Have you been tempted to put all you money on one hyped up stock, get rich and quit your job? If your answer is yes, this article is for you.

Why is diversification a no-brainer?

We all (hopefully) know that smart diversification is an essential part of a good investment plan. However, when we see assets skyrocketing, it is tempting to want to put all you eggs in one basket.

But here is why you should always diversify:


Building the intuition you need to master this important concept

Statistical Power is a foundational concept in a data scientist’ toolkit. If you want to incorporate it into your problem solving skills, you need to build an intuition for it.

First Things First

Statistical Power is the probability that we will correctly reject the Null Hypothesis.

Assuming that β is the probability that you fail to reject the null hypothesis when it is actually false, then Power equals 1 − β.

Here is how you can calculate it in Python with statsmodels:

from statsmodels.stats.power import TTestIndPower, TTestPowerpower_analysis = TTestIndPower()# solves for a given variable (n, effect size, alpha, etc)
power_analysis.solve_power(effect_size=.2, …


Corruption is a global problem that costs governments around the world 1 trillion dollars every year, according to the IMF. Can new technologies make this a problem of the past?

Photo by Possessed Photography on Unsplash

Corruption grows when accountability is low — it is hard to imagine a politician abusing their power for personal gain if they knew for certain that they would get caught and punished. This is why improving accountability is a wining strategy for fighting corruption, and Artificial Intelligence technology can help us do that.

Whether we realize it or not, AI technologies that spot wrongdoing are already all around us. Credit card companies, for example, have been using it for years — if your card is used in strange websites, to buy strange products, in a price range that is strange…


This is a simple and compelling metric for downside risk, especially during times of high market volatility

Drawdown measures how much an investment is down from the its past peak. They are typically quoted as a percentage drop.

Even though drawdown is not a robust metric to describe the distribution of returns of a given asset, it has a strong psychological appeal. Just like Historical VaR, it provides good insight into downside risk by indicating the magnitude of a historical price drop, from peak to trough.

Here is how you can calculate it using Python:

import pandas as pddef…


Whether you are an aspiring day trader, a financial market professional or just a data scientist dipping your feet into finance, you are likely to have come across the concept of Value At Risk , commonly referred to as VaR.

How bad can it get?

Regardless of what your objectives are when you make an investment, the risk associated with it is always at the top of your concerns. VaR is one of the ways you measure the magnitude of that risk.

VaR measures how bad things can get in a given investment. The 1% VaR for a given investment…

Felipe Cezar

Data Scientist, Economist with a background in Banking www.linkedin.com/in/felipecezar1

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