6 rules for daily happiness:
1. Start each day with gratitude—write it down.
2. Let go of grudges; they weigh you down.
3. Pursue what excites you, not what’s safe.
4. Spend time with people who lift you up.
5. Do something kind for others—daily.
6. Find joy in the little things; they add up.

Learn Data Science in 2024

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Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

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There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

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If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

Join @datasciencefree for more

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Here’s a detailed breakdown of critical roles and their associated responsibilities:

? Data Engineer: Tailored for Data Enthusiasts

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1. Coding Proficiency: Develop robust programming skills.
2. Model Training: Understand the intricacies of model training.
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? ML Engineer: Tailored for Deployment Enthusiasts

1. Model Packaging: Acquire knowledge of essential packaging techniques.
2. Model Registration: Master the process of model tracking and registration.
3. Model Containerisation: Understand the principles of containerisation.
4. Model Deployment: Explore strategies for effective model deployment.

I have curated the best interview resources to crack Data Science Interviews
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Today's Question :
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?

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