How to become a Big Data expert | NTT DATA

Thu, 16 August 2018

How to become a Big Data expert

Big data is gradually reaching maturity with varied adoption between different sectors, but growth in all cases. This leads to a booming market in which the battle for talent is no longer only fought in technology or consulting companies, but also in niche companies, startups and end clients, consisting in a strategic asset for all.

All studies carried out on the market situation attest to this. In 2015, the MIT Sloan Management Review already predicted that 40% of companies surveyed were struggling to find and retain data analytics talent. And the picture is starting to look even bleaker. International Data Corporation (IDC) predicts a need by 2018 for 181,000 people with deep analytical skills, and a requirement five times that number for jobs with the need for data management and interpretation skills.

With these predictions, its clear jobs certainly won’t be in short supply for data and analytics professionals, something that is encouraging young people to follow this career path for a secure future.

Steps to take towards a career in data intelligence

Big data is a very generic term that covers all disciplines related to data and analytics, including: information governance, data processing and architecture, information visualisation, advanced analytics and artificial intelligence, management of infrastructure and functional experts. Experts who know how to find high-potential business applications offered by technology. We need to identify the discipline that motivates us most and find specific training paths.

Sources of professionals are increasingly heterogeneous, due to the transversal impact of technology in business. In our latest artificial intelligence projects, in which Natural Language Processing (NLP) is applied, we have even been using computer linguists. However, in this article, we will focus on the current most highly sought-after profiles: data architects and data analysts, who are the pillars of data processing and architecture; and data scientists, who focus on the field of advanced analytics.

Data architect and data analyst profiles tend to follow an IT or Telecommunications Engineering career path, but people with many other specialisations can adapt to this field if they demonstrate strong knowledge of and an interest in programming and technology. This includes people who specialise in industry and civil engineering.

Data scientists tend to have studied Statistics, Applied Mathematics or Physics, or have knowledge of various programming languages, the most common of which is R, and increasingly, Python.

Either way, nowadays, there are transferable skills that span all profiles, such as an eagerness to learn, ability to adapt in a very dynamic technological environment that is constantly evolving, and the ability to share and participate in expert communities with increasing ease, for example:

  • Meet Up, which organises gatherings in major cities.
  • Kaggle “The Home of Data Science & Machine Learning” especially designed for data scientists.
  • Github “Built for Developers” which is aimed at data architects and data analysts, although it is open to all developers, not just those specialised in Big Data.

If you really want to become an expert and be hired (and paid) as such, you need to be able to demonstrate your knowledge through what you bring to these communities.

In any case, professionals joining the jobs market and those interested in a career change have access to universities and schools. Logically, these institutions are up to date with market possibilities and offer a wide range of specialisations linked to big data, data science and machine learning.

As I mentioned before, it’s important to understand what role you would like your career to progress towards and choose a Master’s that best suits your interests. Bear in mind that they will all offer a generic overview, which will later become more specialised in a specific area. For this reason, you need to at least have a strong understanding of the programme on offer, and its specialisation, paying special attention to the amount of hands-on practical work carried out as part of the programme.

In this sense, one of the programmes I found most surprising – due to its very distinct, nerdy (their words, not mine), and eminently practical focus – is the Keepcoding “Big Data & Machine Learning” bootcamp. This startup from Silicon Valley churns out new developers in less than 10 months.

In addition to this programme, and given the interest training in this sector has sparked, here are a few links to some more past and ongoing courses where I came across some great professionals:

Master Business Analytics UPF

Master en Data Science (UB)

Máster en Tecnologías de la Transformación digital ( La Salle)

Master en Data Driven Business ( Barcelona Technology School)

Master en Big Data Science (UVA)

Master Big Data & Data Science (UTAD)

Master in Business Analytics & Big Data (IE)

Máster en Big Data. Tecnología y Analítica Avanzada (Comillas)

II Encuentro Big Data Talent Madrid 2018 (UCM)

Máster Visual Analytics & Big Data (UNIR)

Either way, beyond whichever Master's you choose, make sure that your dream job affords you the opportunity to learn in real projects and to retrain through continuous training programmes that will keep you up to date technologically speaking.


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