Post-Master's Degree in Big Data

The Post-Master’s Degree in Big Data covers a wide range of disciplines; it is designed to be a comprehensive program that incorporates societal, legal and economic aspects. But it focuses above all on technical issues, IT tools and mathematical methods. Applicants must therefore have the necessary foundations in mathematics, computer science and statistics to be able to express themselves in the codes used for the training program. Most of the machine learning courses use Python. However, applicants do not have to be experts in Python when they start the Post-Master’s program. We ask for a strong knowledge base in mathematics which is critical to pursuing formal studies in this area.

The Post-Master’s Degree in Big Data does not simply teach students about the technological building blocks which may soon become obsolete. Instead, we equip them with the knowledge they need to evolve in this rapidly-changing field. It is essential to have a solid understanding of the formal language, meaning a Bachelor’s level in mathematics, basic numerical analysis, probability, statistics and optimization.

It is a progressive training program. The most advanced concepts in mathematics and computer science are not tackled right away. The first term focuses on refresher courses. Students work in groups so those who are the best at mathematics may help the others.

We offer a MOOC called “The Basics of Big Data” on the France Université Numérique (FUN) platform which can help you assess your level. If an applicant successfully completes the MOOC, does that mean that he/she has the sufficient level to enroll in the Post-Master’s degree program?

Applicants must have a sufficient command of the concepts listed below and be comfortable using them. Familiarity with these concepts is not enough – applicants must be able to use them easily.

Mathematics

• Analysis: numerical sequences/series, principles of differential calculus, Fourier analysis and Hilbert analysis
• Algebra: vector spaces, linear applications, matrix calculus, scalar products, quadratic forms
• Optimization: Lagrange multipliers, gradient descent
• Statistics: random experiment, estimator, risk, likelihood maximum, method of least squares, confidence intervals, statistical testing
• Probability: laws of probability, random vectors, conditional distribution/expectation, law of large numbers, central limit theorem, Markov chains

Computer science

Java programming

• Basic concepts: how to define a variable, control structures: for, for each, while loops
• Classes and objects: how to define a class, attributes, methods; notions of public, private, protected; difference between a static or normal (non-static) variable or method; inheritance: how to extend a class; interfaces: how to define an interface and implement a class; polymorphism: how to overload a method, understand how overloaded methods work
• Basics of Java standard library: collections (ArrayList, HashMap, HashSet, etc.); how to insert an item in a list, delete an item at the beginning, end or middle; when to use collections (for example, ArrayList vs. LinkedList).
• How to take a mathematical or textual description from an algorithm and implement it

Python programming

• Master concepts equivalent to the Java concepts listed above (except for public, private, protected interfaces).
• Unix/Linux
• Master basic commands.

Télécom Paris courses take place over a nine-month period (late September to early June). Télécom Paris has developed a comprehensive approach to training future data scientists. The school combines recognized expertise in the field of machine learning and applied mathematics, a competitive edge in research, and an industry perspective and services.

Télécom Paris also has an incubator. The School collaborates with talented, multi-faceted and multi-tasking individuals, who are able to apply the full range of skills needed for the field of data science, from data acquisition to service production. These are just some of the many topics covered extensively in the densely-packed training program. It is not necessary for students to plan on learning other topics in addition to those covered in their courses.

We seek a diverse variety of professional experience when selecting the thirty or so students who will make up the class. In this year’s class, for example, there are students with fifteen or twenty years of professional experience who share their valuable experience and lessons they’ve learned about project management. There are also many students who are continuing their studies after completing a Master’s degree.

Above all, we seek individuals who have computer science and mathematics skills. Applicants must also be curious, motivated and passionate about data. People who want to understand how analysis systems are set up and discover what is hidden in data. They must have a research mindset and be able to read scientific articles.

Lastly, the interview focuses largely on the extent to which applicants have clear career goals. Applicants must be able to talk about their future goals and have a coherent plan.

You must have experience with programming to enroll in this program. However, we are not a programming school, so you do not have to actually be developer. Applicants may, for example, prepare for the program by completing the MOOC in order to refresh their skills. The aim of the program is rather to provide students with an in-depth understanding of concepts and the ability to use current technological building blocks like Cloud services, environments for searching in poorly-structured databases, distributed storage platforms etc.

Around sixty. Due to growing interest for this program over the last two years, we have increased our faculty in order to teach more students and ensure a good learning environment. Most courses are taught by research professors. We only call on outside instructors for certain specific subjects. We have therefore recruited research professors in a variety of disciplines in order to preserve this aspect of our training.

