Here, @hanqi shares tips for beginners based on their invaluable experience working as an instructor at a Data Science bootcamp in Singapore. They introduce the sticky framework of GET, APPLY, MAINTAIN, EXPAND in this article.
Michael has combined his past experience as a heavyweight lifter, his trademark humor and what he has learned in the past year of learning Data Science to write this unique article.
It stands out for its easy-to-follow project structure, clean code and its efficient documentation (code commenting and function description), well-formatted output printing, and great storytelling, especially the visual representation of traffic by hour.
They have demonstrated a geniune curiosity to the data following a non-conventional path for project completing, digging deep into the data, obtaing interesting insights supplemented with plenty of cool and various visualizations.
He has created the ultimate guide to help you do your first unguided data project. He’ll soon post a series of articles showing you how to put this advice to practice. Look out for that!
It demonstrates a very detailed and profound data analysis applying both linear and ridge regression, comparing their results, clean and easy-to follow code, output format, and efficient variable naming.
It stands out for its excellent structure, emphasizing all the important points, completing extra tasks, using functions to optimize the code, and an efficient summary of results.
Guided Project reviewers:
One of the best ways to use this community is by checking out your peers Guided Projects and leaving a quick feedback on them.
You too can join the party by checking out some of the Guided Projects that are being shared in the community. It doesn’t even take too long! Here’s a playbook to help you.
Thanks a lot for participating in our community!
I am proud to reward you with:
A 7 day extension on your current Dataquest subscription
An exclusive Community Champions badge in our Community
They used both lasso and linear regressions, compared both approaches, and obtained interesting insights and conclusions. What’s more, their project stands out for its efficient code and excellent function documentation.
Here, they not only applied the recommended manual approach but also tried scikit-learn classifier, obtaining even higher accuracy in their experiment. Their work also demonstrates clean and perfectly commented code, cool storytelling, and mathematical references.
Guided Project reviewers:
This week I want to highlight @LucaVehbiu and @brayanopiyo18 for giving an awesome feedback to their peer on their Guided Project.
Thanks a lot for participating in our community!
I am proud to reward you with:
A 7 day extension on your current Dataquest subscription
An exclusive Community Champions badge in our Community
It stands out for its perfect structure, clear goals, efficient emphasizing, revising all the resulting tables in the summary of results, and clean code highly optimized by introducing various functions.
Guided Project reviewer:
This week I want to recognise @artur.sannikov96 and @Elena_Kosourova for continuing to provide helpful feedback to the projects shared in this community!
Thanks a lot for participating in our community!
I am proud to reward you with:
A 7 day extension on your current Dataquest subscription
An exclusive Community Champions badge in our Community
Congratulations to this week’s community champions. Let’s continue soldiering as we allocate more time and effort to our learning. The future is actually brighter than it seemed with well qualified and skilled data scientists. Thanks for this new initiative that aims at boosting our morale @nityesh
This stands out for its perfect structure, easy-to-follow code, digging much deeper into the data, excellent storytelling, very informative conclusion, and custom visualizations.
Adam’s work demonstrates profound data cleaning and analysis, fantastic visualizations, great storytelling, very curious findings, attention to details, and emphasis on the most important insights.
Here they are:
Thank You as always.
Happy coding!
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