Skill Development in Statistics and Data Science

Data science is booming and shaping future careers. Job opportunities for data analysts/scientists are growing rapidly as companies are generating massive amounts of data and seek to extract valuable insights from them. In this 21st century, aptitude and skills in statistics and machine learning will drive employment for lucrative jobs. This course provides an opportunity for you to acquire the essential skills and become a proficient R Data Analyst/Scientist.

U.I Consultancy Services Unit in Collaboration with University of Ibadan Laboratory for Interdisciplinary Statistical Analysis (UI-LISA) gladly welcomes Students (Undergraduate and Postgraduates), Data Analysts, Researchers, Data Scientists and anyone who is passionate about quantitative analysis to register for the following modules:

1. Introduction to R and RStudio 2. Data Manipulation (filtering, sorting, recoding and computing new variables) 3. Descriptive Statistics 4. Creating Frequency Tables and Cross Tables 5. Building Charts 6. Checking Assumptions and Detecting Outliers 7. Performing Univariate Analyses (t-test, Binomial test and Chi-square test)

Participation Fee:

In-person - N25,000 per module at UI Consultancy Services Unit, 7, Elkanemi Road, University of Ibadan.
Interactive virtual class - N15,000 per module using Microsoft Teams.
The fee includes the Certificate of Participation and course materials.

Duration and Dates:

In-person - 2 days,
January, May and September every 3rd Thursdays' and Fridays' from 9:00am - 3:00pm daily.
Interactive virtual class - 12 hours (Based on the agreement with the prospective participant),
from 3:00pm - 5:00pm daily

Pre-requisites:

No previous knowledge or background is required except sustained interest and willingness to learn

Expected Learning Outcomes:
At the end of the course, participants will be able to:

  • Manipulate data in R (filter and sort data sets, recode and compute variables)
  • Compute statistical indicators (mean, median, mode etc
  • Determine skewness and kurtosis
  • Get statistical indicators by subgroups of the population
  • Build frequency tables
  • Build cross-tables
  • Create histograms and cumulative frequency charts
  • Build column charts, man plot charts and scatterplot charts
  • Build boxplot diagrams
  • check normality assumption for a data series
  • Determine the outliers in a data series
  • Perfom univariate analyses (one-sample t test, binomial test, chi-squaretest for goodness-of-fit

If interested in the above module, please Register via this link

1. Introduction and preliminary review 2. Test of Association (Pearson, Spearman, Partial, and Chi-square test of Independence) 3. Mean Difference Tests - Part I (Independent, Paired, One-Way ANOVA, Two-Way ANOVA, Three-Way ANOVA, One-Way MANOVA, Mann-Whitney, Kruskal-Wallis tests) 4. Predictive Techniques (Multiple Linear Regression - Basics, Testing Assumptions, with Dummy Variable & Sequential regression) 5. Reliability Analysis (Cronbach’s Alpha, Cohen’s Kappa, & Kendall’s W)

Participation Fee:

In-person - N40,000 per module at UI Consultancy Services Unit,7, Elkanemi Road, University of Ibadan.
Interactive virtual class - N20,000 per module using Microsoft Teams

Duration and Dates:

In-person - 2 days:
February, June and October, every 3rd Thursdays' and Fridays'.
Interactive virtual class - 12 hours (Based on agreement with the prospective participant)

Pre-requisites:

Familiarity with R and R Studio environments
Participation in Module 1 - Basic Data Analysis (Beginner' Level)

Expected Learning Outcomes:
At the end of the course, participants will be able to:

  • Run parametric and non-parametric correlation (Pearson, Spearan, Kendall)
  • Perform partial correlation
  • Run the chi-square test for association
  • Run the independent sample t test
  • Run the paired sample t test
  • Execute the one-way analysis of variance
  • Perform the two-way and three-way analysis of variance
  • Run non-parametric tests for mean difference
    (Mann-Whitney, Kruskal-Wallis, Wilcoxon)
  • Execute the multiple linear regression
  • Compute the Cronbach's alpha
  • Compute other reliability indicators (Cohen's Kappa, Kendall's W)

