in companies… Continue reading Delegating to chatGPT & Stable Difussion

State of DS Survey by Anaconda – Skill Gaps

According to the State of Data Science survey done by Anaconda, the top 5 most important skill gaps in data science are: ⭐️ Engineering skills ⭐️ Probability and statistics ⭐️ Business knowledge ⭐️ Big data management ⭐️ Communication skills These skill gaps cut across multiple knowledge domains, from technical skills to soft skills, reflecting the… Continue reading State of DS Survey by Anaconda – Skill Gaps