about me

Hi! I'm Ayao.

Impressed by the power of R and python in the fields of data analysis and visualisation, I created this site to share what I am learning mainly about these two softwares. Inspired by Hadley Wickham,Yihui Xie, the R community and personal sites, I decided to share my learning in the hope of helping others as these resources help me and develop relationships with others. I believe in the importance of sharing knowledge about data and have developed a special interest in data analysis and visualisation.

I have a university education in mathematics and statistics, professional experience as a programmer-statistician at ODS, and I intend to continuously develop my skills in the field of data science.

Feel free to email me at ayao.nomenyo12@gmail.com, check out my or LinkedIn page, view my code on github, or say hi on Twitter.

my education, experience, & other information

Education

🎓 Structured Master’s in Mathematical Sciences (Data analysis)

2019 – 2020

🎓 Msc in Statistics-Probability

2016 – 2018

🎓 BSc in Pure mathematics

2010 – 2015

Experience

Mathematician Statistician

Office Data Science Coporation

Aug 2020 – Dec 2020 Lomé, Togo

Responsibilities include:

Programming
Analysing
Modelling
Mathematical development

Mathematician Statistician

Office Data Science Coporation

Aug 2019 – Sep 2019 Lomé, Togo

Responsibilities include:

Programming
Analysing
Modelling
Mathematical development

Volunteer teacher, mathematics

High school Avédji Elavagnon

Dec 2015 – Jun 2016 Lomé, Togo

Enseignant of mathematics to fill the teacher gap.

The courses taken

African Institute for Mathematical Sciences Sep 2019 – Jun 2020

Data Analysis with R
Data Science
Mathematical Statistics
Statistical inferential
Mathematical and statistical epidemiology of infectious diseases
Statistics Finance
Survival Analysis
Statistical climatology
Professional Development

See certificate

Institut de Mathématiques et de Sciences Physiques Sep 2017 – Dec 2018

Master II:

Theory and practice of surveys
Time series
Monte de carlo method
Data analysis
Statistical learning
stochastic calculus
Computational statistics
Econometrics of qualitative variables
non-parametric statistics

See certificate

Institut de Mathématiques et de Sciences Physiques Sep 2016 – Sep 2017

Master I:

Functional analysis
Algebraic structure
Convex analysis
probability theory
Hilbertian analysis
Mathematical Statistics
Linear Model
Modeling and random phenomenon
Computer Tools and Simulations.

See certificate

seminar

CIMPA Lomé 2018, School of Research

Lifetime Statistics and Spatial Statistics

09 Sep 2018 – 09 Sep 2018 Lomé, Togo

This research school is intended to expose modern statistical methods for processing and analysis of life expectancy. Particular emphasis will be placed on spatial modelling and modelling of extremes for this type of data. The fields of application of these methods are many and varied: medicine, industry, meteorology, hydrology, epidemiology and public health, agronomy, climate change.

reviews

Some online courses

Communicating Business Insights

IBM

Jun 2020 – Present

In this course, we have learned how to visualize the data and have developed stories to communicate the analysis results to stakeholders who have no analytics background. We have learned how to choose the correct methods to present quantitative information that is best suited for the type of data you collected. See badge

Job Success

coursera

Jun 2020 – Present

how to manage social media profile. See badge

Job Success

coursera

Jun 2020 – Present

Python is a programming language used for general software development purposes that was originally created in 1991. Python is widely regarded as one of the best languages for a beginning programmer. Although it’s very easy to learn it is also robust and many platforms that you are familiar with are developed with Python like Google, Reddit, Instagram and many more See badge.

Cluster Analysis

IBM

May 2020 – Present

The Cluster Analysis course provides the foundational knowledge to build and apply clustering models. The first part of the course explains what is clustering and introduce two clustering algorithms K -means and Hierarchical clustering. Then the two algorithms were been studied to segment Retailer X customer base.See badge

Data Science Fundamentals

IBM

May 2020 – Present

This course covers the basics of data science. What is data science, what is data, and what is the role of data scientists. Also, we looked at some uses cases for data science. Then, the concept of machine learning was been explained and the different techniques of machine learning was been described. See badge

Classification

IBM

May 2020 – Present

This course provides foundational knowledge about data science classification techniques as an example of supervised machine learning. See badge