Python

Introduction to Natural Language Processing in Python

Interactive Maps with leaflet in R

Exploratory Data Analysis in Python

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.


Fundamentals of Bayesian Data Analysis in R

In 1847, the Hungarian physician Ignaz Semmelweis made 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. 


1. Meet Dr. Ignaz Semmelweis

2. The alarming number of deaths

3. Death at the clinics

4. The handwashing begins

5. The effect of handwashing

6. The effect of handwashing highlighted

7. More handwashing, fewer deaths?

8. A Bootstrap analysis of Semmelweis handwashing data

9. The fate of Dr. Semmelweis

Dr. Semmelweis and the Discovery of Handwashing

Once you’ve got some data, you’ll often want to reshape it, move it around, and merge it with other data. Learn the basics of data manipulation using today’s most powerful and popular tools.

Data Manipulation

Creating visual representations of your data is one of the quickest and most effective ways to understand it, discover surprising results, and communicate those results to others. Learn the basics of data visualization using state-of-the-art tools.

Data Visualization

Much of data science comes from the world of probability and statistics, which together provide a set of tools for understanding how data is generated, how to model it effectively, and how to make inferences and predictions based on those models.

Probability & Statistics

The first step of any analysis involves loading your data into the proper environment—Python, R, SQL, or elsewhere—then reshaping it and/or addressing issues such as missing or extreme values. Master the fundamentals by learning the basics of importing and cleaning data.

Importing & Cleaning Data

If you've never done a DataCamp project, this is the place to start! 


This is an introduction to DataCamp projects. DataCamp projects allow you to apply 
the skills you have learned in DataCamp courses. In each project, you will carry out 
an end-to-end analysis on real-world tasks using real-world tools and workflows.

In doing so, you will learn how to work with Jupyter notebooks: an open-source web 
application that is great for interactive data analysis.


1. This is a Jupyter notebook!

2. Put any code in code cells

3. Jupyter notebooks ♡ data

4. Jupyter notebooks ♡ plots

5. Jupyter notebooks ♡ a lot more

6. Goodbye for now!

Introduction to DataCamp Projects

Data science is a vast and rapidly evolving field. For this reason, it’s important for you to put your skills to the test by solving real-world problems once you’ve mastered the basics.

Case Studies

If you have never done a DataCamp project, this is the place to start!  


5. Jupyter notebooks ♡ Data Science

8. A statistical analysis of Semmelweis handwashing data

Use R to make art and create imaginary flowers inspired by nature.


R is a tool for doing serious statistics and data analysis. 
But not everything in life can be serious, life is also beautiful, 
and R can make beautiful things too. R can make art. 

The arrangement of leaves on a plant stem is ruled by spirals. 
This fact is called [phyllotaxis](https://en.wikipedia.org/wiki/Phyllotaxis)
and it is a nice example of how mathematics can describe patterns in nature.
In this project, we will *invent* flowers using this fact. 

This R project assumes you have familiarity with the `ggplot2` package. If you want to see more examples of how you can use R to make art, you should check out 
[the Fronkonstin blog](https://fronkonstin.com/) created by Antonio Sánchez Chinchón.


1. Patterns in nature

2. Warming up: drawing points on a circle

3. Make it harmonious with the Golden Angle

4. Remove everything unnecessary

5. A bit of makeup: size, color and transparency

6. Play with aesthetics: the dandelion

7. Put all it together: the sunflower

8. What if you modify the angle?

9. All together now: imaginary flowers

Phyllotaxis: Draw Flowers Using Mathematics

Analyze the relative popularity of programming languages over time based on Stack Overflow data.


Introduction to the Tidyverse

Case Study: Exploratory Data Analysis in R

Foundations of Probability in R

How can you tell what programming languages and technologies are used by the most people? How about what languages are growing and which are shrinking, so that you can tell which are most worth investing time in?
One excellent source of data is [Stack Overflow](https://stackoverflow.com/), a programming question and answer site with more than 16 million questions on programming topics. By measuring the number of questions about each technology, you can get an approximate sense of how many people are using it. In this project, you'll use open data from the [Stack Exchange Data Explorer](https://data.stackexchange.com/) to examine the relative popularity of languages like R, Python, Java and Javascript have changed over time.


1. Data on tags over time

2. Now in fraction format

3. Has R been growing or shrinking?

4. Visualizing change over time

5. How about dplyr and ggplot2?

6. What are the most asked-about tags?

7. How have large programming languages changed over time?

8. Some more tags!

Rise and Fall of Programming Languages

If you've never done a DataCamp project, this is the place to start!


