The Professional Doctorate in Engineering program Data Science is a two-year post-master's program. It qualifies students with an MSc degree in mathematics, statistic and computer science to become top-level professionals. Such professionals help industry and business with their decision-making processes based on real, actionable data or become innovative entrepreneurs in the data science ecosystem.
Part of the Stan Ackermans Institute
The PDEng program Data Science is part of the 4TU.School for Technological Design, the Stan Ackermans Institute, that offers more than twenty similar two-year post-master technological designer (PDEng) programs. The institute is a joint initiative of the three universities of technology in the Netherlands.
Hosted by JADS and DSC/e
The Jheronimus Academy of Data Science (JADS) – a joint initiative of Eindhoven University (TU/e) of Technology, Tilburg University (TiU), and the Data Science Centre Eindhoven (DSC/e) – hosts the PDEng program Data Science. The academy is a thriving data science community, in which postgraduate education, innovation, entrepreneurial activities, lifelong learning, and scientific research are integrated. A major part of the PDEng program Data Science is related to real-life, cross-functional projects with partners in the data science ecosystem.
Become a top-level professional data scientists
The PDEng program Data Science qualifies its students to become top-level professional data scientists. Candidates hold an MSc degree in fields such as mathematics, statistics, computer science, engineering, econometrics, geophysics, bio-informatics, and computational chemistry. They aim for a career in a big data environment that truly leverages their academic experience and talents in the fast growing in-demand field of Data Science.
The qualified data science professional possesses competencies at high level related to project management, social and communicative skills. They help industry and business with their decision making processes based on real, actionable data and become the innovative, entrepreneurially minded specialists in the data science ecosystem.
Students develop the generalist skills necessary to oversee the entire data pipeline, as well as the specialist skills related to a data science field or data domain. Students become skilled in:
Students learn to work in teams, communicate with professionals from various data domains, carry out project management, and learn the social skills and competencies to become of indispensable value to business and industry.
Besides the specialist themes, the program focuses on generalist themes, as well, thus training its students to become tomorrow’s leading data science professionals. Students are trained and coached to:
Graduates will be uniquely positioned to pioneer developments with leading positions in industry, business, and government
The PDEng Data Science graduate
Leading companies, markets, and businesses in many fields are hiring big data infrastructure professionals at doctorate level to help them store, explore, process, access, visualize the terabytes of data that they collect daily. While the volume of data produced and stored grows exponentially, there is a severe shortage of talent to design, maintain, and optimize data infrastructure and data pipelines and to turn complex and unstructured data into valuable business insights. More than 90% of the foreign graduates from the PDEng program Data Science find a first job in The Netherlands.
Candidates are creative problem solvers and team players who seek interaction - within a multidisciplinary environment - to address problems. They are eager to learn and are dedicated to the quality and quantity of knowledge gained. They have excellent technical skills.
1. Academic Degree
Candidates possess an academic master degree (MSc) in Mathematics, Statistics, Computer Science, or in a relevant application field of Data Science such as Econometrics and Bio-informatics. The MSc-degree is from an accredited institution comparable to the Eindhoven University of Technology or Tilburg University.
2. Candidates have an academic background that includes:
3. Candidates show high motivation and eagerness to develop the skills to:
4. Candidates have an attitude that shows:
Candidates can enter the program only after having successfully passed the full admission procedure. The procedure has three steps and involves a thorough assessment by means of a Data Challenge Week and an application interview. Data challenge weeks are organized twice each year, at the end of March and September. Due date for applying is the end of February/the end of July. The program selects the top 25% from the pool of candidates. Having finished a successful application procedure, candidates will be employed by Eindhoven University of Technology as Technological Designer in Training.
Based on the documents send, the program selection committee invites the candidate to take part in an assessment in the form of a 24/7 Data Challenge Week. The criteria for assessing applicants in the Data Challenge Week focus on the profile of the candidate as described above. Thus, besides on their technical background, applicants are assessed on the following distinctive attitudes and skills: creativity, self-awareness, critical thinking, team player, and communication.
Candidates will also be asked to:
The final step of the admission procedure is the job-interview. The interview is arranged immediately after the assessment. The scientific director, program manager and professional development coach form the interview committee. The focus is on the candidate’s technical background, motivation and the assessment results.
More information and apply for admission
More information and apply for admission
For more information about the PDEng program or applying please contact us at DPdatascience@tue.nl.
Important note: The next Data Challenge Week is from September 21 - 28, 2018. We have 20 spots on a first come, first served base.
PDEng program Data Science
information brochure (pdf)
Application form PD Data Science (doc)
Candidate profile & assessment (pdf)
Data Challenge week (pdf)