Imagine the amount of data that is recorded every time you make an online transaction (amounts, dates, users, accounts, balances, interests, commissions, immediate exchange rates, etc.), multiply it by the number of times you carry them out. during the month and, once again, multiply the result by the hundreds of thousands of clients that your bank may have.
Now, think of the records of the physical state of a person that with mobile devices and smart garments are obtained per second, and multiplies successively, including the number of models of devices on the market, until reaching the number of measurements that could be registered with the inhabitants of a city in just one week or in fifteen days.
Finally, consider all the information collected by Facebook and Twitter in a small region and in a time interval of just one day: dates, texts (number of words, hashtags, links, emoticons, etc.), multimedia content, technical data of the mobiles in which your apps work, interactions, geographical coordinates, revised publications between users, etc.
Big data and Data Science
The data volumes are exorbitant, are big data, and continue to increase every moment, particularly because real-life examples take into account also long time slots and with records kept continuously, as well as its treatment and analysis is necessary.
And, until the above does not happen, organizations remain in a situation of uncertainty. Rather, there are not a few public entities and private companies that obtain such large amounts of information but cannot obtain sufficient value from them due to the lack of infrastructure and trained personnel in recently developed areas of knowledge to exploit them.
Clearly, this leads to a wide demand for professionals -will be paid- that can provide such entities with enough guidance to adapt to the constant and gigantic flow of data, but for this, the professional must have consolidated an arsenal of mathematical tools, statistical analysis with a wide component in the visualization of the information- and cutting-edge computing since conventional methods are not enough for such complex work, especially, because they were conceived for other purposes.
Big Data and Data Science courses
Fortunately the data science current (Data Science) It provides more than is necessary to deal with it, and its teachings are available to anyone as other areas have managed to bring its news to the entire world.
This time, they speak to us from U-tad about a new pair of postgraduates that they are teaching and that are closely related to the handling of large volumes of data.
The first one titled Mster Telefnica Big Data & Analytics and focuses on equipping young engineers with enough to understand topics like HDFS file systems and the theoretical foundations of a data scientist, the management of non-relational databases, the work of interactive visualization of Big Data, the knowledge of Business intelligence o Business intelligence in such volumes of data and, in general, the abstraction of knowledge in different entities and with distributed databases.
The other option, with a much more technical component, is referenced as Data Science Expert and its area of action is in the field of data analysis (Data Analytics) in the new environments of Big and Small Data Business Intelligence. Handling of Hadoop, Storm and Spark, advanced data analysis with R and RStudio (with special libraries), programming in Python, NoSQL environments, machine learning techniques (Machine learning) and even legal and regulatory aspects of data capture and processing are included in the powers of your curriculum.
In short, the invitation is not superfluous to soak up such an important new space of development that seeks the professional and academic world, since the research is enormous – to take advantage of this huge mine of information.