PDEng Program Data Science

Teaching disruptive new business models to data entrepreneurs
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. 
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 scientist
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.
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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. 

Course Program

The PDEng program Data Science combines techniques from statistics, computer science, mathematics, and design theory with the business acumen to explore data sets, gather insights, visualize results, and communicate meaningful findings, whilst taking account of underlying ethical and legal constraints. Its graduates make sense of data and have the ability to articulate their discoveries and recommendations regarding industrial and business design and decision processes to those not schooled in the world of data. 

 The essence of knowledge transfer in the program is: (1) to be able to apply knowledge at hand to solve a problem from practice, (2) to know what knowledge is of use in solving that problem, (3) to detect and acquire missing knowledge fast. Students in the program are responsible for their own learning process. 
In cooperation with the program’s industrial partners, students are provided with the resources, directions, and opportunities to be engaged in purpose-driven training projects, generalist and specialist courses, and extensive one-week workshops and assessments. Blended learning is part of the educational structure, where knowledge is acquired from open source courses from Coursera, eBooks, and GitHub.

Students are offered a personalized program with a wide focus from general data analytics and computing, modeling and simulation, data mining and process mining, data exploration and visualization, to ethics and law, with application to a large diversity of data domains such as health, smart mobility, agro, energy, and pharma. 
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:
  • Identifying data related problems in product and process development
  • Designing data architectures and data structures
  • Designing data analysis and data visualization software applications
  • Developing data models and simulations in a design context
  • Applying techniques from the fields of data mining and process mining
  • Facilitating effective communication with scientific and non-scientific collaborators
 
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:
 
  • Acquire knowledge independently
  • Use knowledge creatively to fetch and analyze data and turn findings into actionable changes for external stakeholders
  • Manage the full data-to-business cycle with respect to project planning and time planning, taking into account the stakeholder’s constraints
  • Communicate and report results, conclusions, and recommendations clearly
  • Coach other participants in the process
 
Graduates will be uniquely positioned to pioneer developments with leading positions in industry, business, and government

Curriculum

Partnering with industry
The PDEng program Data Science offers an interdisciplinary learning environment devoted to professional training in knowledge, skills, and competencies that are required of a data scientist at doctorate level. The program aims to broaden and specialize the professional profile of students in a personalized way. By means of nine independent educational modules, the program integrates the fundamental elements of the data science process: preparation, exploration, analytics, visualization, mining, and business intelligence, from a design perspective.
The program is run in close collaboration with the partners from the ecosystem. These partners provide real-life data challenges that are addressed by way of
 
  • Assignments in the modules
  • Short- and long term projects carried out by one or two students full time
  • Data Challenge Weeks with one hosting partner
  • Lectures on specific data domains

The PDEng Data Science graduate 

  • Has the skills to properly apply techniques from  statistics, computer science, and mathematics in a variety of data domains.
  • Shows curiosity with the desire to discover, distill, and model a problem down to a clear set of concepts and hypotheses to be tested.
  • Is skillful to use data to tell a story and communicate that story effectively to stakeholders.
  • Demonstrates cleverness to look at a problem in different creative ways.
  • Collaborates in teams and with a variety of stakeholders to get optimal results.

Career prospects

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.

Admission

Your Profile
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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:
  • Mathematics & Statistics – a least two semesters of mathematics and statistics. Courses on calculus, linear algebra, statistics, optimization theory, and probability theory are required. Courses on topics as operations research, signal and time series analysis, and dynamical systems are recommended. The courses should indicate that the applicant has achieved the mathematical and statistical maturity to be expected of an upper level mathematics / statistics / econometrics  graduate.
  • Computer Science – at least two semesters of computer science. Courses on programming, algorithms & data structures, databases, data mining/machine learning are required. Courses on topics such as object oriented programming and web development are recommended. The courses should indicate that the applicant has achieved solid knowledge and experience with the computer science aspects that are highly relevant for Data Science.
  • Data Science – Experience with the application of Data Science technology to real world problems, via projects carried out during their education or their working experience afterwards. 
3. Candidates show high motivation and eagerness to develop the skills to:
  • Combine generalist thinking and expert thinking in various data domains
  • Formulate opportunities and discover value in data
  • Solve complex problems in a data driven environment.
  • Demonstrate sense for entrepreneurship, business and industrial processes
  • Execute projects in a well-managed, professional way, optimally using resources and obeying constraints
  • Acquire knowledge through a self-directed learning style
4. Candidates have an attitude that shows:    
  • Technical expertise - desire to become an expert in one or more data domains with a  generalist thinking attitude
  • Curiosity - desire to discover, distill, and model a problem down to a clear set of concepts and hypotheses that can be tested
  • Creativity - desire to look at a problem in different ways and find novel solutions
  • Communication skills - desire to communicate effectively with all stakeholders, on all aspects of any data science project that include requirement elicitation, project progress, developed solutions, up to use of data to tell a story.
  • Social skills - desire to collaborate with others, with similar and different backgrounds, to tackle challenging problems

Assessment

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. 
Step 1
A first selection takes place based on the following documents that should be submitted online  to the secretariat of the program:
  • Completed online application form
  • Application letter
  • Curriculum vitae
  • Copy of the MSc-diploma
  • Certified educational master program and grades
  • Proof of level of English language (for example IELTS)
  • Copy of ID or passport
Step 2
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:
  • Write a document on personal understanding of the data set to be challenged and a personal introduction.
  • Take part in all team activities such as group meetings and technical discussions.
  • Do part of the final presentation at the end of the Data Challenge Week. They present results and conclusions of their group to the representatives of the company or business that set the data challenge.
  • Candidates fill out a personality questionnaire. They fill out the MPT-BS: Multi-Cultural Personality test – Big Six.
 
Step 3
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
Important note
Due to the uncertain situation created by the COVID-19 pandemic we change our assessment procedure until further notice. The most important change is that the assessment will take place entirely online. Next online Data Challenge Week (DCW) takes place from September 27th up to and including October 2nd, 2020. The online DCW consists of a 4-day online teamwork assessment concluded with an online interview. Due to the online character of the next Data Challenge Week combined with the uncertain international travel regulations, we give priority to applicants that currently live in the Netherlands or in countries close to the Netherlands. We hope this situation will be over soon so our program can revert to its normal assessment procedure. 

Note:
The registration for the data challenge week of September is closed. If you want to apply for the PDEng DS program, please send in your application from November 2020 to January 2021!
Downloads
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.
“There’s a deep sense of community within JADS that is really conducive to learning”
Parvathy Krishnan 
​(first year PDEng student 2017-2018)
For more information about the PDENG program or applying please contact us at DPdatascience@tue.nl.