If interested in the above module, please Register via this link

1. Introduction and preliminary review 2. Mean Difference Tests - Part II (ANCOVA, Within-Subject ANOVA, Mixed-ANOVA, Friedman test) 3. Predictive Techniques (Binomial, Multinomial & Ordinal Regressions ) 4. Grouping Methods (Multidimensional Scaling, Factor Analysis, Cluster Analysis & Discriminant Analysis)

Participation Cost:

In-person - N80,000 at UI Consultancy Services Unit, 7, Elkanemi Road, University of Ibadan.
Interactive virtual class - N40,000 using Microsoft Teams

Duration and Dates:

In-person - 2 days:
March, July and November every 3rd Thursdays' and Fridays'
Interactive virtual class - 18 hours (Based on agreement with the prospective participants)

Pre-requisites:

Familiarity with R and R Studio environments
Participation in Module 2 - Intermediate Data Analysis
Knowledge of advanced statistics will be an advantage

Expected Learning Outcomes:
At the end of the course, participants will be able to:

  • Perform the analysis of covariance
  • Run the one-way within-subjects analysis of variance
  • Run the two-way within-subjects analysis of variance
  • Run the mixed analysis of variance
  • Perform the non-parametric Friedman test
  • Execute the binomial logistic regression
  • Run the multinomial logistic regression
  • Perform the ordinal logistic regression
  • Perfom the multidimensional scaling
  • Perform the Principal component analysis and the factor analysis
  • Run the simple and multiple correspondence analysis
  • Run the cluster analysis (k-means and hierarchical
  • Run the simple and multiple discriminant analysis

If interested in the above module, please Register via this link

1. Introduction, Concepts and Key Issues in Machine Learning 2. Cross-Validation Techniques 3. OLS Regression & Validation 4. Best Subset Regression (Best Subset Selection, Backward Selection, Forward Selection & Validation) 5. Penalized Regression (Ridge, Lasso & Validation) 6. Partial Least Squares (PLS) Regression & Validation 7. Logistic Regression (Prediction Accuracy, ROC Curve, Lasso Logistic Reg. & Validation) 8. Discriminant Analysis (Linear, Quadratic & Validation) 9. Naive Bayes Estimation 10. K-Nearest Neighbour (KNN) 11. Support Vector Machine (SVM) 12. Decision Trees and Random Forests 13. Neural Networks 14. Principal Component Analysis (PCA) 15. Cluster Analysis (Hierarchical & K-Means)

Participation Cost:

In-person - N150,000 at UI Consultancy Services Unit, 7, Elkanemi Road, University of Ibadan.
Interactive virtual class - N80,000 using Microsoft Teams

Duration and Dates:

In-person - 5 days;
April, August and December from 3rd Tuesdays' to Fridays'
Interactive virtual class - 30 hours (Based on agreement with the prospective participant)

Pre-requisites:

Familiarity with R and R Studio environments
Participation in Module 3 - Advanced Data Analysis
Knowledge of advanced statistics will be an advantage

Expected Learning Outcomes:
At the end of the course, participants will be able to:

  • Understand the essential concepts related to machine learning
  • Perform model cross-validation to assess model stability on independent datasets
  • Execute advanced regression analysis techniques:
    Best subset selection regression, penalised regression, PLS regression
  • Perform logistic regression and discriminant analysis
  • Apply complex classification techniques:
    Naive Bayes, K nearest neighbor, support vector machine, decision trees
  • Use neural networks to make predictions
  • Use principal components analysis to detect patterns in variables
  • Conduct cluster analysis to group observation into homogeneous classes

If interested in the above module, please Register via this link

Payment Details

All payments should be made directly to:

Account Name: U.I. Consultancy Services Unit
Name of Bank: First Bank Plc
Account Number: 2005514755
Sort Code: 011191238
Tin No: 00010472-000

For Inquiries

Please call
Helen (UI Consultancy Unit) on +2348029185588, +2347088400003 or
Dr.Serifat F. (UI-LISA) on +2348132686810.
You may also send us an email on
uistat.lisa@gmail.com or management@lisaui.com.