Scala

Introduction to Scala

1. This is a Jupyter Notebook!

3. Jupyter Notebooks ♡ SQL (part i)

4. Jupyter Notebooks ♡ SQL (part ii)

5. Jupyter Notebooks ♡ SQL (part iii)

6. Jupyter Notebooks ♡ SQLAlchemy

7. Jupyter Notebooks ♡ plots

8. Goodbye for now!

Visualize the rise of COVID-19 cases globally with ggplot2. 


Data Manipulation with pandas

Theory

Data Visualization for Everyone

Introduction to Regression in R

Introduction to Writing Functions in R

Spreadsheets

Intermediate Spreadsheets

Intermediate Regression in R

Hypothesis Testing in R

Sampling in R

Object-Oriented Programming with S3 and R6 in R

Introduction to Spark with sparklyr in R

Within months, COVID-19 went from an epidemic to a pandemic. From the first identified case in December 2019, how did the virus spread so fast and widely? In this free R project, we will visualize data from the early months of the coronavirus outbreak to see how this virus grew to be a global pandemic.

This project assumes you can manipulate data frames using `dplyr` and make plots using `ggplot2`. 


1. From epidemic to pandemic

2. Confirmed cases throughout the world

3. China compared to the rest of the world

4. Let's annotate!

5. Adding a trend line to China

6. And the rest of the world?

7. Adding a logarithmic scale

8. Which countries outside of China have been hit hardest?

9. Plotting hardest hit countries as of Mid-March 2020

Visualizing COVID-19

Use a variety of data manipulation techniques to explore different aspects of Lego's history!

Data Science for Business

Building Web Applications with Shiny in R

The [Rebrickable](https://rebrickable.com/downloads/) database includes data on every 
LEGO set that has 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!
In this project, you will get to explore the Rebrickable database and answer a series of 
questions related to the history of Lego!


1. Introduction

2. Reading Data

3. Exploring Colors

4. Transparent Colors in Lego Sets

5. Explore Lego Sets

6. Lego Themes Over Years

7. Wrapping It All Up!

Exploring the History of Lego

Use web scraping and NLP to find the most frequent words in one of two pieces of classic literature: Herman Melville's novel, Moby Dick, or Peter Pan by J. M. Barrie.


Introduction to Python

Intermediate Python

Supervised Learning with scikit-learn

Python Data Science Toolbox (Part 1)

Python Data Science Toolbox (Part 2)

Introduction to Importing Data in Python

Intermediate Importing Data in Python

In this Project, you'll scrape a novel from the website Project Gutenberg (which contains a large corpus of books) using the Python package `requests`. Then you'll extract words from this web data using `BeautifulSoup`. Finally, we'll dive into analyzing the distribution of words using the Natural Language ToolKit (`nltk`).

The natural language processing tools used here apply to much of the data that data scientists encounter as a vast proportion of the world's data is unstructured data and includes a great deal of text. To complete this Project, you need to know how to import web data into Python and how to work with natural language text.

1. Tools for text processing

2. Request Moby Dick

3. Get the text from the HTML

4. Extract the words

5. Make the words lowercase

6. Load in stop words

7. Remove stop words in Moby Dick

8. We have the answer

9. The most common word

Word Frequency in Classic Novels

Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.


Manipulating Time Series Data in Python

Importing and Managing Financial Data in Python

When you assess whether to invest in an asset, you want to look not only 
at how much money you could make but also at how much risk you are taking. 
The Sharpe Ratio, developed by Nobel Prize winner William Sharpe some 
50 years ago, does precisely this: it compares the return of an investment 
to that of an alternative and relates the relative return to the risk of
the investment, measured by the standard deviation of returns.

In this project, you will apply the Sharpe ratio to real financial data using pandas.


1. Meet Professor William Sharpe

2. A first glance at the data

3. Plot & summarize daily prices for Amazon and Facebook

4. Visualize & summarize daily values for the S&P 500

5. The inputs for the Sharpe Ratio: Starting with Daily Stock Returns

6. Daily S&P 500 returns

7. Calculating Excess Returns for Amazon and Facebook vs. S&P 500

8. The Sharpe Ratio, Step 1: The Average Difference in Daily Returns Stocks vs S&P 500

9. The Sharpe Ratio, Step 2: Standard Deviation of the Return Difference

10. Putting it all together

11. Conclusion

Risk and Returns: The Sharpe Ratio

The finance community has been one of the eager to embrace data science. From stock market predictions, to asset pricing and calculating the probability of loan defaults, the applications are nearly endless!

