Data Analysis with R

This was the first course I took since I started thinking about analytics and R. A fellow Data Scientist recommended it to me. I was bit surprised when I saw the level as Intermediate still decided to pursue. Duration of the course is 2 months and that's what it took me to complete it with 2-3 hours a day.

About the course -
If you are new to R, it will not teach you the ABC of it, but believe me I never felt the need of it though the only programming language I knew was COBOL. It is a primarily an analytics course and gets one well versed with the Data Analytics concepts and uses R as a tool to do that. The name it apt - Exploratory Data Analysis "using R"! The questions are interesting, having "no right or wrong answer" (as they keep saying :), and will make you think.

1. The course directly takes you to the next level of R without knowing much about it and you learn it well. After completing his course, I took the Coursera's R programming to learn the basics of the language. The basic difference was Coursera course is to be an R programmer while Udacity's course revolves around Data analytics, playing with data, visualization, a bit of statistics.

2. Plots and visualizations was never so much of fun. Few days after this course, I read somewhere that teaching R should be started with ggplot and I very much agree.

3. The course is self-paced, has tiny modules and heap of quizzes in between which really helps understand the concepts.

I've become a fan of Udacity and have done multiple course around statistics after this.

The course provides an overview of using R to explore data and focuses heavily on the use of the ggplot2 package in R to create data visualizations. Although the course touches briefly on high-level theory and concepts like summary statistics, transforming data, correlation and linear regression, almost all of the quizzes and homework questions have to do with creating plots and making observations based on plots. This is not necessarily a bad thing--learning to plot in R is a valuable skill and an important part of exploratory data analysis--but it seems like the course should have spent a bit more time covering high-level concepts and numeric methods for exploring data like using tables and summaries. Despite that quibble, this is good course with a lot of high quality and practical content. It moves slowly enough for you to get comfortable with basic potting syntax before building up to more complex visualizations, but fast enough to keep you engaged.

I was skeptical when I enrolled in UDACITY's Data Analysis Nano Degree Program but not only have they provided the experience they said they would they have steadily made improvements since I enrolled. How many times in your life have you had that experience? Here are SOME of the improvements they have made while I have been enrolled. Initially one could get one-on-one help but usually it was 1 to 2 days out but at least was a video chat.
This was great. I had tried a competitor's course and sometime s one just cannot figure out why something is not working. But not with Udacity. Then they scrapped that and instituted a MENTOR program. Here one could instant message someone who would get back to you in a few hours. Then they scrapped that and now offer LIVE HELP. It is a chat box that one types the gist of your question into. In less than 10 min, often in 3 min , someone comes on. Usually they can immediately figure out your mistake ( it seems students make a finite # of errors) but if they cant they ask you to copy and paste your code. And if they still cannot figure it out, i.e., if you have really made a mess of things they do a screen sharing session to get you back on the rails . Don't make a mistake. Just sign up for Udacity.