Learn Python, SageMath, Matlab, Maple, Stata and R for mathematics and statistical analysis by taking advantage of our online self study program for only KES 7,250 ($72.5).   Read more

Quarterly workshops: Data analysis, biostatistics and epidemiology, numerical analysis, data science and visualization. Our facilators are experts with many years of experience in robust and trusted software in the scientific field. These include: Python, SageMath, Stata, R and Tableau.
Join other researchers, data analysts and scientists from all over the globe in our quartely workshops. Below are the main courses that we offer. Please note that our workshop size is limited to 24 delegates and registration is on first come first served bases. We therefore advice early registration to avoid missing an opportunity.

Course content

  1. Data management and manipulation
    • Introduction to the software
      • Software overview
      • Directory management commands
      • Data types
      • Basic arithmetics
      • Syntax and its format
      • Syntax editors
      • Creating data sets
      • Display and view data sets
      • Interrupting computations
    • Working with data sets, dates and help
      • Rename of variables
      • Managing variables and/or variable properties
      • Importing data from other software
      • Exporting data to other software
      • Count number of observations
      • Generate sequential numbers
      • Change order of variables
      • Saving data sets
      • Loading data into the memory
      • Conditional statements
      • Create subsets
      • Create random variables (from distributions)
      • Sort variables
      • Random sampling
      • Working with dates
      • Obtaining help in Stata
    • Creating and changing variables
      • Create new variables
      • Extended generate command
      • Duplicate an existing variable
      • Replace contents of a variable
      • Convert numeric to string
      • Convert string numbers to numeric
      • Convert numeric values to missing and vice versa
      • Recode string variables
      • Decode numerically coded variables
      • Transforming a continuous variable to categorical
      • Reduce number of categories of a categorical variable
      • Managing duplicates
    • Transforming variables
      • Split variables
      • Extract parts of variables
      • Standardize variables
      • Create dummy variables
      • Create separate variables
      • Transpose variables
      • Stack variables
      • Unstack variables
      • Convert datasets from wide to long
      • Convert datasets from long to wide
    • Appending and merging data sets
      • Appending data sets by rows
      • Appending data sets by columns
      • One-to-one merging
      • One-to-many merging
      • Many-to-one merging
      • Many-to-many merging
      • Using merge to update data sets
  2. Descriptive statistics
    • Introduction to statistical concepts
      • Review of research process
      • Research designs
      • Sampling techniques
      • Types of data
      • Descriptive statistics
      • Graphs for descriptive statistics
    • Explanatory data analysis in Stata
      • One way frequency tables
      • Crosstabutions
      • Tables of descriptive statistics
      • Exporting tables to Excel
      • Multiple responses
  3. Data visualization (charts)
    • Introduction to graphing
      • The graphics dialog windows
      • Graph elements (x and y labels, titles, legends)
      • Graph appearance (marker symbol, color, size, line width, pattern, e.t.c)
      • Multiple graphs (by option)
      • Graphics syntax
      • Adding text and annotations to graphs
      • Saving and printing graphs
      • Combining active graphs into one figure
      • Graphics window (interactive plotting)
    • Common graphs and charts
      • Pie chart
      • Simple bar graph
      • Grouped/clustered bar graph
      • Stacked bar graph
      • Confidence interval bands
      • Line graph
      • Combined graph
      • Area bar graph
      • Scatter plot
      • Box plot
  4. Hypothesis testing
    • Hypothesis testing background
      • Definitions
      • Statistical inference
      • Generalizability
      • Confidence intervals in clinical research
      • P-values in clinical research
      • Hypothesis testing
      • Interpreting hypothesis test results
    • Tests of differences in population means
      • One sample z tests
      • One sample t tests
      • Two sample independent z tests
      • Two sample independent t tests
      • Two sample paired t test
      • One way analysis of variance
      • Two way analysis of variance
      • Analysis of covariance
    • Analysis of contingency tables
      • Proportion test
      • Chi-square goodness of fit test
      • Chi-square test of independence
      • Chi-square test of homogeneity
      • Proportion test
      • Fisher’s exact test
      • McNemar matched pairs for binary response
    • Non-parametric methods
      • Sign test
      • Wilcoxon signed-rank test
      • Median test
      • Wilcoxon signed-sum (Mann-Whitney) test
      • Kolmogorov-Sminorv goodness-of-fit test
      • Kruskal-Wallis one way analysis of variance
      • Friedman two-way analysis of variance
      • Spearman's rank order correlation
      • Kendall's correlation
  5. Regression analysis and correlation
    • Pearson correlation coefficient
    • Linear regression for continuous response
      • Overview of linear regression analysis
      • Simple linear regression
      • Multiple linear regression
      • Prediction in linear regression
      • Testing for assumptions of linear regression
      • Regression diagonostics for linear regression
    • Logistic regression for categorical response
      • Overview of logistic regression analysis
      • Simple logistic regression
      • Multiple logistic regression
      • Tests for logistic regression
      • Ordinal logistic regression
      • Multinomial logistic regression
      • Conditional logistic regression
    • Poisson regression for count response
      • Overview of Poison regression analysis
      • Simple Poison regression
      • Multiple Poison regression
      • Ordinal Poison regression

