Graphing SEO metrics from Google Analytics to show meaningful insights for an online educational platform that has been running an extensive content marketing campaign for the last 18 months in order to drive more traffic to their website.Read more
This is the first time in human history our planet's atmosphere has had more than 415 parts per million CO2, according to sensors at the [Mauna Loa Observatory](https://www.esrl.noaa.gov/gmd/obop/mlo/), a research outpost of the [National Oceanic and Atmospheric Agency](https:/Read more
Learn how to quickly get set up and running with Kyso's data science docker image. This tutorial will also demonstrate how you can append your own libraries to the image & run the extended image.Read more
The country with the best restaraunts
The magazine Restaraunt just released its Worlds best restaraunts 2018 list and I wanted to see which country is most represented. Obviously this list is super subjective, but its fun.
Spain comes out on top with 7 restaraunts on the list, followed by the USA (6) and France (5).Read more
1. Meet Professor William Sharpe
An investment may make sense if we expect it to return more money than it costs. But returns are only part of the story because they are risky - there may be a range of possible outcomes. How does one compare different investments that may deliver similar results on average, but exhibit different levels of risks?
Enter William Sharpe. He introduced the reward-to-variability ratio in 1966 that soon came to be called the Sharpe Ratio. It compares the expected returns for two investment opportunities and calculates the additional return per unit of risk an investor could obtain by choosing one over the other. In particular, it looks at the difference in returns for two investments and compares the average difference to the standard deviation (as a measure of risk) of this difference. A higher Sharpe ratio means that the reward will be higher for a given amount of risk. It is common to compare a specific opportunity against a benchmark that represents an entire category of investments.
The Sharpe ratio has been one of the most popular risk/return measures in finance, not least because it's so simple to use. It also helped that Professor Sharpe won a Nobel Memorial Prize in Economics in 1990 for his work on the capital asset pricing model (CAPM).
Let's learn about the Sharpe ratio by calculating it for the stocks of the two tech giants Facebook and Amazon. As a benchmark, we'll use the S&P 500 that measures the performance of the 500 largest stocks in the US.Read more
1. Dr. John Snow
Dr. John Snow (1813-1858) was a famous British physician and is widely recognized as a legendary figure in the history of public health and a leading pioneer in the development of anesthesia. Some even say one of the greatest physicians of all time.
As a leading advocate of both anesthesia and hygienic practices in medicine, he not only experimented with ether and chloroform but also designed a mask and method how to administer it. He personally administered chloroform to Queen Victoria during the births of her eighth and ninth children, in 1853 and 1857, which assured a growing public acceptance of the use of anesthetics during childbirth.
But, as we will show later, not all his life was just a success. John Snow is now also recognized as one of the founders of modern epidemiology (some also consider him as the founder of data visualization, spatial analysis, data science in general, and many other related fields) for his scientific and pretty modern data approach in identifying the source of a cholera outbreak in Soho, London in 1854, but it wasn't always like this. In fact, for a long time, he was simply ignored by the scientific community and currently is very often mythified.
In this notebook, we're not only going to rediscover his "data story", but reanalyze the data that he collected in 1854 and recreate his famous map (also called The Ghost Map).Read more
1. Bitcoin. Cryptocurrencies. So hot right now.
Since the launch of Bitcoin in 2008, hundreds of similar projects based on the blockchain technology have emerged. We call these cryptocurrencies (also coins or cryptos in the Internet slang). Some are extremely valuable nowadays, and others may have the potential to become extremely valuable in the future1. In fact, the 6th of December of 2017 Bitcoin has a market capitalization above $200 billion.
The astonishing increase of Bitcoin market capitalization in 2017.
*1- WARNING: The cryptocurrency market is exceptionally volatile and any money you put in might disappear into thin air. Cryptocurrencies mentioned here might be scams similar to Ponzi Schemes or have many other issues (overvaluation, technical, etc.). Please do not mistake this for investment advice. *
That said, let's get to business. As a first task, we will load the current data from the coinmarketcap API and display it in the output.Read more
1. The Statcast revolution
This is Aaron Judge. Judge is one of the physically largest players in Major League Baseball standing 6 feet 7 inches (2.01 m) tall and weighing 282 pounds (128 kg). He also hit the hardest home run ever recorded. How do we know this? Statcast.
Statcast is a state-of-the-art tracking system that uses high-resolution cameras and radar equipment to measure the precise location and movement of baseballs and baseball players. Introduced in 2015 to all 30 major league ballparks, Statcast data is revolutionizing the game. Teams are engaging in an "arms race" of data analysis, hiring analysts left and right in an attempt to gain an edge over their competition. This video describing the system is incredible.
In this notebook, we're going to wrangle, analyze, and visualize Statcast data to compare Mr. Judge and another (extremely large) teammate of his. Let's start by loading the data into our Notebook. There are two CSV files, judge.csv and stanton.csv, both of which contain Statcast data for 2015-2017. We'll use pandas DataFrames to store this data. Let's also load our data visualization libraries, matplotlib and seaborn.Read more
This study looks at how a fairly simple machine learning algorithm might be used to predict bike-share traffic. Scikit Learn is pretty much the golden standard when it comes to machine learning in PythonRead more