Society has entered a new digital era where creation, consumption and use of digital content have changed our lives. Proliferation of sensors, the Internet of Things, and digital services do continuously produce highly heterogeneous, highly unstructured and high dimensional data hardly interpretable from human intelligence. Reasoning and mining on this data can be a novel way rethinking out of the box/addressing unsolved old problems or a wealth creation engine through the introduction of novel practices, services and policies. The future of business intelligence therefore relies on mastering jointly data mining and business analytics techniques which become the pillars of such an interdisciplinary effort.
Intelligent usage of data is the root of today’s business decisions and the driving force of the societal and economical evolution of the years to come. It is probably one of the most important topics of our days. Analysts estimate that data-related businesses generated about 10 billon dollars in 2012, and will probably generate more than 30 billion dollars in 2017. In terms of employment, companies and organizations will need around a couple of hundred thousand data scientists and business leaders for the year to come.
Furthermore, the exponential growth as it concerns content generation would certainly create a tremendous need for highly qualifi ed individuals with an in depth knowledge and global understanding of the technological and business challenges behind such digital evolution era. Recent business studies converge to an estimated need of educating several millions data scientists and leaders within the decade to come. This is why Centrale Paris and ESSEC Business School, one of the best engineering and business schools in the world have partnered to propose a very innovative and complete program.
At the end of the program, the students will have been developing their skills to become Data Scientists or Business Analytics Experts. From San Francisco to Tel Aviv, places of IT offer their best jobs for data scientists. Data science impacts a wide range of spheres from health care industry to business to engineering companies. International companies like Google, Microsoft or Amazon and startups like Snapchat Waze or Uber are hiring data scientists.
In contrast, business analytics experts focus more on the strategic aspects of the business and may also undertake careers in consulting firms, the financial sector, the pharmaceutical industry, telecommunications, retailing and fast moving consumer goods companies and in web analytics or digital companies as well. In any case, this program prepares students to meet the growing demand in every industry for data-driven leadership or to engage entrepreneurship
T1: COMPULSORY COURSES
Foundations of Big Data Analytics (6 ECTS):
This course deals with statistical methods for the analysis of multidimensional data. It aims at developing analytical problem-solving skills while presenting quantitative methods apt to support decision-making processes in the face of uncertainty. It also provides with actionnable tools to analyse and leverage data (PCA, Regressions, decision trees, clustering, data and text mining).
Foundations of Signal processing / sparse coding / optimization (6ECTS):
This class will introduce the mathematical concepts and techniques to achieve a solid understanding of the fundamental principles of signal processing, sparse coding and optimization. We will focus on providing a short overview of the most recent methods both in terms of representations (sparsity/compressed sensing and coding) as well as in terms of representations and inference including continuous and discrete optimization.
Foundations of Machine learning (6ECTS):
This course gives an overview of the most important trends in machine learning, with a particular focus on statistical risk and its minimization with respect to a prediction function. A substantial lab section involves group projects on data science competitions and gives students the ability to apply the course theory to real-world problems.
Foundations of Strategic Business Analytics (6 ECTS):
This course aims at helping students use quantitative techniques in a strategic consulting approach in order for them to be able to design, assess and manage business strategies. It is based on real life cases and will give you prerequisites on how to build a strategy and how to present it to different type of audiences and stakeholders.
Introduction to neural information processing (4ECTS):
This course provides a state of the art overview of computational systems towards data understanding. It covers a range of techniques including statistical learning theory, information theory, graphical models, and non-linear and discrete optimization, as well as their application to important prediction problems facing science and industry.
Introduction to distributed optimization and computing (4 ECTS):
The objective of this course is to introduce the theoretical background which makes it possible to develop efficient algorithms to successfully address these problems by taking advantage of modern multicore or distributed computing architectures. This course will be mainly focused on nonlinear optimization tools for dealing with convex problems.
Introduction to Deep Learning (4ECTS):
The advent of big data and powerful computers have made deep learning algorithms the current method of choice for a host of machine learning problems. This course will discuss the motivations and principles regarding learning algorithms for deep architectures, starting from the unsupervised learning of single-layer models such as Restricted Boltzmann Machines, and moving on to learning deeper models such as Deep Belief Networks.
Introduction to Geometric data analysis (4ECTS):
Data analysis is the process of cleaning, transforming, modeling or comparing data, in order to infer useful information and gain insights into complex phenomena. This course reviews fundamental constructions related to the manipulation of such phenomena, mixing ideas from computational geometry and topology, statistics, and machine learning.
T3: Excellence Courses
Excellence in Massive data Processing (4ECTS):
Analysis of massive data sets that are generated in many scientific and business intelligence applications. Given the scale and speed of data that needs to be processed, Big Data challenges the state of the art machine learning algorithms and for this reason there is on ongoing effort to design learning algorithms accommodating a parallel/distributed or even a streaming evaluation.
Excellence in Business Intelligence (4ECTS):
This course refers to the technologies and processes for collecting, blending, modeling, analyzing and visualizing data in order to gain insights and make better decisions. It will present information systems and technologies pertaining to decision making, as well as BI-specific modeling techniques. The ultimate aim of the course is to arm students with the knowledge and skills pertaining to the use of IT for decision making.
Excellence in High Performance and Parallel Computing (4ECTS):
This course will present the most modern and efficient tools in terms of distributed and parallel computing. The content will refer both to the underlying theoretical foundations distributed programming as well as to the practical use of existing distributed programming architectures able to accommodate asynchronous flow of high dimensional/heterogeneous data.
Excellence in Supply Chain Analytics & Tools (4ECTS):
This course has two main objectives: (1) To familiarize students with the core planning and optimization tasks of a supply chain manager, and (2) To introduce course participants to use supply chain applications to solve the most prominent supply chain problems. Course participants study and apply the latest methods that are used to solve supply-chain related tasks.