Applied Finance

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


Almost every web service you join will require you to come up with a password. 
But what makes a good password? 
In June 2017 the National Institute of Standards and Technology (NIST)
published [publication 800-63B](https://pages.nist.gov/800-63-3/sp800-63b.html) 
titled *Digital Identity Guidelines: Authentication and Lifecycle Management*. 
This publication doesn't tell you what is a *good* password,
but it does have specific rules for what is a *bad* password.

In this project, you will take a list of user passwords and, 
using publication 800-63B, you will write code that automatically 
detects and flags the bad passwords.


1. The NIST Special Publication 800-63B

2. Passwords should not be too short

3.  Common passwords people use

4.  Passwords should not be common passwords

5. Passwords should not be common words

6. Passwords should not be your name

7. Passwords should not be repetitive

8. All together now!

9. Otherwise, the password should be up to the user

Bad Passwords and the NIST Guidelines

Discover the top tools Kaggle participants use for data science and machine learning. 


When beginning a career in data science, one often wonders what programming 
tools and languages are being used in the industry, and what skills one should learn first. 
By exploring the 2017 Kaggle Data Science Survey results, you can learn about 
the tools used by 10,000+ people in the professional data science community.

Before starting this project, you should be comfortable manipulating data frames 
and have some experience working with the `tidyverse` packages `dplyr`, `tidyr`, and `ggplot2`.

This project uses a subset of the
[2017 Kaggle Machine Learning and Data Science Survey](https://www.kaggle.com/kaggle/kaggle-survey-2017?utm_medium=partner&utm_source=datacamp.com&utm_campaign=ml+survey+case+study) dataset.
If you want to know more about the tools and techniques Kaggle participants use, check out the full [report of the Kaggle 2017 survey results](https://www.kaggle.com/amberthomas/kaggle-2017-survey-results?utm_medium=partner&utm_source=datacamp.com&utm_campaign=ml+survey+case+study).


1. Welcome to the world of data science

2. Using multiple tools

3. Counting users of each tool

4. Plotting the most popular tools

5. The R vs Python debate

6. Plotting R vs Python users

7. Language recommendations

8. The most recommended language by the language used

9. The moral of the story

Exploring the Kaggle Data Science Survey

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. 

This project uses a dataset parsed by Andrew J. Beveridge and Jie Shan 
which is available [here](https://github.com/mathbeveridge/asoiaf). 
For more information on this dataset have a look at
[the Network of Thrones blog](https://networkofthrones.wordpress.com).


1. Winter is Coming. Let's load the dataset ASAP!

2. Time for some Network of Thrones

3. Populate the network with the DataFrame

4. The most important character in Game of Thrones

5. The evolution of character importance

6. What's up with Stannis Baratheon?

7. What does Google PageRank tell us about GoT?

8. Correlation between different measures

9. Conclusion

A Network Analysis of Game of Thrones

Wrangle and visualize musical data to find common chords and compare the styles of different artists.


Apply data-wrangling and visualization tools from the tidyverse to musical
data. Find the most common chords and chord progressions in a sample of 
pop/rock music from the 1950s-1990s, and compare the styles of different 
artists. 

This project assumes familiarity with standard TidyVerse tools
for R, in particular the `tibble` data structure and the `dplyr` and 
`ggplot2` packages. No specific musical knowledge is required, though it 
may give you ideas for further exploration of the dataset after completing
the project.


This project uses a parsed and cleaned version of the 
[McGill Billboard Dataset](http://ddmal.music.mcgill.ca/research/billboard),
version 2.0 (CC0 license).


2. The most common chords

3. Visualizing the most common chords

4. Chord "bigrams"

5. Visualizing the most common chord progressions

6. Finding the most common artists

7. Tagging the corpus

8. Comparing chords in piano-driven and guitar-driven songs

9. Comparing chord bigrams in piano-driven and guitar-driven songs

10. Conclusion

Wrangling and Visualizing Musical Data

You will explore the market capitalization of Bitcoin and other cryptocurrencies.


To better understand the growth and impact of
Bitcoin and other [cryptocurrencies](https://en.wikipedia.org/wiki/Cryptocurrency) 
you will, in this project, explore the *market capitalization* of different cryptocurrencies.

**Warning: The cryptocurrency market is exceptionally volatile,
and any money you put in might disappear 
into thin air. Never invest money you can't afford to lose.**


1. Bitcoin and Cryptocurrencies: Full dataset, filtering, and reproducibility

2. Discard the cryptocurrencies without a market capitalization

3. How big is Bitcoin compared with the rest of the cryptocurrencies?

Exploring the Bitcoin Cryptocurrency Market

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