20 Friday Mar 2015
Posted in Data Science, Programming
Tags
A good article has appeared on R Bloggers on how to analyse LinkedIn using R.
It shows how to analyse using a package called RlinkedIn to create a Wordcloud/TagCloud.
Rlinkedin can also be used to query a number of Linkedin API’s
13 Friday Mar 2015
Posted in Data Science, Programming
Tags
There is a good collection of R Cheat Sheets at RStudio that cover:
10 Tuesday Mar 2015
Posted in Data Science
Tags
Watching different Data Science & Statistics training videos one statement that comes up often is “Correlation does not imply causation”.
Wikipedia defines this as:
Correlation does not imply causation is a phrase used in science and statistics to emphasize that a correlation between two variables does not necessarily imply that one causes the other.
The xkcd comic site has a great strip on the subject
To illustrate this further, there are a number of Graphs that have been put together that visualise why Correlation does not imply causation.
If however Mozzarella Cheese is ever found as a link to Engineering Doctorates it will be time to by shares in the cheese manufacturing companies as sales would soar!
09 Monday Mar 2015
Posted in Cloud, Data Science
Tags
Microsoft have released a free e-book to download about using Data Science, R and Azure ML (Machine Learning).
Data Science in the Cloud with Microsoft Azure Machine Learning and R
The topics in the book cover:
Added to my every increasing pile of things to read.
06 Friday Mar 2015
Posted in Data Science
Tags
Scanning my daily feeds from feedly I came across this post about a new Data Analysis and Statistical Inference course on Coursera that has just started this week. looks to be a good grounding on the subject.
The course is split into 7 modules
The timetable of work over 10 weeks
Week 1: Introduction to Data, March 2 – 9
Review the START HERE! pages
Review Learning Objectives for Unit 1
Watch the videos for Unit 1 Introduction to Data
Start Quiz 1 — due at 13:00 EST (-5:00), Monday, March 16
Begin Lab 0 — due at 13:00 EST (-5:00), Monday, March 16, this lab is not graded (for practice)
Begin Lab 1 — due at 13:00 EST (-5:00), Monday, March 16
Explore the Discussion Forums and contribute
Week 2: Probability and Distributions, March 9 – 16
Review Learning Objectives for Unit 2
View videos for Unit 2 Probability and Distributions
Start Quiz 2 — due at 13:00 EST (-5:00), Monday, March 23
Begin Lab 2 — due at 13:00 EST (-5:00), Monday, March 23
Explore the Discussion Forums and contribute
Begin your Project Proposal — due at 13:00 EST (-5:00), Monday, March 23
Complete Quiz 1 — due at 13:00 EST (-5:00), Monday, March 16
Complete Lab 0 and Lab 1 — due at 13:00 EST (-5:00), March 16
Week 3: Foundations for Inference, March 16 – 23
Review Learning Objectives for Unit 3
View videos for Unit 3 Foundations for Inference
Start Quiz 3 — due at 13:00 EST (-5:00), Monday, March 30
Begin Labs 3A and 3B — due at 13:00 EST (-5:00), Monday, March 30
Explore the Discussion Forums and contribute
Submit your Project Proposal before 13:00 EST (-5:00), Monday, March 23
Complete Quiz 2 — due at 13:00 EST (-5:00), Monday, March 23
Complete Lab 2 — due at 13:00 EST (-5:00), Monday, March 23
Week 4: Foundations for Inference and Midterm, March 23 – 30
No new materials
Review Learning Objectives for Unit 3
Complete videos for Unit 3 Foundations for Inference
Complete Quiz 3 — due at 13:00 EST (-5:00), Monday, March 30
Complete Lab 3A and 3B — due at 13:00 EST (-5:00), Monday, March 30
Begin assessing Project Proposals — due 13:00 EST (-5:00), Monday, April 6
Begin Midterm — due 13:00 EST (-5:00), Monday, April 6
Week 5: Statistical Inference for Numerical Variables, March 30 – April 6
Review Learning Objectives for Unit 4
View videos for Unit 4 Statistical Inference for Numerical Variables
Start Quiz 4 — due at 13:00 EST (-5:00), Monday, April 13
Begin Lab 4 — due at 13:00 EST (-5:00), Monday, April 13
Explore the Discussion Forums and contribute
Complete Project Proposal assessments — due 13:00 EST (-5:00), Monday, April 6
Complete Midterm — due 13:00 EST (-5:00), Monday, April 6
Please submit at least 3 hours before the deadline
Week 6: Statistical Inference for Categorical Variables, April 6 – 13
Review Learning Objectives for Unit 5
View videos for Unit 5 Statistical Inference for Categorical Variables
Start Quiz 5 — due at 13:00 EST (-5:00), Monday, April 20
Begin Lab 5 — due at 13:00 EST (-5:00), Monday, April 20
