Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data.
Study online but work in group
Get help from a local expert
Why we coach MOOCs
The European Data Innovation Hub is partnering with top experts to offer MOOC participants the possibility to do these online courses in group. During the duration of the Mooc participants will be welcome to come to the Hub in Brussels to work and to go through exercises with other participants. On specific days one or more domain expert will be present to coach the students.
Organizations use their data for decision support and to build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term Data Science. This course will attempt to articulate the expected output of Data Scientists and then teach students how to use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments include Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (part of Apache Spark), but previous experience with Spark or distributed computing is NOT required. Students should take this Python mini-quiz before the course and take this Python mini-course if they need to learn Python or refresh their Python knowledge.
What you’ll learn
Learn how to use Apache Spark to perform data analysis
How to use parallel programming to explore data sets
Apply Log Mining, Textual Entity Recognition and Collaborative Filtering to real world data questions
We’re excited to announce our coaching in the most popular Massive Open Online Course: Machine Learning by Andrew Ng! We’ve got some beautiful new office space thanks to our buddies at AXA Belgium where we’ll be holding meetups to discuss and work through course materials. We’ll start Monday 4th of May around 7 pm, so keep a look out at our various channels of communication! Here’s the Meetup.com event with address details, a Calendar file and so on.
Andrew Ng’s ‘Machine Learning’ is one of the first courses on Coursera which has grown to amazing popularity, and rightfully so! This course covers ‘how to make computers act without explicitly programming them’, as Andrew puts it, by explaining concepts like multivariate regression, neural networks, support vector machines and much more. This information is invaluable for many branches of data science and gives a good look at what’s ahead for those willing to get their hands dirty. You don’t have to be an expert programmer for it either. Everything Andrew does is in Octave, but to make our learning experience even more exciting we’ll be repeating the Octave exercises in R, a very common language among all data- or statistics workers. R is a great language to learn if you’re looking to go forward in (online) courses concerning data science.
With our group, you’ll be guided in understanding the concepts and assignments given to you in this course, giving you valuable experience in what Machine Learning is and what can be accomplished with it. We’ll also give a little more background on some of the stuff Ng talks about so that each and all can keep their head above water.
And, of course, it’s free! We want to stimulate a learning environment and attract enthusiasts on all levels, so feel free to join in. After our first meetup we’ll hook a camera up with a Google Hangouts group so that you can follow online.
First ‘in-real-life’ meeting will start Monday May 4th and from then on we’ll get together every Thursday (except on main meetup days, about once per month). Edward and I will coach, though enthusiasts are always welcome to help out or hang around.
I had a nice lunch with Prof. Dr Bart Baesens today at the MIM to discuss his recent book‘Analytics in a Big Data World: The Essential Guide to Data Science and its Applications’
One topic we discussed was knowledge transfer and certification.
Next to the recorded presentations already available on dataminingapps.com, the professor told me that his new course about Credit Risk Analytics would soon be released. Here is for you, in avant première, the content of this course that he has put together with SAS. This course will be available mid November 2014.
New e-learning course Credit Risk Analytics by professor Bart Baesens
The outline of the course is as follows:
Lesson 1: Introduction to Credit Scoring
Lesson 2: The Basel Capital Accords
Lesson 3: Preparing the data for credit scoring
Lesson 4: Classification for credit scoring
Lesson 5: Measuring the Performance of Credit Scoring Classification Models
Lesson 6: Variable Selection for Classification
Lesson 7: Issues in Scorecard Construction
Lesson 8: Defining Default Ratings and Calibrating PD
Lesson 9: LGD modeling
Lesson 10: EAD modeling
Lesson 11: Validation of Credit Risk Models
Lesson 12: Low Default Portfolios
Lesson 13: Stress testing
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.