To apply to the Post-Master’s program, you must have one of the following degrees:

• an engineering degree in computer science or telecommunications
• A Master of Science in computer science or applied mathematics
• A four-year scientific or technical degree, equivalent to a Bachelor’s Degree, in computer science or applied mathematics, and at least three years of professional experience
• A degree from a foreign university equivalent to five years of higher education, a MSc or MBA, in computer science or applied mathematics

Due to the great number of applications we receive, we are not able to pre-assess applications or conduct interviews before the application process. We advise you to read these FAQs carefully before you decide to apply. You may also consult interviews with three students in the Post-Master’s program:

If you are completing the final year of a Master’s degree or an engineering degree, you must upload your transcript showing the last three years of study and ask at least one of your professors to provide a letter of recommendation, which they can send directly to the Master’s program address, specifying your name and the Post-Master’s program to which you are applying. You will then be required to submit your diploma or certificate of completion as soon as possible, and no later than 15 December.

The TOEIC exam is not mandatory. However, applicants must have a sufficient level of proficiency in order to take courses taught in English. Since English is also the language of technology and science, it is essential to be able to read scientific articles. The TOEIC exam is the best way to demonstrate proficiency in English. Applicants who have not taken the exam can demonstrate their level of English with academic transcripts, for example.

The program consists of lectures in which the professor presents a topic, combined with lab work and projects throughout the year. The lab facilities feature advanced equipment. IMT (Institut Mines-Télécom) has a platform called Teralab, on which students can work, in addition to commercial platforms like Amazon Web Services.

There are also professional seminars every Thursday, giving students the opportunity to connect with companies who are also future recruiters. These seminars allow students to learn about the challenges related to Big Data in a wide range of fields (health, finance, energy, marketing etc.) Industry events are also organized at Télécom Paris, such as the Post-Master’s Degree Forum in March, and students are invited to shows including Big Data Paris in March and DataJob in November.

Students can also interact with engineering students, with whom they share certain courses.

Télécom Paris has an Economic and Social Sciences Department and, in particular, a Personal Data Values and Policies Research Chair, which explores how data is used to create value and issues related to privacy and legal aspects.

We have three courses which are overseen by the Economic and Social Sciences Department. One course focuses on internet data with an emphasis on intellectual property and rights, another course is dedicated to the Big Data ecosystem and includes many guest speakers from major companies, start-ups and government institutions to explore how data is used to create value, and the third course focuses more specifically on the econometrics of Big Data.

We also organize professional seminars every Thursday where we invite representatives from a wide range of industries (health, defense, e-commerce, finance, insurance etc.) to talk to students about their experiences managing Big Data. Economic development is a key part of the program, but it remains very technical overall.

They are non-technical seminars. A summary of most of the sessions can be found on the website for the Machine Learning for Big Data Chair. The idea is to give companies the opportunity to present a specific field or a challenge related to Big Data. This challenge can relate to computing platforms to explain different systems, geomarketing architecture, cybersecurity, defense, finance, health, new technologies etc. It’s also a way for companies to meet potential recruits.

The ecosystem of Big Data education at Télécom Paris comprises consultants, Chief Data Officers, individuals who explain exactly what a project is, how it is organized, and how we start with needs and constraints and finish with a new service or product. There are no management courses; students develop management skills through hands-on learning activities including the internship and projects. There are, however, courses in law and economy.

We do not consider that we are in a competitive industry: to offer high-level training in Big Data, an institution must be free of disciplinary constraints, be able to organize a program that brings together mathematics, computer science, business, economics and law, and crucially, to work in close contact with industry.

In France, only a top engineering school like Télécom Paris can offer all of this. Our legitimacy is derived from a long history of research in the field of Big Data. Télécom Paris stands out for the fact that our researchers were already working in close collaboration with industry before the concept of Big Data emerged. As a result, Télécom Paris already had experience and credibility in these fields. Just take a look at the non-exhaustive list of publications by our research professors on the Machine Learning for Big Data Chair website.

Each module is assessed individually; students are awarded a degree if they obtain enough ETCS credits. Various assessment methods are used: continuous assessment, challenges, projects or combinations of these methods. The goal of these assessments is to verify that students understand concepts and can apply them operationally.

Courses take place over a nine-month period, from October to June. The days last from approximately 8.30 am to 5:30 pm. But there is a significant amount individual work as well: we expect each student to spend an equivalent amount of time on individual work. There are also group projects that are spread out over several terms. A period of time during the week is reserved for individual or group work.

