Program content of the MSc in Data Science

The aim of this program is to fill in the gap between business and engineer higher education in AI, by providing leaders with an action-oriented approach to master fundamental technological practices, together with a concrete understanding of human and business impacts. The program combines experiential and academic activities to learn how to think, to design and to deploy AI applications in line with a responsible data governance, business strategy and transparent practices.   In response to the extensive recruitment needs of the continuously growing AI-driven strategies and practices, this program gives you the opportunity to acquire key competencies by understanding, experiencing, and doing AI.

Discover the program content for the academic year 2022-2023.


Welcome to emlyon's MSc in Data Science

Amy Ding holds a PhD in Cognitive Science and Information Technology from Carnegie Mellon University, USA. Her research areas focus on Responsible Artificial Intelligence, Smart healthcare, Bias and Discrimination in AI, Digital Ecosystem, PhD Quantitative Research Methods, Management Information Systems, Digital Transformation. She is a full-time Professor of Artificial Intelligence and Business Analytics, emlyon business school, France, and Visiting professor at Asia Europe Business School, China. She conducts forefront theoretical and empirical research on responsible artificial intelligence (AI), human and machine cognition, and their applications in business analytics, healthcare, digital marketing, and digital innovation.  
Also she received outstanding contribution award for undergraduate/graduate program, emlyon business school, 2021.

Year 1

Python and machine learning

Python is one of the most popular cross-platform programming languages for its simplicity, readability and extensibility. Because of these characteristics, Python language is widely used in practical applications, such as data analysis, data visualization, providing basic analysis for business intelligence.
This course discusses the theoretical knowledge based on python, the basic elements of programming and how to construct basic programs. Meantime, it will introduce the basic problems and algorithms of machine learning, and adopt python programming language and machine learning algorithms to solve practical application problems. The course requires students to improve their knowledge, skills and application ability

Open source and cloud computing

As a type of distributed computing, cloud computing refers to the computing model of breaking large computing tasks into many smaller pieces, using many server systems. Various business models have emerged based on the cloud computing model, including SaaS, PaaS, etc. Open source software ecosystem has laid a solid foundation for the development of cloud computing. Therefore, it is important to understand the strategic implications of open source cloud computing for enterprises, in the era of expedited development IT infrastructures. This course first introduces historical development and evolution of open source software ecosystem, then discusses the foundational knowledge about open source cloud computing, including the business models, implementation, key technologies, operations, and applications of cloud computing.

Introduction to data science

Combined with the rapid development of information technology, this course content is rich in information, and the four technologies involved are highly logical. They are promoted layer by layer to reshape the classroom teaching content.
Based on the school’s "big data and artificial intelligence training platform", innovative and diversified teaching methods have strengthened classroom design. In addition to theoretical teaching, teaching methods such as group discussion, classroom interview and data analysis project practice have also been proposed to enhance classroom interaction. The practice of data analysis project and the construction of "recommended reading literature library" help to expand the depth of classroom knowledge, effectively improve the challenges of classroom learning, radiate the vitality of classroom, and give full play to the role of the main position, main channel and main battlefield of classroom teaching.

Computational Linguistics

This course is mainly for graduate students interested in Computational Linguistics / natural language processing. It mainly introduces the basic knowledge of computational linguistics, including common machine learning models, such as Bayesian classifier, decision tree, k-nearest neighbor, support vector machine SVM, logical regression, maximum entropy, hidden Markov model HMM CRFs, artificial neural networks and deep learning. At the same time, some common basic NLP tasks are also introduced, such as word segmentation, part of speech tagging, syntactic analysis, and applied NLP tasks, such as language generation, sentiment analysis, information retrieval, information extraction, etc.

