Dec

Produce clean code by refactoring common code smellsPhoto by Markus Spiske on UnsplashLack of experience, rushed deadlines, or missed code reviews are just a few factors that might cause you to create ill-conceived code, the so-called “code smell”.To fix these “smells” you have to open your IDE, examine your code, and find out which part of your code is affected.Although a code smell does not break your app instantly, it may lead to errors in the future because it might make the code unreadable and unmaintainable.…

These goals will help you set up and organize your upcoming year of becoming a data scientistPhoto by Zoritsa Valova on UnsplashIf you’re anything like me, December is the month that is dedicated to preparing for the upcoming year. Whether it’s cleaning up my computer files, donating old items I didn’t use in the past year, or purchasing the books I’ll be reading for the upcoming year, I believe that December should be set aside for preparation so that you can enter January organized, focused, and without any baggage from…

Master these libraries for a smoother career pathImage by 200 Degrees on PixabayIf you want to study Python for Data Science to start a new career, I’m sure you are struggling with all these things to know and master. I know you are overwhelmed by all these new concepts, including all the mathematics you should know, and you may feel you’ll never arrive at the goal of your new job.I know: job descriptions do not help with that. It really seems like Data Scientists must be aliens; even juniors, sometimes.In my opinion, an…

Coffee Data SciencePivoting on what makes great espressoIn my previous Moka pot study, I found large holes were effective for espresso brewing without coffee migrating through the basket. So I wanted to modify a basket with an aim at imprecision. Most of the espresso community has pivoted towards precision baskets. The original investigation into precision baskets aimed for a particular shot time not necessarily extraction yield.My previous work has looked at filter baskets based on how much area of the basket is open. I…

5 Ways to Level Up Your Data Science Skills with PandasPhoto by billow926Pandas is a powerful and versatile Python library for data science. It is often one of the first libraries you come across when learning how to use Python for data science applications. It can be used to load data, visualise it and manipulate it to suit the objectives of the project you are working on.However, many people don’t go beyond the basics of how to use the library and don’t take advantage of some of the more advanced and interesting…

List is a core data structure for PythonPhoto by Kelly Sikkema on UnsplashData structures are of great importance for any programming language because how you store, access, and manage data is one of the key factors in designing efficient programs.Python has 4 built-in data structures:List is represented as a collection of data points in square brackets and can be used storing values with different data types. Here is an example of a list:mylist = In this article, we will learn 4 methods for adding new items to Python…

This new language model could be the pair programmer of your choice going forwardImage by author — created with Stable DiffusionTwo days ago OpenAI released ChatGPT, a new language model which is an improved version of GPT-3 and, possibly, gives us a peek into what GPT-4 will be capable of when it is released early next (as is rumoured). With ChatGPT it is possible to have actual conversation with the model, referring back to previous points in the conversation.I wanted to try out if I could use this model as a pair…

Proactively delivering real insights into metrics changesNote: The term root-cause analysis is commonly used in IT and data engineering as the process to identify root causes of faults or problems. This article focuses on diagnostic analytics to understand the drivers of business metrics changes.Image by rawpixel.com on FreepikWhy is revenue down? Why did the conversion rate spike? Why is the average order value flat?Depending on your industry and business goals, your key metrics might be different. But if you care about…

Not that good, but we are getting thereImage from PixabaySince the release of GPT-3, several other large language models have been introduced and evaluated in machine translation.But how good are large language models at translation compared to the standard machine translation encoder-decoder approach?Unfortunately, we can’t easily answer this question.The evaluations of large language models for translation are often too sloppy or incomplete to draw credible conclusions.To have a clear answer, a research team from Google…

https://pixabay.com/Part 6: Documents EmbeddingIn the latest articles of this series, we have often said that text data, in order to be used as input of mathematical models (the ultimate goal of many NLP applications) need to be somehow converted into a numerical representation. We have seen some of them, like TF-IDF matrix or bag-of-words. However, those techniques are not “informative” when it comes to capturing the meaning of a word or document.This is when Document Embedding comes into the game. The general idea of…