Big Data in Learning

There are many fields in which big data can improve results. One of these being (e-)learning. Until recently the focus on analysing learning lay on analysing results of exams but with big data and analytics there are new possibilities to enhance the experience of learning as a whole. For example there is the possibility to personalize learning and helping students to achieve better results. Big Data makes this possible in nearly real time. There is the possibility to help students in the process of learning, as soon as the programm realizes a problem and providing a solution in the workflow, instead of the student having to stop his learning process for his problem to be solved and then continue. This also applies for working environments.
Not only inside a process of learning or work analysing data can come in handy. Even after a course is finished analysing the data produced during the course by all students can help optimize the course and resulting exam. Identifying where users got stuck or what was to easy will improve the learning experience for everyone.
There are already efforts to integrate this into the learning experience like Predictive Analytics Reporting (PAR) Framework.
PAR is trying to integrate several data sources and base their studies on this data instead of the studies that are based on individual programms. This approach broadens the base and this may make it able to find other (better) insights into the educational system of the U.S.

Data Scientist: Hype or Sexy?

Data Scientists seem to be everywhere nowadays. This title has seen a huge increase in appearences in job descriptions, as Indeed.com demostrates in its data.
There are several sites and articles that even describe the job as sexy:

The combination of handling Big Data and Analytics is what makes this title so attractive. So far handling data and analytics were too parts, sometimes combined in one person, but most times not. But with all the unstructured data available and new tools to handle it, the combination is easier to handle for one person. But technical understanding is not enough to become a Data Scientist. It requires an understanding of products and customer behaviour as well as how to manage and analyse data.
Right now there is no programm that graduates with the title Data Scientist, so companies have to look for people that learned all skills during their career so far or have strong affinities towards analysis or programming with corresponding skills learned during their studies or education.
Because of the interdisciplinary area of this field, some companies try to get their hands on graduated physicist. Their studies involve a great deal of interdisciplinary themes and the affinity to both algorithms and research.
Other possibilities are Business Intelligence Analysts, Data Mining specialists or even Web Analytics manager, depending on their career so far, especially their experience with different kinds of data and their presentation skills regarding actions resulting from their analysis.
All in all this new title and the news coverage it is getting is a great opportunity for people already working in this field and newbies wanting to work with both data and analysis.

This makes this new profession sexy.