Algorithms for data science

Data science has become a basic tool to extract value from data. Any enterprise can take data collection, storage and processing as a part of its business. The huge multi-source heterogeneous data flow has also become an important mysterious asset to be deeply excavated because of its rich information. Based on the basic models and methods of business intelligence and data analysis introduced in the course of “Introduction to Data Science” (i.e. the "Meta-method" of Data Science), this course will deeply explore the practical application scenarios and cases of data science methods and models in different fields (i.e. the "combination algorithm" of data science, such as recommendation system, sentiment analysis, search engine principle, fraud detection, customer segmentation, relationship marketing, etc.). By using Rapidminer, Python and other platforms and tools, we can enable students to strengthen their ability to practice and solve data science application problems while learning and mastering theoretical methods.

Digital Transformation and Project Management

Digital Transformation refers to how organizations leverage digital technology and digitalized workflow, to significantly change their routines, processes, and business models, for the ultimate goal of improving their performance. In the new era of digital infrastructure construction, digital transformation has major strategic implications for business firms and organizations alike. This course introduces the concept, patterns and key factors in digital transformation, as a special form of organizational transformation. Further, this course discusses the benefits and challenges associated with digital transformation, as well as the corresponding requirements on organizational culture, talents, structure and technology infrastructure, etc.

Web analystics

This course mainly introduces two parts of network analysis, that is, web content analysis and link analysis. By utilizing relevant practical cases in business and software tools such as Gephi as well as programming language Python, this course aims to explain and discuss the basic concepts in network analysis, core measurements, classical algorithms, and applications scenarios, including:

  • web content analysis methods, such as vector space model, latent semantic indexing, latent dirichlet allocation;
  • link analysis methods, such as nodes measurements, network measurements, algorithms in information retrieval (PageRank, HITS), community detection algorithms, and bipartite graph mining;
  • using software Gephi and programming language Python to conduct network analysis.

Machine learning advanced models

Machine learning is an area of artificial intelligence, data science and computer languages which covers supervised learning and unsupervised learning. The subjects becomes increasing important in various industries including Finance.

In this course, we will cover basic machine learning models such as linear regression, logistic regression, decision tree models etc. We will also discuss advanced models such as support vector machines, neural networks and various ensemble models.

Responsible AI

Artificial Intelligence (AI) is changing the way we live, work and do business, however, as AI technologies become more pervasive and responsible for an increasing number of decisions (e.g., benefit payments, mortgage approvals, and medical diagnosis), they may amplify existing human biases, create new biases, and become less transparent. Organizations thus face ethical as well as legal and regulatory risks if they use a “black box” approach to AI. This course introduces students to knowledge of techniques, architectures, regulations and AI principles used to achieve responsible artificial intelligence. The course helps students understand key challenges of addressing AI-related digital bias, discrimination, privacy, security, explanation, and trust issues in general content, and discusses how key responsible components land at both development and deployment levels.

Natural language and machine translation

Based on the basic knowledge and methods of natural language processing and understanding in the course of “Computational Linguistics”, this course will focus on the problem of "machine translation" in intelligent natural language processing. The course will introduce the main problems in the development of machine translation system, the development process of machine translation and the latest cutting-edge progress of machine translation in recent years, focusing on the mainstream methods in the field of machine translation: rule-based method, example-based method, popular statistical method, segmentation-based method and relatively cutting-edge deep learning machine translation. In addition, this course will also discuss the evaluation of machine translation, as well as the main participants and commercialization status in this field.

Applied deep learning in industry

Deep learning is an important development of machine Learning. In this course, we will cover machine learning models such as logistic regression, Neural Networks, convolution Neural Networks, Recurrent Neural Networks and Graph Neural Networks.

Throughout the lectures, the students will be required to implement all the models using Python languages. With the tool of Python and machine learning models we will show how to use these tools to solve real industry problems.

Year 2

Business model innovation and innovative design

The objective of this course is for students to get a solid 360° understanding of “what is a business”, and “how to innovate a business by focusing on the customer’s and company’s value proposition” via a simple canvas of business model.

