They mix languages with tools, and in the worst cases with soft skills — saying your ‘4.5 stars’ at Leadership isn’t hel

I hope this post has helped you in some way. If you want to give DataQuest a try, then just check their website and start their free trial. As a disclaimer, I don’t receive any compensation from DataQuest or any other platform mentioned in this post in exchange for my recommendation. So, let’s get to work.

I’ve seen some beautiful CVs (I’ve saved a few of these for personal inspiration) but I’ve also received text files (.txt) that lack any formatting. Working on your CV can be a pain, and if you’ve chosen data science as your endeavor there’s a good chance you don’t enjoy creating aesthetic designs in your spare time. Without going overboard, you do want to look for a nice template that enables you to get everything across in limited space. Use the space wisely — it’s useful to split the page and highlight specific sections that don’t fall under the chronological work/education experience. This can include the tech stack you’re familiar with, a list of self-projects, links to your github or blog and others. A few simple icons can also help with emphasizing section headers. Many candidates use 1–5 stars or bar charts next to each language/tool they are familiar with. Personally, I’m not a big fan of this approach for several reasons:

In conclusion, if you are in a career transition to Data Science, probably in your 30s, then you don’t want to waste time on questions that already have a clear answer. For this reason, I have outlined what I wish someone told me before starting my career change:

I am sure you have watched a video on how to write a function or a code. But, have you watched it whilst trying to understand the logic behind a code and typing on your keyboard? As Python and functions get more complicated, learning from videos, do not work. As a consequence, you will often have to watch over and over the same explanation, which is less effective and time-consuming. Basically, you will spend a lot of time pausing the video and trying to find the precise minute/second you want to start watching again. This is annoying.

I hope this post has helped you in some way. If you want to give DataQuest a try, then just check their website and start their free trial. As a disclaimer, I don’t receive any compensation from DataQuest or any other platform mentioned in this post in exchange for my recommendation. So, let’s get to work.

Watching a video tutorial seems the preferred learning method of the 21st century. It is easy to find a video online; you only have to click on play and could even multitask. However, when learning data science and programming, watching videos is NOT the optimal learning format.

- Monthly membership and a bit expensive: in this category, you can choose from three platforms (DataCamp, Codecademy or DataQuest). All of those platforms offer a Data Science with Python track.

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coroutines, shutdown_default_executor() and coroutine asyncio.to_thread() have been added. The shutdown_default_executor schedules a shutdown for the default executor that waits on the ThreadPoolExecutor to finish closing. The asyncio.to_thread() is mainly used for running IO-bound functions in a separate thread to avoid blocking the event loop.

I’m going to quickly run through your CV to look at your previous positions and see which are marked as ‘Data Scientist’. There are some other adjacent terms (depending on the role I’m hiring for), such as ‘Machine Learning Engineer’, ‘Research Scientist’ or ‘Algorithm Engineer’. I don’t include ‘Data Analyst’ in this bucket as the day-to-day work is typically different from that of a Data Scientist and the Data Analyst title is an extremely broad term. If you’re doing data science work at your present job and you have some other creative job description, it’ll probably be in your best interest to have your title changed to a Data Scientist. This can be very true for Data Analysts who are de facto Data Scientists. Remember, even if the CV contains descriptions of the projects you’ve worked on (and they include machine learning), a title other than Data Scientist will add unnecessary ambiguity. Additionally, if you’ve undergone a data science bootcamp or full-time masters in the field, this will probably be considered the beginning of your data science experience (unless you worked in a similar role earlier, which will warrant questions at a later stage).

Managing Riskified’s Data Science department entails a lot of recruiting — we’ve more than doubled in less than a year-and-a-half. As the hiring manager for several of the positions, I also read through a lot of CVs. Recruiters screen through a CV in 7.4 seconds, and after recruiting for several years my average time is pretty fast, but not that extreme. In this blog, I’m going to walk you through my personal heuristics (‘cheats’) that help me screen a resume. While I can’t guarantee that others use the same heuristics, and different roles will differ in the importance of each point, paying attention to these points can help you conquer the CV screen stage. Additionally, some of these heuristics may not seem fair or could potentially overlook qualified candidates. I agree that talented Machine Learning practitioners who don’t invest in their CV could get rejected with this screen, but it’s the best tradeoff considering the time. Remember, a highly sought after position may attract a hundred or more CVs. If you want an efficient process, the CV screen has to be quick.

That said, I strongly recommend DataQuest because their lectures are not videos but texts. You have to read and then start typing what you read. It seems a bit old school, but it works really well for beginners, who require more time to process what they are learning. They provide you with the instructions and a script area to test your code on the same screen. Their format is simply perfect for those who are seeking to optimise their study time.

What’s your formal education and in what field. Is it a well-known institution? For more recent grads, I’ll also look at their GPA and whether they received any excellence awards or honors such as making the Rector’s or Dean’s list. Since Data Science is a wide-open field without any standardized tests or required knowledge, people can enter the field in various methods. In my last blog, I wrote about the 3 main paths taken into the field and based on your education and timing, I’ll figure out which one you probably took. Hence, the timing helps understand your story — how and when did you transition into data science. If you don’t have any formal education in data science, that’s fine, but you need to either demonstrate a track record of work in the field and/or advanced degrees in similar fields.

My recommendation is to try DataQuest. Not only for the well-structure curriculum but also for something that might surprise you and is what brings me to the third and last thing I wish someone told me before.

Last but not least, the DataQuest curriculum is created based on real-life data, such as Android