This is followed by an internship period (professional thesis) which lasts between 4 months (minimum) and 6 months; starting in June.

It is not possible for students to work while they are completing the Post-Master’s Degree; it is truly a full-time program. Individuals who are not one hundred percent available may instead choose the “Data Scientist” Specialized Studies Certificate.

The aim of the year-long project (projet fil rouge) is to support students’ career goals. This is separate from the internship at the end of the program. It is a group project (4 to 5 students) carried out at the same time as other coursework. The project is spread out over three terms, with a day and a half each week devoted to working on it.

Groups manage the project together but students receive individual grades. These projects are provided and supervised by companies and are based on real problems and real data. The goal is to give students the opportunity to explore all aspects of Big Data: acquisition, storage, analysis, and consider data ownership, in particular in relation to legal issues.

The final internship is customized to the student’s needs and lasts between 4 and 6 months. The objective is to write a professional thesis, which may be completed within the framework of a permanent contract.

In general, they are confidential since they address highly strategic issues for companies. Students have completed professional theses in the consulting industry and in highly technological areas, such as energy, for example, and have worked on setting up a Big Data platform, developing a solution and making it available as web service. There are also theses which focus more on strategic thinking and organization, such as building a recommendation engine to cite one example. There are a wide range of topics.

There are many industrial partners on the improvement committee which manages the organization and knowledge base for the Post-Master’s program. It should also be noted that research at Télécom Paris is funded almost exclusively through contracts. Industrial partners and patrons fund three Research Chairs that support this program: the Machine Learning for Big Data Chair, the Personal Data Values and Policies Chair and the Big Data & Market Insights Chair.

Télécom Paris contributes its expertise to industry by training experts to meet market needs. In return, companies share their vision with students through the Big Data seminars held every Thursday at Télécom Paris. They provide insight into how Big Data affects the organization of their IT management, new markets, and how they try to anticipate these issues.

They may also contribute to courses by presenting technical aspects, such as in the field of cybersecurity and biometrics. They can make their data available for challenges on which students will be graded. Corporate partners propose topics for and oversee year-long projects, in which students are given access to companies’ real data. At the same time, students come face to face with the realities of industry. They also offer a great number of internships in a wide variety of fields.

A Post-Master’s degree certifies a level of training equivalent to six years of higher education. Students may pursue such a degree after completing a Master’s degree. This kind of training program is accredited by the Conférence des Grandes Ecoles. Around a dozen leading companies (Thales, Safran, Airbus, Orange etc.) are members of the improvement committee for the Post-Master’s Degree in Big Data, ensuring that course content continues to meet the needs of businesses.

As far as the program’s reputation goes, the first graduating class has already proven itself with a variety of companies (major international groups and start-ups). Students receive internship and job offers on a weekly basis.

After completing the training program, students receive many different job offers so they have their choice of industries. Each week, companies send us fifteen or twenty offers for internships and jobs, for both fixed-term and permanent contracts. Big Data obviously involves large volumes of data and first became popular in the internet industry, which is still the top industry for this specialization. But today, this expertise has been transferred to all industries: energy, transportation, banking, commerce, security, defense, forensics etc.

In data science, we need to start out with technical expertise and knowledge in order to know what it is possible to do. In general, it is essential for individuals to have a technical background in order to obtain the most strategic management positions. Information and communication technologies are revolutionizing a wide range of industries so it is crucial to start out with technical training in order to work with Big Data.

No, just the opposite; it’s a multidisciplinary field. Beyond the computer science/mathematics aspects, it combines a wide range of skills relating to law, economics, design, data visualization, project management etc. But there is also an important “business” component too. It’s impossible to be a data scientist without taking into account a company’s activity and core business, in order to take effective action and develop projects that deliver results. Data scientists, or chief data officers, must be able to communicate with all parts of a company. They can drive innovation and strategy. It is therefore far from being a “closed-off” field.

Our graduates find jobs very easily. Télécom Paris is in the process of recruiting its third class to take the program. All of the students from the first graduating class who defended their thesis found jobs after completing their internships. They officially graduated in March 2015. Almost all of the industrial internship supervisors for students from this first graduating class were extremely satisfied with the students’ work and offered them a position. Some students even started out with a permanent contract instead of an internship and others started their own company.

A young graduate of this Post-Master’s program can easily earn an annual salary of approximately €45,000 while graduates with professional experience can earn much more. For the first graduating class, the salaries of those who responded to our survey ranged between €50,000 and €80,000 annually.