Begin Data Analysis Project — due at 13:00 EST (-5:00), Monday, April 20
Explore the Discussion Forums and contribute
Complete Quiz 4 — due at 13:00 EST (-5:00), Monday, April 13
Complete Lab 4 — due at 13:00 EST (-5:00), Monday, April 13
Week 7: Simple Linear Regression, April 13 – 20
Review Learning Objectives for Unit 6
View videos for Unit 6 Simple Linear Regression
Start Quiz 6 — due at 13:00 EST (-5:00), Monday, April 27
Begin Lab 6 — due at 13:00 EST (-5:00), Monday, April 27
Explore the Discussion Forums and contribute
Complete Quiz 5 — due at 13:00 EST (-5:00), Monday, April 20
Complete Lab 5 — due at 13:00 EST (-5:00), Monday, April 20
Complete Data Analysis Project — due at 13:00 EST (-5:00), Monday, April 20
Week 8: Multiple Linear Regression, April 20 – 27
Review Learning Objectives for Unit 7
View videos for Unit 7 Multiple Linear Regression
Start Quiz 7 — due at 13:00 EST (-5:00), Monday, May 4
Begin Lab 7 — due at 13:00 EST (-5:00), Monday, May 4
Explore the Discussion Forums and contribute
Begin review of Data Analysis Project due at 13:00 EST (-5:00), Monday, May 4
Complete Quiz 6 — due at 13:00 EST (-5:00), Monday, April 27
Complete Lab 6 — due at 13:00 EST (-5:00), Monday, April 27
Week 9: Review and catch up, April 27 – May 4
Note that all assignment due times are now in Eastern Standard Time (EST)
View review videos
Complete Quiz 7 — due at 13:00 EST (-5:00), Monday, May 4
Complete Lab 7 — due at 13:00 EST (-5:00), Monday, May 4
Explore the Discussion Forums and contribute
Complete review of Data Analysis Project — due at 13:00 EST (-5:00), Monday, May 4
Final exam is available this week — due 13:00 EST (-5:00), Monday, May 11
Week 10: Final Exam, May 4 – 11
(Source: https://www.coursera.org/course/statistics)
23 Monday Feb 2015
Posted in Architecture, Data Science
Tags
An interesting piece of research on Data Scientist Job Titles has been carried out from data on LinkedIn of over 10,000 professionals.
The data splits out into 11 categories listing 700+ Job Titles:
The raw data is available to play with. I have been looking at the Architecture job titles that fall into the “Data Plumbing” category to initially have a view of Architecture Roles within Data Science and what that means. I will continue this research and blog later about it.
As a bit of fun if you want a generated Job Title, one app to try is this one – Generate a Job Title for you comes up with “Your Silicon Valley job title is……..”
My favourite Generation is the “Software Warlock”
A close second is “Grand Poobah of Digital Innovation”
16 Monday Feb 2015
Posted in Data Science, Open Source, Social Media
Tags
Have you been caught out by the ever increasing world of Clickbait, enticed in with titles like “You’ll Never Believe What The Parrot Did Next!” in your Social Media feeds such as facebook.
The main purpose of Clickbait is to get you to a site where adverts are displayed to get you to onward click and generate revenue for the sites owners.
Max Woolf @ Minimaxir.com has recently mapped out 15,656 BuzzFeed Listicles which have been shared on Facebook.
This has been achieved using R and ggplot based on a dataset from Buzzfeed. A copy of the code is also available on the authors Github repository.
Looking at the dataset itself:
The top 3 articles shared
The bottom 3 articles shared:
The total number of shares in the data set is a staggering 185,415,297
09 Monday Feb 2015
Posted in Data Science, Programming
Tags
Fancy testing your R skills? There is an Base R Assessment written by Francis Smart now available. Presents a good challenge on testing your skills based on 5 different skill levels.
The tests are slightly slow at the moment but worth a go to challenge yourself.
Source: http://www.econometricsbysimulation.com/2015/02/base-r-assessment.html
03 Tuesday Feb 2015
Posted in Data Science, Machine Learning
Here are some useful links and resources to learning Statistical Inference
*check the sites for any appropriate rules around downloading of course.
28 Wednesday Jan 2015
Posted in Data Science
Tags
If you have not yet had a chance to try “R” as a language, here is a good site for having a go at some of the functions and power of the R programming language.
There are 8 lessons in this group
The lessons present you with an R interface so you don’t have to have the R software installed on your end device.
There are also other good lessons available from http://www.codeschool.com such as Try GIT and GIT Real.
Max Hemingway 