Teaching Experience
Tutorial Assistant, Department of Statistics, University of Ibadan
UNDERGRADUATE COURSES [2017 - 2021]
COURSES
STA111/114 - Descriptive Statistics/ General Statistics |Data types, collection, source, quality and measurement scale| Data classes, and presentation & Introduction to R programming| Measures of central tendency|Measures of dispersion/variability| Measures of shapes| Estimating with confidence| Hypothesis testing| Exploring bivariate analysis ~ Regression and Correlation|
STA115 - Probability Theory I |Counting techniques - Sampling with & Without replacement | Combinatorial | Introduction to R programming | Axiomatic probability | Independent & Conditional probability | The law of total probability| Bayes rule | Random variables & Moments | Chebyshev's inequality | Discrete & Continuous univariate distributions | Joint distributions | Sampling distributions I, II & III |
STA 121 & 221 -Â Statistical Inference I & II | Introduction to sampling distributions | Sampling distribution of sample statistic | Sampling distribution of the difference between two sample means | Population parameters and estimations | Sampling distribution of a sample & population proportion| Point & interval estimations | Large sample confidence interval for a single population mean & proportion | Large sample confidence interval for the difference of two population means & proportions | Hypothesis testing | Confidence interval & Hypothesis test ~ Small sample | Chi-square and F tests | Analysis of variance (ANOVA) test.
POSTGRADUATE COURSES [2018 to date]
Under the supervision of the course Professor (O. E. Olubusoye)
COURSES
STA 772 - Probabilistic Graphical Model (PGM) |Components of PGM | Marginalizations - Factor product, reduction & marginalization | Properties of independent variables |Vectorization |Conditional independence | Bayes Net & Naive Bayes | d-separations | JTA - Junction Tree Algorithm | Etc.
STA 779 - Advance Statistical Data Mining (SDM) |Introduction to CRISP-DM | Statistical learning & algorithms | Supervised & Unsupervised learning | Basic data manipulation & preparation | Cross-Validation | Outlier detection & analysis | Pattern mining | Text analysis| Etc.
Virtual Teaching Assistant (2021), Neuromatch Academy, United States of America (USA)
COURSE
Computational Neuroscience
Volunteer Graduate Assistant, Centre for Petroleum, Energy, Economics and Law (CPEEL), University of Ibadan [2016 - 2019]
Under the supervision of the course Professor (O. E. Olubusoye)
COURSE
CEE 711 - Applied Econometrics |Introduction to basic concepts of econometrics |Methodology of econometrics |Single equation regression models | Two variable regression analysis - basic idea & problem of estimation, Interval estimation and hypothesis testing | CNLRM| Multiple regression analysis - the problem of estimation & inference | Dummy variable regression models | Multicollinearity | Heteroscedasticity | Autocorrelation| Econometric modelling - Model specification & Diagnostics testing | Etc.