Meet Luis, Data Mentor, for our Data Analytics course Barcelona
In this series, you’ll be able to get to know the Ubiqum Code Academy team and hear what it’s like to work as a Mentor in one of their bootcamps.
In this article, we talk to one of our Data Analyst bootcamp Mentors, Luis, who dissects what really makes a good analyst and why the Ubiqum Code Academy courses are perfect for beginners. He discusses big data, machine learning and discusses his feelings on migrating from the Ubiqum Barcelona campus to Berlin, where he’ll lead the Data Analytics course that starts on the 12th February.
How did you first get into Data Analytics?
After finishing my degree in physics, I spent six years working in a nuclear physics research group in Valencia. One of my most important responsibilities as a researcher was to analyse data to later publish scientific articles. Given my technical experience, I decided to migrate to the private sector and did the Ubiqum Data Analytics part-time remote course. Once I finished the course, I became part of the Ubiqum staff, performing my first projects as an analyst.
What kind of skills or characteristics are needed to pursue a career in data analytics?
The most important characteristics needed to successfully perform tasks of analysis are restlessness and communication. Carrying out the technical part of a project requires training, but the ability to transmit the findings to clients or other departments of the company is crucial. Likewise, correctly interpreting the results of an analysis requires experience and knowledge of both the business and the specific context of the project. A great analyst must be a great communicator.
The use of a programming language and dashboard tools are essential, but what will differentiate a good analyst from an average one is their ability to create mental structures that allow them to successfully address any type of problem. The analyst’s real work begins once he/she has the result of the analysis and has to interpret that result and effectively transmit the knowledge acquired to his/her client or to the corresponding department of his/her company.
Is there any difference between a data analyst and a data scientist? What would you call yourself?
Currently, the line that separates a data scientist from a data analyst is not well defined. In theory a data analyst is a professional who performs data analysis, while a data scientist is able to develop their own algorithms.However, today the same job is given different names depending on the company and not on the responsibilities attached to it.
Since there is not really a clear difference between the two positions, I would say that my denomination changes from project to project, depending on the tasks assigned to me. However, I like to focus on generating and solving business questions. Currently, I consider myself a data analysis mentor with good communication skills.
Why is the data analyst bootcamp at Ubiqum Code Academy perfect for beginners?
Ubiqum offers a global and complete overview of the work of an analyst that isn’t found anywhere else. The Ubiqum experience consists of 5 full-time months in a simulated work environment where the working conditions of an analyst are recreated from day one. As we learn the skills needed for the job, we get in touch with companies and professionals in the sector, presentations are made open to the public and reports are delivered with the same requirements of a company.
The learning curve is designed so that analysts without previous experience acquire all the necessary skills during the course. Analysts with technical or business experience deepen their knowledge while working on the area they feel less confident in. At the end of the 5 months, the student is an all-round professional analyst, capable of working independently from day one in any company.
What can a prospective student expect to learn on the data analytics course at Ubiqum Code Academy?
The first thing that a future analyst will learn is to think like a professional. Our analysts learn to face any type of problem in an autonomous and efficient way, find information in the right sources and solve projects with presentations and quality reports.
At a technical level, it is essential to be able to work with a programming language and with data mining or dashboard tools. In our case, we work with R as a language and RapidMiner as a data mining program. Our students not only learn to use these tools, but also understand the processes of analysis in general, meaning that they’re not very sensitive to a change in technology. Each company has its own analysis tools but the adaptation period for our students is shorter because the process is what’s important, not the technology used for it.
In the Ubiqum Data Analyst bootcamp you focus on predictive analytics, what is the difference between this and descriptive analytics and business intelligence?
The descriptive analysis consists of using tools that do not modify the data to obtain information about the data itself. These tools include filters, histograms, graphs, etc.
Business intelligence is a form of descriptive analysis with the fundamental difference that a graphical user interface (GUI) is used so that people without programming knowledge or little technical skills can perform these tasks.
Finally, predictive analysis, and in our case machine learning, are tools that allow us to anticipate the value of a variable based on information we have. We can explain these concepts with a very simple example of customer segmentation. If we conduct a survey of buyers of a particular product made by our company with postal, geographic, economic and social information, we can anticipate if an unknown person is a potential customer by simply filling out the same survey.
For those who are just starting out, could you explain what machine learning is and how it is used by data analysts?
The concept of machine learning is used when we train an algorithm with some data (training set) and we make predictions in a new data table, which we have not used to perform the training (test set). Analysts use these algorithms to solve problems such as customer segmentation, image recognition, stock and sales predictions or number recognition within images or written language.
The term ‘big data’ is thrown around a lot, what exactly does this mean?
The term ‘big data’ refers to the storage and management of large amounts of data that must be worked on in a distributed manner. This data is cut into pieces that are stored on different servers throughout the world.
After the data management is done on each of these servers, tasks can be performed in parallel that would take days or even years in reasonable time frames.
After completing the course with you, what do Ubiqum students go on to do — what kind of companies do they end up working for?
After finishing the course, our analysts land data analyst positions in very diverse companies. There are analyst positions in marketing companies or positions related to marketing that add machine learning to the process of creating campaigns. Some people join consultancies as data analysts for clients, while some get taken on by large multinationals to analyse in-house data of the company itself.
The experience in general is that students begin work in positions of responsibility as companies are still creating or defining their data analysis departments. In addition, depending on the company, they add various analysis, dashboard and visualisation technologies to their profile.
You’re moving to the Ubiqum Berlin campus in February, are you excited? Nervous? Sad to leave the Barcelona campus?
Leading the data analytics experience in Berlin is a very attractive and, at the same time, very demanding challenge. We are going to start the first course on the 12th of February with a group of very keen analysts. The German market is very advanced and very demanding one and for that reason, we’re excited to generate our first analysts there.
On a personal level, I really want to start the course, although I will miss the people of Ubiqum Barcelona. We are a young team, eager to make the Ubiqum project as successful as possible and my relationship with the mentors and the team is unbeatable. In any case, communication with Barcelona will be constant and I expect team visits. Besides, it’s never too late to study German.
Any last words of advice for anyone looking to pursue a career in data analytics?
Never stop learning market trends and keep an eye on neural networks because they are very fashionable. Make tutorials, incorporate technologies and new algorithms to your range of skills. Read about technology, business and application of data analysis in all sectors of industry and research.
Work hard, be curious and you will succeed as an analyst. And be prepared to pack your bags because working as an analyst can take you anywhere ;)