9 ways to Level up your Data Science practice 9 ways to Level up your Data Science practice
We love reading articles with tips and best practices, and we agree with a lot of the advice we see out... 9 ways to Level up your Data Science practice

We love reading articles with tips and best practices, and we agree with a lot of the advice we see out there (#5 on this list is great!). So, we asked the Domino team for advice to pass on to researchers and scientists searching for ways to get to that next level, and here’s what we heard:


1. Learn ways to parallelize your code.

You already know about map-reduce frameworks like Hadoop and Spark, but these might be an overkill for the sizes of datasets you’re working with. Consider leveraging a machine with multiple cores, or a GPU, to dramatically speed up calculations on “medium size” data. You can use joblib in Python; in R, the ‘parallel’ package and ‘foreach’ package are great (there are many tutorials on the topic, here is one) for parallelization on CPUs.

Certain kinds of data science tasks can also benefit from GPUs. A lot of deep learning packages already use GPUs to massively accelerate the training process, but there’s a lot more potential there. Explore some libraries like BIDMach, gpustats, PyCUDA, and gputools, to accelerate the rest of your code.

2. Explore Flask alternatives for creating cool dashboards in Python.

Shiny is THE way for R users to create nifty dashboards and interactive visualizations. The de facto tool to do that in Python is Flask, but Flask is meant for much heavier lifting. The Python community has been at work. Check out Bokeh, Plotly, Jupyter dashboard, Pyxley, or Spyre to accelerate the delivery of your insights to your business users.

<h4>3. Form a reading group for discussing arXiv and academic papers.</h4>
<p>Even if you read only a few a year, papers will expand your mind from the tools and methods that you already know. <a href=Here’s a list to help you get started.



4. Get plugged into a data science community.

 /> There are tons of data science communities out there via meetups, IRC channels, Slack groups, Twitter, and on Reddit. If you don’t have one in your area, start your own! Get plugged in, and participate in the discussion. These communities may offer great opportunities to volunteer or mentor individuals who want to learn about the field. Online, you can start with <a href=Hackbright Academy and rOpenSci.


5. Compete in a Kaggle competition!

 /><a href=Kaggle is a stellar place to try out new tools, techniques and technologies (like Domino!). Find a Kaggle competition you like, preferably in a field different than your own and see what you can accomplish. Hang out on the forums, join a team, and most importantly have fun. Find out more on their website.


6. Clean up your Github and social profiles.

More often than not, people will Google you as soon as they meet you..probably while you are still standing there! Take a few minutes and clean up your Github, add a cool header to your Twitter profile, and make that first virtual impression strong. You never know what kinds of opportunities arise from having the right person find out that you’re the unicorn of their dreams.


7. Submit a talk at a conference or event.

 />Even if you don’t get chosen or you’ve never spoken, you may find that you’d like to present your talk at a local meetup. This is the perfect forum to give a 10-minute lightning talk. If you’re a seasoned speaker, polish your best talk or find a topic outside of your comfort zone. Data science often relies on great communication to deliver business impact. This is also a great line to add to your CV or LinkedIn profile.</p>
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<h4>8. Start listening to these podcasts during your commute or at the gym.</h4>
<p>We like <a href=Partially Derivative, Data Skeptic, and Becoming a Data Scientist. There’s some amazing content being created by data scientists for data scientists and available through your favorite podcast app.


9. Read Weapons of Math Destruction to understand how the work we do can impact our society and culture.

 />The work we do is often deep and complex, yet abstracted away from the direct human impact. This book should help remind us that the work we do is uniquely capable of creating massive ripples in the work around us.</p>
<p>We hope you have a fantastic 2017 and we’d love to hear from you in the comments below. What are you planning to tackle this year?</p>
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