Data Scientist: Definition, Job Description & Training

by | 2 October 2019 | Basics

Data Scientist Training - With the massive increase in data volumes in companies and organisations and the associated need for data analysis, the demand for professionals is growing. One job description that is closely related to this and was called the "Sexiest Job of the 21st Century" by the Harvard Business Review, for example, is the Data Scientist. This also raises the question of data scientist training, opportunities for on-the-job training and suitable courses of study.

Need and shortage of Data Scientists

Why is there a shortage of Data Scientists in the market? The question of the shortage should be preceded by the question of how the term Data Scientist is interpreted. A rough distinction can be made between two job profiles:

  • Enterprise Data Scientist: In a way, represents a mix of business economist, IT specialist, statistician and communication expert.
  • Academic Data Scientist: Developed pure Algorithms and works with "ideal" data and is less practice-oriented than method-oriented.

In the enterprise environment, it is rather rare that a completely new algorithm is developed. Rather, existing concepts are adapted or extended to the concrete problem, since a completely new development of modelling procedures often takes too long.

A study by McKinsey Global shows that demand in the US alone will far exceed supply in the coming year. A difficulty that is not reflected in the sheer numbers: There is not the a Job description of the Data Scientist or only a special Data Scientist training. The requirements in the respective industries are very different. This raises the specific question: What are the different opportunities for training as a data scientist in Germany and what does everyday working life look like?

Data Science Definition

Data science - i.e. the science of data - is initially a bundle of different disciplines such as Computer science, mathematics, business administration and statistics. The origin of the subject is not, as one might assume, the university, but it developed out of the economy in the course of changing needs. This is also the reason for the high practical relevance of the occupational profile as well as of the science of Data Science and, last but not least, data scientist training.

Broadly speaking, data science is about, Examine data using scientific methods and in the context of companies and organisations. The requirement profile for a data scientist grows accordingly through the embedding of his or her activity in companies.

The job description of the Data Scientist is shaped by practice

Data scientists are not only familiar with the Data evaluation employed, but must understand business contexts and communicate the resultsn can. However, the bulk of a data scientist's day-to-day business is to identify and compile suitable data sources and to prepare and carry out the analyses.

A Data Scientist bears a great deal of responsibility in part because of the Results of the data analyses much can depend on it. That is why it is of enormous importance to check the underlying data again and again for plausibility, completeness, correctness and relevance.

Modern Data Scientist
© Marketing Distellery

Solve problems like a detective

The "Enterprise Data Scientist" can again be subdivided into "internal" Data Scientists who are employed by companies, and "external" Data Scientists who active in an advisory capacity are. The externals are often consulted by strategy committees against the background of digitalisation and Industry 4.0, for example.

As a service provider, they also work together with the different departments in a company, create root cause analyses for specific questions or act as "sparring partners" for internal data scientists. In this function, they have an unbiased view of the facts, can bring in fresh ideas and show specialist departments alternatives that they may not have thought of before. They also actively offer companies help, raise questions or make departments aware of possible solutions in the first place.

Finally, Data Scientists translate requirements into abstract data-based questions and then develop solutions that are answer specific business questions. The procedure is based on hypotheses that are rejected or confirmed. This hypothesis-driven, experimental way of working is very similar to scientific work and this also explains the term data scientist.

Courage is also required, Questioning problemsWhat is to be achieved in the first place and why? In the search for the solution to very tricky problems, the data scientist thus acts almost like a kind of top detective.

If you consider this comprehensive and very demanding requirement profile, it quickly becomes clear why there is a shortage of data scientists. The combination of very well-developed communication skills and great technical know-how is a major hurdle.

Good to Know:
Gartner as well as McKinsey expect that the demand for data scientists in 2017 will already be 60 % greater than the existing supply. IDC puts the number of data scientists needed by 2018 at just over one million, and sees a parallel fivefold increase in the need for people with good data management and interpretation skills.

In the meantime, (postgraduate) degree programmes and further training opportunities for data scientists are being created in many places in Germany, Switzerland and Austria. However, the success of these measures still has to be proven in reality. Often there is still a gap between theory and practice. Worldwide, few companies therefore also offer Trainee programmes for Data Science and Trainee-Programmes for Data Engineers an.

Technical know-how and communicative strength

The basis is a good knowledge of computer science, business administration, mathematics and statistics. A data scientist must therefore:

  • Be able to understand business processes
  • Interpret results of analyses
  • Trace data generating processes

But also the deep understanding of data structuresand models are mandatory competences. In addition, there are the programming skills to be able to work or interact with this data. This includes, among other things, linking different data sources, creating complex queries and mastering very large amounts of data.

Statistical and analytical skills come into play when historical data is used to make predictions about zsions of future events are to be derived. The ability to understand and analyse processes and to visually present data and analysis results is also very important.

The profile is rounded off by a high problem-solving competence and good communication skills. These are necessary because complex facts and models must be communicated in such a way that management, users and customers trust the solution, and so that the customer perspective and vision do not get lost on the way through the data jungle. After all, it is about telling the story that lies in this data, packaged appropriately and relevantly for each target group.

