I was recently contacted by a recruiter from a Big Tech company. Why now and never before? Few tips on how you can increase your chances.

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Photo by Mitchell Luo on Unsplash

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.


Start the New Year with one of the best New Year’s resolutions: Learn more pandas.

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Photo by Michael Payne on Unsplash

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.

In this article, I’m going to show you 5 pandas tricks that will make you more productive…


Python is evolving. Don’t get left behind!

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Photo by Michael Dziedzic on Unsplash

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.

You’ll learn:

  • How to format big integers more clearly
  • What are Magic commands in IPython
  • A simple way to debug code
  • A better way to work with file paths
  • The proper way of string formating

1. Underscores in Numeric Literals


These tips are also applicable to Software Engineers. Make a few changes in your CV and land that job!

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Photo by Christina @ wocintechchat.com on Unsplash

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.

1. Beautiful Design


Only a fool learns from his own mistakes. The wise man learns from the mistakes of others.

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Photo by shiyang xu on Unsplash

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:

1. How NOT to visualize a Weighted Average


These 8 tips will help you to spot bugs before training a Machine Learning model.

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Photo by Michael Dziedzic on Unsplash

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. …


PyTorch and TensorFlow aren’t the only Deep Learning frameworks in Python. There’s another library similar to scikit-learn.

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Gif from giphy

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. …


A few tips on how NOT to start with Machine Learning. I also present a better way on how to start learning it.

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Photo by Justin Luebke on Unsplash

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:

  • Should I start learning ML bottom-up by building strong foundations with Math and Statistics?
  • Or top-down by doing practical exercises, like participating in Kaggle challenges?
  • Should I pay for a course from an influencer that I follow?

In this article, I give answers to the questions above and I also present a better way on how to start learning Machine Learning. …


This is my advice to all aspiring Data Scientists

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Photo by LinkedIn Sales Navigator on Unsplash

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.

What is the best way to learn and practice Data Science?

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.

Ask questions in Reddit communities or in Cross Validated community. …


Switching context from Data Science to Software Engineering can be challenging for Data Scientists.

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Photo by Grzegorz Walczak on Unsplash

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:

- Labeling and Data Engineering for Conversational AI and Analytics- Data Science for Business Leaders [Course]- Intro to Machine Learning with PyTorch [Course]- Become a Growth Product Manager

About

Roman Orac

Senior Data Scientist, tweeting twitter.com/romanorac.

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