• Module Titles                                                                                                  Period (tentative)                                        Nber of Days   No Page

    Statistical methods 18–20/January 2021 3 24
    Introduction to R 15–17/February 2021 3 25
    Introduction to Python 21–22/January 2021 2 26
    Introduction to numerical optimisation 24–25/June 2021 2 27
    Introduction to forecasting 18–19/February 2021 2 28
    Modelling 22–24/March 2021 3 29
    Multidimensional Analysis of Data 19–21/April 2021 3 30
    Classification & Discriminatory Analysis 22–23/April 2021 2 31
    Data visualization 22–24/February 2021 3 32
    SGBD and Big Data :  SQL and NoSQL 25–27/January 2021 3 33
    SQL and NoSQL with R/Python 28–29/January 2021 2 34
    Analysis of Stationary time series 25–26/March 2021 2 35
    Modelling II 17–19/May 2021 3 36
    Treatment of outliers and missing data 20–21/May 2021 2 37
    Survey practice with R 19–20/July 2021 3 38
    CAPI with CSPRO 21–23/June 2021 3 39
    R Package – R Shiny 26–28/June 2021 3 40
    Web-Scraping 26–28/April 2021 3 41
    Machine Learning 29–31/April 2021 3 42
    Text & Graph Mining 29–30/April 2021 2 43
    Introduction to Linux 28–30/June 2021 3 44

     

    Implementation of a Data Warehouse 26–28/July 2021 3 45
    Introduction to Git 25–26/February 2021 2 46
    Introduction to Deep Learning 29–31/July 2021 3 47
    Introduction to Docker 23–25/August 2021 3 48
    Cluster under Linux 26–28/August 2021 3 49
    Cloud Computing 23–24/September 2021 2 50
    Enriching an R Package 20–22/September 2021 3 51
    Introduction to Hadoop 22–24/November 2021 3 52
    Introduction to Spark 25–26/November 2021 2 53
    Use of R in « Big Data » 25–27/October 2021 3 54
    Use of Python in Big data 28–29/October 2021 2 55
    Introduction to Kubernet 06–07/December 2021 2 56
    Introduction to Kafka 08–09/December 2021 2 57
    Introduction to Nifi 10–11/December 2021 2 58
This training is mainly intended for professionals who wish to acquire skills in the field of Data Science. The target audience consists of statisticians, data analysts, IT/development project managers. At the end of the training, the auditors will be able to :

  • Understand the methods of data science ;
  • Work with data in a variety of formats (structured and unstructured)
  • Analysing massive data using algorithms and statistical methods

Each training module can be chosen independently of the others. The courses take place at the Ecole Nationale Supérieure de Statistique et d’Economie Appliquée in Abidjan.

 

Period/Deadline for registration :

  • Big Data Certificate : From 15 October to 13 January 2021
  • Specific modules : 15 days before the start of each module

NB: The proposed dates are provisional and subject to change.

 

  • Head of Training (FC-BD): Nathaniel GBENRO, Lecturer-Researcher, ENSEA; Contacts : (+225) 22 48 32 47 – nathaniel.gbenro@ensea.ed.ci
  • Executive Assistant: Honorine AMAN; Contacts : (+225) 22 48 32 32 / 22 48 32 11 – ensea@ensea.ed.ci

 

You are a company and you want to register several of your employees, please get in touch with the Continuing Training Department by:

  • mail: Ensea Formation Continue / Avenue des Grandes Ecoles / Cocody-Abidjan
  • E-mail: ensea@ensea.ed.ci
  • phone : 225 22 44 08 42

DATA DAY : Data at the Heart of Business Transformation

The Data Day, organized on April 25, 2019 at ENSEA Abidjan was a day of promotion of data sciences (Data Science – Big Data) to companies and the general public in order to better promote the chair “International Data Science Institute – CÔTE-D’IVOIRE” (IDSI-CI) resulting from the cooperation between INP-HB of Yamoussoukro, ENSEA of Abidjan and the Ecole Polytechnique de Paris. It was also a forum for exchange with the professional world. The Data Day was intended to be a platform for discussion between students and data professionals, as well as an opportunity to showcase the expertise of the students of this institute.