Understand the functioning of its organization and its environment to be able to set up the dashboards to follow them and, in an iterative logic, to adjust and adapt, using the best tools computer.

The objective of this MBA is to train true contributors to the control and development of the activity of its organization.

Training Objectives - Year 1

From decision to action, how to prepare your organization to react. The dashboard is the tip of the iceberg. Is the company ready to react? When a fire alarm goes off, everyone knows what to do. How many organizations know how to react to the evolution of a key indicator.

  • Build a dashboard. Learn to define and build an indicator. Make sure that what you measure makes it possible to monitor and control the activity.
  • Build an expanded dashboard. Have an overview of the data chain: internal to the company, external with those of the suppliers or those contextual and environment.
  • Understand management issues: from the analysis of the cost structure to the analysis of the revenue structure. Analyze the past to better control the future.
  • How to master an environment that is always evolving faster and more complex; interconnected. Learn to go from a "classic" accounting to a multidimensional or event accounting.
  • Master the office tools (not exhaustive): Word, PowerPoint, Excel, Access. Comparison with office suites.
  • Prepare the M2 with initiations to programming: Macro-commands, SQL, Python, R, Javascript, HTML / CSS, XML

Training Objectives - Year 2

  • Understand how the chain of processing data is decomposed, from creation through the action of an individual or an object to its exploitation using high frequency and volume resources.
  • Understand how to exploit data . Small to gain control ... Until Big with a neural network.
  • How to make Machine Learning and make good use of Artificial Intelligence , cognitive services and other algorithms.
  • Understand strategic issues Where and how to store and process your data in a context of increased competitive pressure.
  • Starting from the office tools that will have to be mastered, the teaching will gradually increase in power with "server" and collaborative tools .
  • Acquire a master of office tools (non-exhaustive): Excel, Access, MS SQL, Pivot Power, Power BI, Tableau, Hadoop, Jira, Monday, Dataiku, Cuda, SQL, Python, R, Javascript, HTML / CSS


Anée 1


  • Strategy
  • Accounting
  • Management control
  • Audit
  • Financial analysis
  • Reporting: from the scoreboard to the BSC (Balanced Score Card)
  • Theory of organizations
  • Decision making
  • Paradigm of sustainability

Scientific and Technical

  • Mathematics: Descriptive Statistics
  • Analysis of data from a survey conducted by students (segmentation, typology, etc.)
  • Office Tools: Word Processing, Presentation, Spreadsheet, Database.
  • Collaborative tools
  • Web Initiation (HTML, CSS
  • XML
  • Predictive simulation tool with solver, chart for extended decision-making with partners (suppliers, subcontractors, etc.)

Acquired skills

Use the basic features of office tools

  • Word Processing with Styles, Templates and Plan Mode
  • Presentation tool
  • Spreadsheet
  • Database.

Use advanced features of office tools

  • Consolidation, abacus and solver. Simulation tool
  • Make a multidisciplinary dashboard
  • Master the tools
  • Fabricate tools for statistical processing
  • Make a pivot table
  • Conduct an online survey of questionnaire design to the exploitation of collected data
  • Learn to juggle between general accounting and management control

Anée 2

Semester 1

data processing

  • Business intelligence from small to big
  • IOT
  • Programming html css Javascript SQL
  • Python Statistical Language


  • English: digital practice / oral
  • Valuable creation
  • crowdfunding
  • Digital and collaborative economy -INNO and breaks
  • EnVie: the challenges of the world # permaconnected
  • Written / oral expression
  • Financing the digital economy
  • Integration


  • Descriptive and inferential statistics

Semester 2

data processing

  • Hadoop
  • Statistical language
  • Modeling (UML / BPML)
  • Javascript following Semester 1
  • Machine Learning
  • Project management


  • Data law
  • Economic Intelligence Review
  • Economics of information Technology (Olivier Williamson Shapiro Varian Volle)
  • Public speaking
  • CSR
  • IT security
  • Business English


  • Descriptive and inferential statistics

Graduation thesis

  • Choice of a subject related to your professional project

Methodology and pedagogical approach

The pedagogical approach is based on four fundamental pillars: learning, understanding, mastering and evolving.

  • Learn the basics to ensure a good understanding.
  • Understand to have control.
  • Master to be able to evolve.

The process combines the acquisition of technical knowledge, management and business to be on the border of worlds that sometimes have trouble understanding each other.

At the level of tools the approach is agnostic. It does not focus on one, but seeks to highlight what is common to all tools and rather highlights the advantages and disadvantages of each.

This methodology applied in both M1 and M2 will allow you to successfully meet the challenges of Big (and small) Data of any type of organization

Off Course Activities: Individual Work


  • Specific evaluations according to the courses
  • Cross-sectional practical cases
  • Serious gaming
  • hackathon


Training offers opportunities in several key sectors that are considered to be cross-functional within a company. More specifically, it aims to enable future graduates to translate functional and business needs into technical components and to propose in advance components that can create value and conquer new markets. The goal is to automate data collection, analyze and structure data and extract useful information in real time and at the right time and transform it into added value.

At ease with the statistics, the databases, the future graduate will be able to immediately visualize the elements to be exploited to be more in phase with the needs of the company 2.0, will be able to pilot and carry out complex projects in Big Data environments, bringing together experts from different disciplines.

  • Responsible for expert studies of the Chief Data Officer type, at the heart of the new digital economy, to address the issues of big data-related businesses: marketing, actuarial science and finance.
  • A wide range of professions in charge of studies, both in private companies (SMEs and large companies) and in public companies,
  • Multiple activity sectors: banking, insurance, studies and consulting, telecommunications, distribution, automobile, etc.


Admission Requirements - Year 1


  • Post Bac 3 in business school, IEP, IAE, engineering schools and digital courses
  • Validated experience: BAC 2 6-9 years

Admission interview file

Admission Requirements - Year 2


  • Post Bac 4 in business school, IEP, IAE, engineering schools and digital courses
  • Validated Experience - Process VAPP

Admission interview file

Program taught in:
  • French

See 11 more programs offered by ESLSCA Business School »

Last updated March 1, 2019
This course is Campus based
Start Date
2 years
12,000 EUR
per year
By locations
By date
Start Date
End Date
Application deadline
Application deadline
End Date