Through this course, we will:

  • Introduce innovative business models in China and around the world (by presenting and discussing different cases)
  • Students are requested to work on some projects, and to conduct the market survey before participating this course, and thus, students will
  • Understand what the value proposition of the company is;
  • How to detect the needs (and pain) of customer
  • What company can offer as product/service, so as to differentiate with the competitors

Digital marketing and personalization for brand management

Digital marketing is an expansive area. Advances in information and big data technology have provided brands with abundant opportunities for a more customized approach to reaching potential and existing customers. Consumers’ experience with the brand in a virtual context, as well the experience in offline setting, is an integral part of the holistic brand experience, which determines brand equity.

Digital channels and prevalence of social media have created more opportunities to understand and analyze customer requirements and preference and engage customers to co-create values of their brand. The crisis of COVID-19 has led to significant changes in consumers’ information searching, brand learning, brand buying, and brand consumption behavior. There are high demands for more convenient and no human contact service and integrated e-commerce platforms that can perform numerous marketing functions like provision of brand information and service, taking orders, sharing of experiences, and arrangements for delivery and return orders. How to use digital marketing strategy to support the positioning and identity of the brand and delivery of the promised values to target customer and how to apply digital marketing tools to create brand community and enrich and enhance brand experience are hot agendas to brand owners.

Cognitive neurosciences

Cognitive neurosciences is an important course that students in management specialty need to know. Students should master the basic theories, models, equipment, tools and methods, and the work principle of nervous system through this course. They should know how to design simple experiment based on the basic principles of brain and cognitive science; know the current research trends and latest research results, and to obtain the awareness to track the latest information of this field.

Introduction to brain-computer interface

Computing changes life and technology empowers the future. In this digital age, the creation of new business model and management innovation depend on our human intelligence. The study on human brain relies on the development of new instruments and measuring technologies used to capture our brain activities. Brain-computer interface (BCI) technology is a new research field, involving computer science, neuroscience, cognitive science, biomedical engineering, mathematics, signal processing, clinical medicine, automatic control and other fields. This course helps students acquire and understand current BCI technologies. Using a series of research cases and their applications in management science, medical rehabilitation, artificial intelligence and autonomous vehicle, we will introduce the current BCI methods, their related measurement instruments and technologies, adaptive methods, as well as the challenges faced by implementing BCI.

Quantitative Finance: Block Chain Management

This course introduces the application of blockchain technologies in the world of money and finance. We will start by reviewing Bitcoin – the world’s first blockchain application, with its design, cryptocurrency usage, and critiques. We will then gradually introduce the commercial, technical, and public policy fundamentals of blockchain technologies, distributed ledgers and smart contracts. Cases in various financial sectors will be discussed and critiqued, including but not limited to payment systems, money and central banking, trading, etc.

Professional Thesis

The Professional Thesis is an analytical work on a topic related to the industry and to the professional experience gained during your internship. This is a great opportunity for you to research a subject which is not only of particular interest, but which has the potential to contribute powerfully to your personal and professional development.


emlyon business school will support you by providing you with tools and helping you find the right internship. This is a great opportunity to put your academic knowledge to the test, and acquire tangible experience within the industry.

China's Economy and Politics: History and Current context

The course grasps the two key words of "theory" and "practice", highlights the "research-based" teaching process and ability training.

The course content covers 13 teaching topics with both internal relations and special theoretical value and academic functions. They are:

  • Socialism with Chinese characteristics is the theme of all the party's theories and practices since the reform and opening up;
  • China’s economy opening up and reform since the 1970s, its accession to the WTO;
  • The transformation of major social contradictions and the entry of socialism with Chinese characteristics into a new era;
  • The general task and strategic arrangement of the development of socialism with Chinese characteristics in the new era;
  • Building a modern economic system to boost high-quality economic development;
  • The political development path of socialism with Chinese characteristics;
  • Cultural self-confidence and socialist core values;
  • People's livelihood security and social governance modernization;
  • The value following and system guarantee of the construction of socialist ecological civilization with Chinese characteristics;
  • The external environment and international strategy of the construction of socialism with Chinese characteristics in the new era;
  • One belt, One road, and the construction of the community of human destiny;
  • China and the world, geopolitical context in the 21st century;
  • China’s business environment, inward and outward foreign direct investment.