My work involves

===> Teaching Python Programming to students (scientific computing )
===> Organizing and conducting tutorials based on the courses delivered by world-leading lecturers
===> Providing students with coaching and support for the courses on the agenda
===> Coaching students throughout their end-of-the-year research thesis (Taught Master and Co-Op Masters)
===> Empowering students with tips and tricks they need when entering the internships phases in the industry
===> Delivering seminars on use cases solved in Industry to prepare the students for the world of work.
===> Interacting with world-leading experts in Mathematical Sciences among which Nobel Prize Laureates to increase my own capacity in research and teaching of mathematical sciences.

Courses on the Agenda involves:

===> Statistics Problem Solving by Prof. Roger Stern (University of Reading, UK) & Dr. James Musyoka ( Maseno University, Kenya)

===> Computational Mathematics with SAGE by Dr. Daniel Duviol Tcheutia (University of Kassel, Germany)
===> Advanced Data Analytics and Visualization techniques by Dr. Charalampos Chanialidis (University of Glasgow)

===> Scientific Computing by Prof. Georg Bader (BTU Cottbus, Germany)

===> Operations Research by Prof. Paolo Serafini (Università di Udine, Italy)

===> Statistics of Mathematical Population Genetics by Prof. Kristan Schneider (Hochschule Mittweida, Germany)

===> Data Analytics by Dr. Bubacarr Bah (German Research Chair, AIMS South Africa)

===> We use the ELK Stack (ElasticSearch, Logstash, Kibana) as well as Nagios to help companies process real-time analysis of log events coming from many sources of theirs like routers, operating systems, switches, firewalls, internal software, workstations for security purposes.

===> We take advantage of the ELK Stack (ElasticSearch, Logstash, Kibana) on a daily basis to help companies extract insights from their (Big) Data through Log Analysis, especially banks and using Kibana, through visualization, we help them understand the behaviour of their clients for marketing automation or decision-making process.

===> I also invest a good bunch of time writing user stories to better understand the clients, the market, the segmentation of clients as well as develop the attitude of answering the right questions for a full satisfaction of the customer.

Working along with a team of community managers, graphist, statisticians, developers, digital strategists, to improve the customer relationship management (CRM) of companies through Data Science tools like Machine Learning, Big Data, Data Analytics and Business Intelligence

1. Mini MBA courses on 

• Introduction to Business Intelligence
• Business Strategy
•  Fundamentals of Economics
• Organizational Behaviour, Marketing
• Finance and Accounting
• Data analytics in Business for Mathematicians
• MARGA Simulation
• Corporate Strategy.

2. The backbone of the program is the 12-week internship module with participating industry partners, beginning 5 weeks after program start, during which participants will experience a business environment first hand and apply their academic learning. Companies will also undertake a team-based consulting project with a group of interns.

A collection of state-of-the-art courses on

• Big Data Analytics 
• Statistics with R
• Python for Software Development (Web Scraping)
• Applied Graph Theory with NetworkX
• Machine Learning with sci-kit learn
• Natural Language Processing with NLTK
• Text Mining with the built-in Regular Expression Library 
• Operating System Management with the os, sys library.
• Probability Theory and Random systems
• Bayesian Statistics and Linear Algebra.
• An essay project with the topic being "The Significance of 5-sigma and its applications in Particle Physics"

A 2 years cursus with the following completed courses courseworks:

===> Master I

•  Mathematical Techniques
• Tensor Calculations
• Numerical Methods
• Signal Processing
• Introduction to Seismology
• Remote Sensing
• Renewable Energies
• Tropical Meteorology and Climatic Observations
• Practicals and Introduction to Geology
• Geostatistical Methods and Cartography

• Inversion of geophysical data
• Acquisition and collections techniques of data
• Geothermic and radiology
• Magnetic Methods (Geoelectricity and Magnetotelluric)
• Seismic and geology  of the Earth System
• Gravity and Magnetic method
• Introduction to the research setting, the bibliography and the scientific reporting.