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Why I rejected β€œstrong” Data Scientists for this role πŸ’»πŸ›‘οΈ When I was hiring for Senior Data Scientist roles, I noticed the same pattern again and again in interviews. Many candidates had solid tec
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This SQL question shows up in almost every interview. Finding duplicate records is not about memorizing queries. It’s about explaining how you think. In interviews, what matters is: β€’ Understanding
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A strong ML project is not enough. Most candidates get rejected because they explain *what they built* instead of *why it mattered*. In 2026, interviewers care about: β€’ Business context β€’ Trade-off
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Classification vs Regression is one of the most basic ML interview questions β€” but many candidates still explain it poorly. The simple difference: Regression predicts continuous numerical values. Ex
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Most candidates memorize RΒ² and Adjusted R²… But in interviews, the real test is whether you understand why Adjusted RΒ² exists. RΒ² tells you how much variance in the target variable is explained by
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Most people know Linear Regression uses MSE. But very few can clearly explain why. This is one of those ML interview questions that looks basic, but tests whether you actually understand the fundamen
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#nevergiveup #motivation #careeradvice #techcareers #interviewtips
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Not all data is meant to be viewed on the same scale πŸ‘€ Log transformation can make messy, highly skewed data far more meaningfulβ€”and that’s where better analysis begins. #datascience #dataanalysis #
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Data Science Roadmap from Data Science Manager ……! #datascience #roadmap #sql #python #ai
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ML Interview Series If an interviewer asks β€œWhat assumptions does Linear Regression make?”, this is the cleanest way to answer it. Save this for your next ML interview. #machinelearning #explorepage
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Still confused about K-means? This is the simplest way to understand how it groups data points into clusters. I also break down: WCSS, the elbow method, and silhouette score Save this for later if y
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Most people confuse KNN and K-Means β€” but they’re completely different. KNN is a supervised algorithm: it predicts using the nearest labeled neighbors. K-Means is an unsupervised algorithm: it groups
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About @datascience.interview

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datascience.interview's engagement rate sits at 5.66%. That's 13% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. Content shows up about every 12 days. Captions lean moderate, averaging 413 characters.

5.66% engagement rate β€” 13% above average for 1K-10K follower accounts.
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Data Science Manager @ Akamai | Boston πŸ‡ΊπŸ‡Έ Real DS & ML Interview Questions Daily SQL β€’ ML β€’ Stats ⬇️ Free Interview Gu...

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