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Data Science & Analytics

Analysis, notebooks, visualization, pandas, statistics

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Test yourself: Top 30 advanced Data Science & Analytics interview questionsMultiple choice, with the correct answer and why it is correct on every question. Free, no sign-in.

Advanced everything in Data Science & Analytics, page 4

Streaming Ingestion: Catching Data as It Happens
advanced2 min read

Streaming Ingestion: Catching Data as It Happens

Streaming ingestion is a conveyor belt for data, catching events as they happen instead of in batches. It's used for real-time fraud detection and IoT monitoring. The footgun is confusing ingestion (getting data in) with processing (acting on it).

advanced2 min read

Scraping Dynamic Sites: Find the API, Not Just Render

To scrape a dynamic site, find the hidden API call its JavaScript makes to fetch data instead of rendering the whole page. This is faster and more reliable. This applies when your scraper gets empty HTML but you see data in your browser.

R & Python Interoperability with Reticulate
advanced2 min read

R & Python Interoperability with Reticulate

Reticulate embeds a Python session inside R, letting you use Python libraries as if they were native R objects. Use it when a team uses both languages or you need a Python library in an R workflow.

Dask: Parallel Computing with Familiar APIs
advanced2 min read

Dask: Parallel Computing with Familiar APIs

Dask parallelizes Python analytics by breaking data into chunks and building a task graph of operations. It's like giving Pandas and NumPy superpowers for data too big for RAM. The footgun: its lazy evaluation means you must explicitly call .compute().

advanced2 min read

The Jacobian Matrix: A Derivative for Multiple Dimensions

The Jacobian matrix is the multi-dimensional version of a derivative. It's a grid of partial derivatives showing how a small change in each input locally affects each output of a vector function. Don't confuse the matrix with its determinant.

advanced2 min read

Uplift Modeling: Who to Target, Not Just Who Will Convert

Uplift modeling finds who to target by predicting the *change* in behavior from an action, not just the final outcome. It's used in marketing to decide who gets a discount, optimizing spend. The footgun is confusing it with a simple conversion model.

advanced2 min read

Causal Inference: Proving Cause, Not Just Correlation

Causal inference goes beyond correlation to ask "did X *cause* Y?". It's used to prove a new feature drove engagement or a drug improved outcomes. The main footgun is mistaking association for causation, ignoring confounding variables.

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