new courses!

We're proud to launch two new courses this week! Learn the incredibly important skill of cleaning data in Python, and discover the exciting world of Bayesian statistics! [DataCamp]( New Courses! [Cleaning Data in Python]( Cleaning Data in Python Taught by Daniel Chen, Data Science Consultant at Lander Analytics It is said that data scientists spend 80% of their time cleaning data and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from diagnosing problems to dealing with missing values and outliers. [Start Learning ›]( Beginning Bayes in R Taught by Jim Albert, Professor at Bowling Green State University The core of the Bayesian perspective is the idea of representing your beliefs about something using the language of probability, collecting data, then updating your beliefs based on the evidence. Discover this increasingly popular statistical school of thought! [Start Learning ›]( Cleaning Data in Python: What You'll Learn Chapter One: Exploring your Data Learn how to explore your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows. Chapter Two: Tidying Data for Analysis Learn the principles of tidy data and, more importantly, why you should care about them. Chapter Three: Combining Data for Analysis The ability to transform and combine your data is a crucial skill in data science because your data may not always come in one file or table for you to load. Chapter Four: Cleaning Data for Analysis Learn about string manipulation and pattern matching to deal with unstructured data, then explore techniques to deal with missing or duplicate data. Chapter Five: Case Study Apply all of the data cleaning techniques you've learned towards tidying a real-world, messy dataset obtained from the Gapminder Foundation. [Play now ›]( Beginning Bayes in R: What You'll Learn Chapter One: Introduction to Bayesian Thinking This chapter introduces the idea of discrete probability models and Bayesian learning. Chapter Two: Learning About a Binomial Probability This chapter describes learning about a population proportion using discrete and continuous models. Chapter Three: Learning About a Normal Mean This chapter introduces Bayesian learning about a population mean. Chapter Four: Bayesian Comparisons In the final chapter, you'll use a Bayesian regression approach to learn about a mean or the difference in means when the sampling standard deviation is unknown. [Play now ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02141 [Unsubscribe](