Data Science & Machine Learning

Coding

Data Bias

The section below contains a bullet point summary from this excellent article from geckoboard...

https://www.geckoboard.com/best-practice/statistical-fallacies/https://www.geckoboard.com/uploads/data-fallacies-to-avoid.pdf

Cherry Picking

The practice of selecting results that fit your claim and excluding those that don’t. The worst and most harmful example of being dishonest with data.

Data Dredging

The failure to acknowledge that the correlation was in fact the result of chance.

Survivorship Bias

Drawing conclusions from an incomplete set of data, because that data has ‘survived’ some selection criteria.

Cobra Effect

When an incentive produces the opposite result intended. Also known as a Perverse Incentive.

False Causality

To falsely assume when two events occur together that one must have caused the other.

Gerrymandering

The practice of deliberately manipulating boundaries of political districts in order to sway the result of an election.

Sampling Bias

Drawing conclusions from a set of data that isn’t representative of the population you’re trying to understand.

Gambler's Fallacy

The mistaken belief that because something has happened more frequently than usual, it’s now less likely to happen in future and vice versa.

Regression Toward the Mean

When something happens that’s unusually good or bad, over time it will revert back towards the average.

Hawthorne Effect / Observer Effect

When the act of monitoring someone can affect that person’s behavior.

Simpson's Paradox

A phenomenon in which a trend appears in different groups of data but disappears or reverses when the groups are combined.

McNamara Fallacy

Relying solely on metrics in complex situations can cause you to lose sight of the bigger picture.

Overfitting

A more complex explanation will often describe your data better than a simple one. However, a simpler explanation is usually more representative of the underlying relationship.

Publication Bias

How interesting a research finding is affects how likely it is to be published, distorting our impression of reality.

Danger of Summary Metrics

It can be misleading to only look at the summary metrics of data sets.

Bibliography

https://towardsdatascience.com/parking-spot-detection-using-mask-rcnn-cb2db74a0ff5https://github.com/academic/awesome-datasciencehttps://www.geckoboard.com/best-practice/statistical-fallacies/https://www.geckoboard.com/uploads/data-fallacies-to-avoid.pdf
GANhttps://towardsdatascience.com/build-a-super-simple-gan-in-pytorch-54ba349920e4https://towardsdatascience.com/generative-adversarial-networks-gans-2231c5943b11
Flaskhttps://towardsdatascience.com/streamlit-can-not-yet-replace-flask-the-streamlit-2020-roadmap-64840564acdehttps://devpost.com/software/built-with/flask
Streamlithttps://towardsdatascience.com/streamlit-can-not-yet-replace-flask-the-streamlit-2020-roadmap-64840564acdehttps://streamlit.io/gallery
WPS Analyticshttps://www.worldprogramming.com/products/wps-analytics-platform

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