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New courses: Introduction to Statistics

January 31st, 2017 in R Programming

This week we are launching four new courses as part of our Introduction to Statistics curriculum. We are taking a modern approach to teaching statistics with the use of simulation and randomization rather than a more traditional theory-driven approach. We have amazing instructors teaching the following courses:

Introduction to Data by Mine Çetinkaya-Rundel, Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University.

  • Statistics is the study of how best to collect, analyze and draw conclusions from data. In this course, you will focus on identifying a question or problem and collecting relevant data on the topic. Get started today.

Exploratory Data Analysis by Andrew Bray, Assistant Professor of Statistics at Reed College.

  • In this course, you’ll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. By the end of this course, you’ll be able to answer which variables suggest interesting relationships and which observations are unusual. All the while generating graphics that are both insightful and beautiful. Get started here.

Correlation and Regression by Ben Baumer, Assistant Professor in the Program in Statistical & Data Sciences Program at Smith College and previously a Statistical Analyst for the NY Mets.

  • Ultimately, data analysis is about understanding relationships among variables. In this course, you will learn how to describe relationships between two numerical quantities and characterize these relationships graphically in the form of summary statistics and through linear regression models. Start here.

Foundation of Inference by Jo Hardin, professor of Mathematics & Statistics at Pomona College.

  • Inference, the process of drawing conclusions about a larger population from a sample of data, is a foundational aspect of statistical analysis. In this course, you will learn the standard practice of disproving a research claim that is not of interest, the degree of disagreement between data and the hypothesis (p-value) and lastly confidence intervals. Start today.

Some of our instructors are part of OpenIntro, an organization that aims to make educational products that are free, transparent, and that lower barriers to entry to education. Here’s what  they had to say about this new method of teaching statistics:

“In these courses, we introduce foundational statistical topics such as exploratory data analysis, statistical inference, and modeling with a focus on both the why and the how. We use real data examples to introduce the ideas of statistical inference within a randomization and simulation framework. We also walk students through the implementation of each method in R using tools from the tidyverse so that students completing the courses are equipped with both a conceptual understanding of the statistical methods presented and also concrete tools for applying them to data.”

- Mine Çetinkaya-Rundel

“The time commitment (~4 hours) for each of the DataCamp courses is just long enough to really sink your teeth into a topic without having to commit to an entire semester.   After taking a course, you will be in a position to move forward either to apply the topic to your own work or to take more courses in order to deepen your knowledge.”

- Jo Hardin

"If you want to build your technical skills for data science, there are many resources online. What makes DataCamp special is the interactive coding environment that offers immediate feedback. This introductory statistics sequence goes even further by coordinating a sequence of courses around a single theme."

-Ben Baumer

The following courses will be the first of our upcoming introduction to statistic track. Expect four more courses in the near future so stay tuned. DataCamp is also officially launching DataCamp for the classroom - teaching staff will now be able to use DataCamp for free with their students. Professors are able to create assignments, manage due dates and have access to all of DataCamp’s premium courses. We believe this introduction to statistic track will make an excellent addition to a classroom.

See you in the course!

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Comments

me-1b133d42-1023-49d9-898a-cf6aa8b73d2a
Must agree as well...I even do not know where to start..There should be some learning path that can take us from point A to point Z of data analysis or data scientist..or to choose the path we want to follow..this is nice all the courses but not useful if you do not know where to progress
02/07/17 8:18 AM |
tryingtolearninvest
Can you guys please do like a course track? Cant tell which one I should pick next after basic R, intermediate R and practice R. This is more apparent in Stats where after introduction to Data ...which one should I pick next??
02/03/17 6:45 PM |
motuchsznajder
I agree. It's hard to choose course by myself. After starting some course I discovery that I should take another one first. A course track is a must-have for DataCamp, in my opinion.
02/08/17 7:48 PM |
fdnieuwveldt
I agree totally. There is no structure, can you guys at datacamp please work on this.
02/05/17 11:57 AM |
ramesh516390
seems these are based on R , do we have probability and statistics courses based on python or language neutral, please.
02/01/17 3:06 PM |
andreausperkins
There are probability and statistic courses on here for Python.
02/08/17 6:11 AM |