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Data Job Search Tips! - DSBoost #29
💬 “Interview” of the week
This time our “interview” will be a little bit different.
The other day, an intern in my company asked me about Data Science.
Here is our conversation:
What are some common mistakes people make when learning, and how can they be avoided?
One mistake is constantly searching for resources. While there are many great courses, books, and videos available, it's important to choose one and stick with it. Additionally, certificates aren't as crucial as the actual learning process. Avoid doing a course just to obtain a certificate; focus on learning instead.
What are the best courses or learning tools you recommend?
I personally like Coursera and any of Andrew Ng's courses. Moreover, you can learn something new every day on platforms like Twitter or LinkedIn. Building connections in these networks can also be beneficial for the future.
How do you stay up-to-date with current technologies and continue learning?
I subscribe to various newsletters, which is an excellent way to stay current. Additionally, I actively create content on Twitter, maintain a blog, and send out newsletters. This not only helps me learn new things but also keeps me accountable as I have a community expecting updates from me. These practices have significantly contributed to my continuous learning and staying up-to-date.
Any advice for someone starting to learn more about the field?
Take your time, don't rush. Ensure you thoroughly understand concepts before moving on to more complex topics. Remember, theory alone is useless without practical application. Build models and deploy them to gain valuable experience.
Do you recommend doing Kaggle competitions for practice or to enhance one's CV?
Participating in Kaggle competitions can be very beneficial for practice. You can learn a lot from the experience.
What are the top skills that employers or you look for in a candidate?
Knowledge about model deployment is a crucial skill that employers and I look for. This will vary from position to position of course!
Do you think pursuing a master's degree is necessary to break into the field?
While having a master's degree may be considered by many employers, it's not essential. However, it can be advantageous.
I'm interested in research. Should I pursue it?
If you are passionate about research and don't mind the potential financial constraints, then it's worth considering. Personally, I'm not a big fan of research due to the constant concern about grants. But if you enjoy working with cutting-edge technology, it might be a suitable path for you.
What experience and skills should I aim for to increase my chances of being hired after graduation?
Make sure you have a comprehensive understanding of every stage of the data science process. Focus on data processing, as it's a key stage. Also, learn how to deploy models and work on real-world projects to showcase your abilities effectively.
🧵 Featured content
DSist, remote job, posted 1 hour ago, 644 applicants. As soon as you see this impostor syndrome kicks in and you conclude that you have zero chance to get this job.
This is not true.
In similar roles usually less than 5% of applicants are qualified. (As one response states)
As the top comment says:
I was in the position to interview candidates. We had almost 1000 applications after a week of posting. Most were incompetent or had trouble communicating during technical interviews. Good candidates were rare. Good candidates with pleasant personalities were even rarer.
Of course, you need to have the required skillset and good communication skills, after this,
Have a great CV that is easy to read and contains no typos.
Write a great cover letter.
Have a clean LinkedIn profile.
Too general? I know, but most applicants will not take these basic steps!
A few notes on this high number of applicants:
LinkedIn numbers are inflated:
This is a repost, so the number of applicants from the previous post is included.
As soon as one clicks the “Apply” button, the number increases. Even if you don’t hand in your application.
If you click on “Apply“ 5x, the number will increase by 5.
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🎙️ Podcast of the week
How Data Science Can Sustain Small Businesses with Kendra Vant, Executive GM Data & AI Products at Xero
Being proficient with data, understanding its nuances, spotting statistical lies, and grasping statistical applications will become increasingly vital in everyone's professional life over the next years.
Ensuring that data is tidy and easy to analyze is critical, especially for small business owners who might not have the resources for extensive data cleaning.
Data cleaning starts already at the generation. Make sure that the recorded data is consistent, easy to work with, and try to minimize mistakes.
Don't be afraid to revisit your initial assumptions or methods; sometimes, starting over with a fresh perspective can be beneficial. Approach data analysis with a curious and open mind, believing in your ability to learn and uncover insights.
Even the smallest businesses can benefit from data and automation, especially with new AI tools. All businesses have finances at least, so data analysis can be applied.