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The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.
1. Why a data scientist is not a data engineer?
Good post on the difference between data engineer and data scientist and why you need both roles in a data team. I chuckled when one of the sections had explanations around why data engineering != spark since I completely agree that these roles should be boxed around just one or two tools! read the full post here
2. Correlation vs Causation:
1 picture = 1000 words!
3. Best Practices from Facebook’s growth team:
Read Chamath Palihapitiya and Andy John’s response to this Quora question here
4. Simple mental model for handling for handling “big data” workloads
5. Five things to do as a data scientist in firt 90 days that will have big impact.
Eric Weber gives 5 tips on what to do as a new data scientist to have a big impact. Read here
Thanks for reading! Now it’s your turn: Which article did you love the most and why?Note from Paras: To support this blog and free content here, I partnered with Datacamp for affiliate marketing revenue. I personally use this platform (see my profile here) and highly recommend it. I wouldn't be endorsing them if it wasn't 10/10! Click on the link below to start your journey towards becoming the BEST data professional! Paras's datacamp referral link