Research and study: Data Science at German universities

Even though data science developed out of the business world, the scientific study of the topic is now an integral part of the university landscape in Germany. The different research areas in which data science is applied show how universally applicable the methods of data science are. From medicine to the humanities to space research, there are many different fields of research. Numerous research areas in which data analyses bring new insights.

At the same time, the universities and universities of applied sciences thus offer an opportunity for data scientist training. In the meantime, more than 20 universities and colleges in Germany and Austria already offer data science courses. The majority of these are Master's degree programmes.

Anyone who chooses this path to become a Data Scientist should should make sure that they acquire knowledge in the fields of following five areas to acquire:

  1. Analytics
  2. Data Management
  3. Information Design & Communication
  4. Entrepreneurship
  5. IT

In our article on "Study Artificial Intelligence and Data Science"you can find more information about the Bachelor's and Master's degree programmes.

Interest in digital professions is growing strongly

In Germany, the profession of data scientist is more popular than any other. This was also recently revealed by a Data analysis of the job portal Glassdoor. Around half a million search queries were evaluated. In addition to the data scientist, who landed in first place of all queries, a total of 5 new digital professions landed in the top 10. Among others also the Software Developer (4th place), the Data Analyst (8th place) and the UX Designer (9th place). The job portal Monster.de also recorded a doubling of searches for the profession Data Scientist in recent months.

According to a recent Study of the job platform "Jobfit there is not only a strong increase in the demand for data scientists. The analysis of more than 64,000 job advertisements has shown that above all an academic background is standard in the advertisements and soft skills such as communication skills, teamwork and creativity are even more important than knowledge such as SQL or Machine Learning are in demand.

Job profile Data Scientist
© Joblift

Data Scientist education, training and on-the-job training

The But studying is not the only wayto become a data scientist. Rather, this is a great opportunity for engineers, economists, statisticians, mathematicians or related fields. Commercial providers such as the Fraunhofer Gesellschaft offer training, courses and further education in which specific individual skills can be learned. The advantage of these alternative training paths for Data Scientist training is that practical knowledge from certain areas is often already available. Those who have the relevant prerequisites can also acquire key qualifications as part of a trainee programme.

Training courses for beginners or our Data Academy are a good opportunity for a first approach towards a Data Scientist training. Furthermore, we at Alexander Thamm GmbH are committed to empowering our customers and the employees of our partner companies to realise the added value from their data through training and education.

Data Scientist Training

The profession of data scientist is associated with many hopes and opportunities. The job titles and training paths in this still young field of activity are still partly inconsistent, but reflect the great diversity of the fields of application for data scientists. From marketing to Industry 4.0, everything is possible.

The high degree of specialisation also makes it difficult to establish or recommend a uniform vocational training or a uniform course of study as the golden path. It is precisely this wide range and high level of practical relevance that makes the profession and data scientist training such a unique experience. attractive career option. Skilled workers and specialists from a certain field can further their education through suitable measures and thus obtain a promising and currently one of the most sought-after jobs.

The perfect Data Scientist does not exist

Of course, professionals contribute differently to projects according to their respective strengths and preferences, and data scientists also develop their own areas of focus in their work. Basically, however, all of these different skills are required and encouraged of all applicants in the data science field.

By the way, we have not yet seen the perfect data scientist and if you take the sum of all skills and put them in relation to the rapid technological development, the perfect data scientist will probably never exist. Rather, the point is that a data scientist forms the brace to be able to solve data-driven questions from start to finish, and to be able to replace experts like statisticians or Data Engineers to be used in a targeted manner.

If, for example, the colleague from the specialist department is dissatisfied with the "performance" of his work, then the data scientist must be able to understand the following points of view:

  1. Business perspective: For example, are my project goals at risk?
  2. Data view: Are my database queries running too slowly?
  3. Analytics view: Is the forecast quality / model performance too poor or is the Data visualisation too slow in displaying the data?

Without the data scientist, the experts here would spend hours pondering what the colleague might have meant.

Conclusion:

The conditions for a data scientist are excellent: very good earning opportunities, a diverse, multifaceted field of work and, above all, great potential for the future. Although there is still some resistance to overcome in companies - keyword "silo thinking" - and it is often a question of corporate culture whether digitisation approaches will prevail, the "megatrend" of digitisation has long since ceased to be a trend, but an exponentially accelerating development that can no longer be stopped.

Intelligent machines, for example, will take over more and more activities from humans, also in the cognitive area, for example in pattern recognition and idea generation. Here, it is not a matter of replacing people, but of meaningfully supplementing their respective skills and processes. This is precisely why the need for data scientists will continue to rise.

With their expertise, it will be possible in future to work together with intelligent machines and not against them. Because they manage to translate the business or technically oriented question into a data-driven question - and that is not possible without sound evaluation and development of insights.

In the foreseeable future, hardly any company will be able to do without the services of data scientists, because big data and data analyses will no longer be just "nice to have" but crucial for business success and competitiveness. That is why there must be further intensive investment in the training of specialists.

<a href="https://www.alexanderthamm.com/en/blog/author/michaela/" target="_self">Michaela Tiedemann</a>

Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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