I was recently contacted by a recruiter from a Big Tech company. Why now and never before?
In this article, I present my theory of why a recruiter contacted me for a Senior Data Science position. You can use my theory (and develop it further) to increase your chances of getting contacted by Big Tech company.
Many Software Developers dream about working for a Big Tech company. How do I know? I was one of them.
Pandas needs no introduction as it became the de facto tool for Data Analysis in Python. As a Data Scientist, I use pandas daily and it never ceases to amaze me with better ways of achieving my goals.
For pandas newbies — Pandas provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
The name pandas is derived from the term “panel data”, an econometrics term for datasets that include observations over multiple time periods for the same individuals.
In this article, I’m going to show you 5 pandas tricks that will make you more productive…
Start the New Year with one of the best New Year’s resolutions: Learn more Python.
You can start with this article in which I present 5 Python tricks that will make your life easier.
Writing a good CV can be one of the toughest challenges of job searching.
Most employers spend just a few seconds scanning each CV before sticking it in the Yes or No pile.
Here are the top 5 tips that will increase the chances that your CV lands in the Yes pile.
Have you ever planned you’d need an hour to finish a short task, but then you spend a whole day working on it? If yes, welcome to my world!
In this article, I present 3 pandas mistakes that took me much longer to solve than they should. I also share the link to the Notebook with examples at the end of this article.
Only a fool learns from his own mistakes. The wise man learns from the mistakes of others.
See my pandas articles to learn more about Data Analysis with pandas:
One of the most common misconceptions in Machine Learning is that ML Engineers get a CSV dataset and they spend the majority of the time optimizing the hyperparameters of a model.
If you work in the industry, you know that’s far from the truth. ML Engineers spend most of the time planning how to construct the training set that resembles real-world data distribution for a certain problem.
When you’ve managed to construct such training set, just add a few well-crafted features and the Machine Learning model won’t have a hard time finding the decision boundary.
In this article, we’re going to go through 8 Machine Learning tips that will help you to train a model with fewer screw-ups. These tips are most useful when you need to construct the training set, e.g. you didn’t get it from Kaggle. …
scikit-learn is my first choice when it comes to classic Machine Learning algorithms in Python. It has many algorithms, supports sparse datasets, is fast and has many utility functions, like cross-validation, grid search, etc.
When it comes to advanced modeling, scikit-learn many times falls shorts. If you need Boosting, Neural Networks or t-SNE, it’s better to avoid scikit-learn.
scikit-learn has two basic implementations for Neural Nets. There’s MLPClassifier for classification and MLPRegressor for regression.
While MLPClassifier and MLPRegressor have a rich set of arguments, there’s no option to customize layers of a Neural Network (beyond setting the number of hidden units for each layer) and there’s no GPU support. …
Every day there’s more and more educational content about Machine Learning. With such a high volume of new content, it’s easy to get confused. Many aspiring Data Scientists don’t know where or how to start learning.
These three questions pop up regularly in my inbox:
In this article, I give answers to the questions above and I also present a better way on how to start learning Machine Learning. …
I get many messages asking for advice from aspiring Data Scientists. I am no expert in career advising so take everything that I write with a grain of salt.
I give advice based on my observations of the field and the experience that I’ve developed over the years. This is me, advising younger me as I had similar questions at the start of my career.
My advice would be to start with practical projects and then slowly progress with theory. Kaggle notebooks are a great way to learn the practical part.
Switching context from Data Science to Software Engineering can be challenging for Data Scientists. While the Software Engineering that Data Scientists perform is usually not too complicated, it poses its unique challenges.
In this article, we’re going to go through 7 challenges that Data Scientists need to be aware of when doing Software Engineering. With each challenge, I also show a recommended solution.
Here are a few links that might interest you: