Coached Mooc opportunity – Learn Apache Spark this Summer with edX

mooc coachingspark-logoedX_Logo_Col_RGB_FINAL

This would fit perfectly in our coached Moocs sessions.

Data Science 101

edX has just announced a new series of Big Data courses. The series consists of 2 courses focused around Apache Spark. If you are not familiar with Spark, it is a very fast engine for large-scale data processing. It claims to perform up to 100 times faster than hadoop. Here are the 2 courses:

  1. Introduction to Big Data with Apache Spark
  2. Scalable Machine Learning

The first course starts June 1, 2015, and lasts four weeks. The second course starts in late June and lasts five weeks.

The courses are free but verifiable certificates can be purchased for $50 per course.

If you have been hoping to learn Spark, this might be just the opportunity your were waiting for.

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BlogPost – Datacamp – HOW TO BECOME A DATA SCIENTIST IN 8 EASY STEPS: THE INFOGRAPHIC

This post was written by the team behind DataCamp, the online interactive learning platform for data science.  

After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting ring to it. But it is exactly the name that also raises a lot of questions. Because what is a data scientist and what do data scientists do exactly? Many of us who devote their lives to data science have frequently been confronted with questions like these.

The answers to these questions are mostly not as straightforward as you would expect: a short search on Google with the string of words “How to become a data scientist” shows that the concept has different meanings to different people. In addition, many articles indeed suggest various tools, courses and applications for people to become a data scientist, and with good reason: the options are unlimited. But let’s face it, for someone that is not familiar with the field, this advice may sometimes seem like a jungle of information. What’s more, they could work demotivating: the descriptions are sometimes fearfully long and the many details often hit the readers as an overwhelming avalanche.

DataCamp’s Guide to Becoming a Data Scientist

With all this in mind, DataCamp decided to help those who can’t see the forest for the trees: we designed a step-by-step infographic that clearly outlines how you can become a data scientist in 8 easy steps.  This visual guide is meant for everyone that is interested in learning data science or for everyone that has already become a data scientist but wants some additional resources for further perfection.  The infographic is called “Become a data scientist in 8 easy steps”. Have a look at it!

Become a data scientist in 8 easy steps
Source: blog.datacamp.com

If you are thinking about becoming a data scientist, do not be taken aback by the eight steps that are presented in the infographic. We would like to emphasize that becoming a data scientist takes time and personal investment, but that the journey is everything but dull! And don’t forget, there are plenty of courses available to set you on the right way.

If you are already a data scientist, drop us a line atinfo@datacamp.com if you think of other steps that you have undertaken in your professional journey.

 

 

Sneak preview – Mooc – Bart Baesens – Credit Risk Analytics

Baesens_Bart_small     Big Data World
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
You are invited to send an email to Bart.Baesens@gmail.com if interested in more information.

Coursera – Social Media Analysis – Michigan Univerity

University of Michigan

https://www.coursera.org/course/sna

The Social Network Analysis MOOC started this week on Coursera.
The course is given by Lada Adamic, an assiciate professor at MU who took a sabbatical year to go and work at Facebook. A year later she’s back with this inspiring course.
Lada Adamic will introduce you to social network mechanics and concepts. The tool of choice in this case is Gephi, which is a free to use graph/network visualisation tool.
This 8 week course combines video lectures with homework assignments during which you will learn to use Gephi and apply the freshly acquired knowledge on real data sets.
The course offers the possibility to apply for a certificate.

As a personal note from Glenn Vanderlinden:

I already went through the first couple of units and it looks rather interesting. It makes use of Gephi, which is to an extent an alternative to Neo4j. Might be interested for people who attended the last Meetup or who are interested in graph/network analysis. I hope this is useful for the community.

Instructors

Lada Adamic

Lada Adamic

Coursera – Process Mining -TU Eindhoven – starts Nov 12th

TUeLOG_P_CMYK-2

 

https://www.coursera.org/course/procmin

 

Process Mining: Data science in Action

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.

Course at a Glance

4-6 hours of work / week
English
English subtitles