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datacamp-a-network-analysis-of-game-of-thrones

Analyze the network of characters in Game of Thrones and how it changes over the course of the books. Jon Snow, Daenerys Targaryen, or Tyrion Lannister? Who is the most important character in Game of Thrones? Let's see what mathematics can tell us about this! In this project, you will look at the character co-occurrence network and its evolution over the five books in R.R. Martin's hugely popular book series A Song of Ice and Fire (perhaps better known as the TV show Game of Thrones). You will look at how the importance of the characters changes over the books using different centrality measures.

datacamp-a-new-era-of-data-analysis-in-baseball

There's a new era of data analysis in baseball. Using a new technology called Statcast, Major League Baseball is now collecting the precise location and movements of its baseballs and players. In this project, you will use Statcast data to compare the home runs of two of baseball's brightest (and largest) stars, Aaron Judge (6'7") and Giancarlo Stanton (6'6"), both of whom now play for the New York Yankees. Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.

datacamp-a-visual-history-of-nobel-prize-winners

Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?

datacamp-bad-passwords-and-the-nist-guidelines

Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.

datacamp-classify-song-genres-from-audio-data

Rock or rap? Apply machine learning methods in Python to classify songs into genres.

datacamp-dr.-semmelweis-and-the-discovery-of-handwashing

Reanalyse the data behind one of the most important discoveries of modern medicine: Handwashing. In 1847 the Hungarian physician Ignaz Semmelweis makes a breakthough discovery: He discovers handwashing. Contaminated hands was a major cause of childbed fever and by enforcing handwashing at his hospital he saved hundreds of lives.

datacamp-exploring-67-years-of-lego

In this project we will explore a database of every LEGO set ever built. The Rebrickable database includes data on every LEGO set that ever been sold; the names of the sets, what bricks they contain, what color the bricks are, etc. It might be small bricks, but this is big data

datacamp-exploring-the-bitcoin-cryptocurrency-market

Exploting project I developed using the DataCamp platform for the market capitalization of Bitcoin and other cryptocurrencies.

datacamp-find-out-about-the-development-of-the-linux-operating-system-by-exploring-its-git-repositor

Find out about the development of the Linux operating system by exploring its Git repository history. Version control repositories like CVS, Subversion or Git store rich evolution information about a software project. In this project, you'll be challenged to read in, clean up and visualize a real world Git repository dataset of the Linux kernel. With almost 700k commits and thousands of contributors (find out the exact number in this project ;-) ) there are some little data cleaning and wrangling challenges that you'll encounter. But you'll also gain insights about the development activities over the last 13 years.

datacamp-generating-keywords-for-google-adwords

Automatically generate keywords for a search engine marketing campaign using Python.

datacamp-mobile-games-a-b-testing-with-cookie-cats

Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.

datacamp-naive-bees-image-loading-and-processing

Load, transform, and understand images of honey bees and bumble bees in Python. Can a machine distinguish between a honey bee and a bumble bee? Being able to identify bee species from images, while challenging, would allow researchers to more quickly and effectively collect field data. In this Project, you will use the Python image library Pillow to load and manipulate image data. You'll learn common transformations of images and how to build them into a pipeline.

datacamp-naive-bees-predict-species-from-images

Build a model that can automatically detect honey bees and bumble bees in images.

datacamp-name-game-gender-prediction-using-sound

Analyze the gender distribution of children's book writers and use sound to match names to gender. The same name can be spelled out in a many ways (for example, Marc and Mark, or Elizabeth and Elisabeth). Sound can, therefore, be a better way to match names than spelling. In this project, you will use the Python package Fuzzy to find out the genders of authors that have appeared in the New York Times Best Seller list for Children's Picture books. First, using fuzzy (sound) name matching, you will search for author names in a dataset provided by the US Social Security Administration that contains names and genders of all individuals who have applied for Social Security Cards. Next, we'll aggregate the author dataset by including gender. Finally, you will use the new dataset to plot the gender distribution of children's picture books authors over time.

datacamp-recreating-john-snow-s-ghost-map

Recreate John Snow's famous map of the 1854 cholera outbreak in London. In 1854, Dr. John Snow (no, not the Game of Thrones's character) used a pre-computer method of spatial analysis by mapping patterns and occurrences of cholera outbreaks in Soho, London. He mapped the deaths in the neighbourhood and determined that a vast majority occurred around one particular water well and that those that died used that well. It is not only one of the earliest use of data visualization, but by solving this problem, he also founded spatial analysis and modern epidemiology.

datacamp-the-github-history-of-the-scala-language

Find the true Scala experts by exploring its development history in Git and GitHub.

datacamp-the-hottest-topics-in-machine-learning

Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.

datacamp-who-is-drunk-and-when-in-ames-iowa

Flex your pandas muscles on breath alcohol test data from Ames, Iowa, USA.

datacamp-word-frequency-in-moby-dick

Use python to answer the question: What are the most frequent words in Herman Melville's novel Moby Dick?

mejorandola

commit y padding

mitx_capstone_1

This is the first cheat-sheet for the MITx Statistics and Datascience capstone exam

mitx_capstone_2

This is the second cheatsheet for the MITx capstone exams for Statistics and Datascience

mlops-on-gcp

myrepo

network-analysis-made-simple

For PyCon, PyData, ODSC, and beyond!

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

puls3

Desktop version

training-data-analyst

Labs and demos for courses for GCP Training (http://cloud.google.com/training).