In 2014, a small instant messaging company with about 50 employees was acquired for $19 billion – more than the GDP of Afghanistan at the time. The same year, Google acquired a London-based startup who’s product beat the world’s best chess

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Applied Data Science with Python | Balachandar Kasilingam
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Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.

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JavaScript is a web programming language that is designed and used to make interactive web pages. Interactive is a fancy word which can be boiled down to the interaction between the user and the web page. This interaction is based on the functionality of the web pages and not any manual intervention

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A Deep Dive in Web Architecture | GUVI
https://youtu.be/EIO6jHb-75Q

Web architecture is the conceptual structure of the World Wide Web. The WWW or internet is a constantly changing medium that enables communication between different users and the technical interaction (interoperability) between different systems and subsystems.

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DATA SCIENCE INTERVIEW PREP

5 Questions that actually get asked in DS interviews — with clean, no-fluff answers

Q: What is the difference between bias and variance?*
Bias = error from wrong assumptions (model too simple → underfitting).
Variance = error from sensitivity to training data (model too complex → overfitting).
Goal: minimize both → the "bias-variance tradeoff."

Q: Explain the difference between L1 and L2 regularization.*
L1 (Lasso): adds |weights| penalty → can shrink coefficients to exactly 0 → useful for feature selection.
L2 (Ridge): adds weights² penalty → shrinks coefficients smoothly, never to 0 → useful when all features matter a bit.

Q: How do you handle imbalanced datasets?*
- Resampling: SMOTE (oversample minority) or undersample majority
- Use metrics beyond accuracy: Precision, Recall, F1, AUC-ROC
- Class-weighted loss functions
- Anomaly detection framing if imbalance is extreme (e.g., fraud)

Q: What is the Central Limit Theorem and why does it matter in DS?*
CLT: the sampling distribution of the mean approaches a normal distribution as sample size grows, regardless of the population's original distribution.
Why it matters: lets us use normal-distribution-based tests (t-tests, confidence intervals) even on non-normal data, as long as sample size is large enough (usually n ≥ 30).

Q: You have a model with 95% training accuracy but 65% test accuracy. What's happening and how do you fix it?

Classic overfitting.
Fixes:
- Add regularization (L1/L2)
- Reduce model complexity / prune features
- Get more training data
- Use cross-validation
- Apply dropout (for neural nets) or early stopping

Which one tripped you up? Drop your answer in the comments before scrolling up

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