Seven Tips for Success After Dataquest

CONGRATULATIONS! You just finished your Dataquest career path. But now what?! In this post, I will go over 7 tips on what to do after you have completed the Dataquest analyst/scientist/engineer career path. The best part is you do not even have to be finished with the career path to start using these tips. These tips can be helpful at any skill level.

Tip 1: Review Fuzzy Concepts

After finishing the data scientist path, I quickly realized there were still concepts that were not as clear as I would like them to be. A good tool for assessing your understanding is the PDF summaries that are available at the end of each learning section. Look through these summaries and take note of any concepts that may be a little “fuzzy.” After pinpointing these areas take some time to review and solidify your understanding. Try practicing these concepts on some data that you have not seen before. In fact, this leads us to tip number two – get data that you have not used before.

Tip 2: Get New Data

Practicing your new skills on some fresh data not only reinforces your skills but also provides you with opportunities to build our portfolio. It can be difficult to find data to practice on, luckily there is a great Dataquest blog post discussing where you can get access to data for free. As a side note, this does not have to be data that has never been used by anyone before. It should just be data you have not used before.

Tip 3: Just keep reading…

In addition to the Dataquest platform, there are plenty of web resources dedicated to teaching data science skills. For example, some of my favorite web resources include Toward Data Science, Real Python, Practical business Python, Dataquest blog and as you already know (since you are reading this article) Dataquest Direct. These web resources are a great way to learn something new and gain a deeper understanding of a particular topic. Additionally, these resources are also a great way to figure out where your knowledge gaps are by looking for articles discussing concepts that you are unfamiliar with.

Tip 4: Take Advantage of Your Local Library

While reading articles online can be an excellent way to learn about a specific topic, books can provide a big picture overview of the field and the skills you need. Books, however, can get expensive. That is why I love to take advantage of my local library and check out as many data-related books as I can. If you have a local library there is a chance that they have relevant books as well (and if not, you may be able to request your library purchase a book). Not only can books provide you with the technical details of data science they can also help us learn how to think like a data scientist. Here are a few books I would recommend:

  • Statistics Done Wrong by Alex Reinhart
  • The Art of Statistics: Learning from Data by David Spiegelhalter
  • How to Lie with Statistics by Darrell Huff
  • Naked Statistics by Charles Wheelan

Tip 5: Answer Questions

The first time I answered someone else’s data science question it was a rush! Not only is this a way to build confidence it can also help reinforce and deepen your understanding of that concept. If you do not know anyone who is learning Python, R, or data science that is okay because you can answer questions from other Dataquest users [https://community.dataquest.io/]. Stack Overflow is also a great place to find people with questions. There is no need to worry if you cannot answer the questions. Take a note of these questions and wait for someone else to answer them. Then check out their answers. This is a simple way to see how other people solve problems and structure their code.

Tip 6: Get Connected

If you are learning data science on your own it can be hard to know if your code is any good. In addition to looking at posted solutions on Dataquest and Stack Overflow it can be helpful to join a community. By connecting with a data-related community we can discuss our concerns, learn from others, help others, and even start networking.

If you are wondering where to find a community a great place to start is with an app/website called Meetup [https://www.meetup.com/]. This website helps you find local groups that share similar interests as you.

In addition to local meetups there are plenty of groups on Facebook (just search for Python, Data Science, R Programming, Data Analytics) or you could even join the discord community of data enthusiasts that I started.

Action 7: Apply for Jobs

Start applying for jobs. This tip may seem like a no-brainer, but applying for a job can be the scariest part of this journey we are on! What if people laugh at my resume, what if I get rejected, what if I am not ready… there are a plethora of excuses that can prevent us from putting ourselves out there and applying for data science jobs (even after we have finished Dataquest).

As a Career Moderator at Dataquest, I have seen my fair share of resumes. Some of these resumes are incredible while others need a little work. For those who have access to the premium community features take advantage of Dataquest’s career services. We are here to help you as you work towards getting a data job. Are you worried your resume does not capture your true capabilities and passion? Ask a career moderator. Are you worried you do not have the skills needed for a job? Ask the career moderator. Are you worried about the job interview? Yup, that is right, ask a career moderator. As a general guideline, if you have completed a Dataquest career path and can apply what you have learned on a different dataset then you are probably capable of an entry-level position (and no you do not have to have everything memorized).

Conclusion

Dataquest gives you a great start, but it is just the beginning. Following these tips can keep your skills sharp and help you to continue growing on your data journey (or should I say your data quest!).

Photo by Sam Dan Truong on Unsplash

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Thanks for sharing @bvalgard! Even though I’m quite far away from completion, I still think many of the points are applicable.

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Thanks @masterryan.prof!

This is great @bvalgard! Thanks a lot for sharing your tips with us. :heart:

About Tip 2 -

In the past few months, our community has also become a great source of getting new data you have not used before. That’s because more and more people are sharing their personal projects with the community. Head over to the personal-project tag to find them all!

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Also, would love to read another article from you based on your experience as a Career Moderator in our community. You’ve reviewed tens, maybe hundreds of resumes - I’m sure you’d have valuable knowledge that our community would love to know! :grinning_face_with_smiling_eyes:

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Hi @nityesh,

Thanks sharing this additional source of data!

Also great topic suggestion. I’ll see what I can come up with :smiley:

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Thanks for sharing @bvalgard! Another good tip that can be scary for many beginners (I was one of them) is to share your work (even if it’s something small) on LinkedIn, Twitter, DQ Forum, etc. Your work can be much much better than you think.

When I shared my article on how I automated a non-profit with Python on LinkedIn it became probably the most popular post I’ve ever shared in my life in just one week. And then even offline I received feedback from a couple of people telling me that my work was really cool.

Don’t be afraid to say that you did something cool, interesting, and worth sharing!

Another useful tip is to start building your local library of articles/tutorials/interesting solutions. If you consume a lot of Python material you will tend to forget most of it, so if you find something useful save the link in, for example, Google Sheets and whenever your start working on a new project reference your local library, you may find something very useful:)

Happy coding everyone :smile:

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Hi @artur.sannikov96 Those are great tips, thanks for sharing!!

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Thank you for sharing these great tips…Cant wait to access the Dataquest’s career services soon.

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@okorochukwunonso Thanks!

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This piece of advice is so SO powerful, Artur! :heart: :fire:

It can help learners market themselves and their skills.

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