Course content

  1. Descriptive statistics and graphics
    • Explanatory data analysis in Stata
      • One way frequency tables
      • Crosstabutions
      • Tables of descriptive statistics
      • Exporting tables to Excel
      • Multiple responses
    • Introduction to graphing
      • The graphics dialog windows
      • Graph elements (x and y labels, titles, legends)
      • Graph appearance (marker symbol, color, size, line width, pattern, e.t.c)
      • Multiple graphs (by option)
      • Graphics syntax
      • Adding text and annotations to graphs
      • Saving and printing graphs
      • Combining active graphs into one figure
      • Graphics window (interactive plotting)
    • Common graphs and charts
      • Pie chart
      • Simple bar graph
      • Grouped/clustered bar graph
      • Stacked bar graph
      • Confidence interval bands
      • Line graph
      • Combined graph
      • Area bar graph
      • Scatter plot
      • Box plot
  2. Hypothesis testing
    • Hypothesis testing background
      • Definitions
      • Statistical inference
      • Generalizability
      • Confidence intervals in clinical research
      • P-values in clinical research
      • Hypothesis testing
      • Interpreting hypothesis test results
    • Tests of differences in population means
      • One sample z tests
      • One sample t tests
      • Two sample independent z tests
      • Two sample independent t tests
      • Two sample paired t test
      • One way analysis of variance
      • Two way analysis of variance
      • Analysis of covariance
    • Analysis of contingency tables
      • Proportion test
      • Chi-square goodness of fit test
      • Chi-square test of independence
      • Chi-square test of homogeneity
      • Proportion test
      • Fisher’s exact test
      • McNemar matched pairs for binary response
    • Non-parametric methods
      • Sign test
      • Wilcoxon signed-rank test
      • Median test
      • Wilcoxon signed-sum (Mann-Whitney) test
      • Kolmogorov-Sminorv goodness-of-fit test
      • Kruskal-Wallis one way analysis of variance
      • Friedman two-way analysis of variance
      • Spearman's rank order correlation
      • Kendall's correlation
  3. Regression analysis and correlation
    • Pearson correlation coefficient
    • Linear regression for continuous response
      • Overview of linear regression analysis
      • Simple linear regression
      • Multiple linear regression
      • Prediction in linear regression
      • Testing for assumptions of linear regression
      • Regression diagonostics for linear regression
  4. Basics of epidemiology
    • Overview to epidemiology
    • Measures of disease frequency
      • Importance of measures of disease frequency
      • Measures of risk and association
      • Risk verses prevention
      • Prevalence
      • Incidence, cumulative incidence & incidence density
      • Relationship between prevalence and incidence
      • Stratification of disease frequency
    • Measures of effect for categorical data
      • Risk difference
      • Risk ratio
      • Attribute fraction
      • Attribute risk
      • Relative risk
      • Odds ratio
    • Measures of effect for stratified categorical data
      • Mantel-Haenzsel test
      • Odds ratio for stratified data
      • Odds ratio for matched pairs studies
      • Testing for trends
    • Vital statistics
      • Introduction
      • Death rates and ratios
      • Measures of fertility
      • Measures of morbidity
  5. Epidemiological studies
    • Clinical research designs
      • Study population
      • Exposure and outcome
      • Study designs
      • Causation
    • Case report and series
    • Cross-sectional studies
    • Cohort studies
      • Cohort study design
      • Ascertainment
      • Advantages
      • Disadvantages
      • Poisson regression for cohort studies
    • Case-control studies
      • Case-control study design
      • Advantages
      • Disadvantages
      • Unconditional logistic regression
      • Conditional logistic regression
  6. Other analysis
    • Misclassification
      • Definition
      • Simple linear regression
      • Non-differential misclassification
      • Differential misclassification
      • Assessing misclassification
    • Confounding
      • Confounding overview
      • Evaluation of confounding factors
      • Confounding by indication
    • Remedies for confounding
      • Restriction
      • Stratification
      • Matching
      • Regression
      • Randomization
      • Interpretation after adjusting for confounding
      • Unadjusted verse adjusted association: confounding
    • Effect modification
      • Overview
      • Synergy between exposure variables
      • Effect modification verses confounding
      • Evaluation of effect modification
      • Effect modification in clinical research articles
      • Effect modification on the relative and absolute scales
    • Introduction to survival analysis
      • Overview
      • Organizing survival data for computer use
      • Censoring (right and left)
      • Truncation (right and left)
      • Plotting survival data (the Kaplan-Meier curve)
      • Log-rank tests
      • Hazard rates
      • Cox proportional hazard models

Course content

  1. Getting started
    • Overview
    • Programming editors
    • Modules (Python only)
    • Structure of commands
    • Creating variables and arrays
    • Working with strings
    • Input and output functions
    • Accessing help
  2. Arithmetic operations and built-in functions
    • Real Numbers
    • Complex Numbers
    • Lists (Python only)
    • Tuples (Python only)
    • Round functions
    • Mathematical functions
  3. Functions and program control flow
    • Introduction
    • Scripts
    • User defined functions
    • Program control flow
  4. Vectors and matrices
    • Introduction
    • Creating scalars and arrays
    • Sequences
    • Subscripting arrays
    • Special matrices
    • Restructuring matrices
    • Operations on matrices
  5. Symbolic mathematics
    • Introduction
    • Polynomials and function simplification
    • Solutions of equations
    • Limits
    • Series expansion
    • Series summation
    • Symbolic operations on matrices
    • Differentiation and Integration
    • Ordinary differential equations
    • Transforms
    • Vector differential calculus
    • Vector integral calculus
  6. Introduction to graphs and plots
    • Introduction to plotting
    • The plot functions
    • Titles and axes labels (x and y)
    • Creating multiple graphs
    • Adding annotations / text to graphs
    • X and Y axes properties
    • Additional options
    • Common 2D and 3D plots
  7. Direct solutions of linear systems of equations
    • Introduction
    • Elementary row operations
    • Elementary row operation applications
    • LU factorization
    • Solutions of linear systems with built-in functions
  8. Iterative and conjugate gradient methods
    • Introduction
    • Vector norms
    • Matrix norms
    • Iterative techniques
    • Conjugate gradient methods
  9. Solutions of single variable equations
    • Introduction
    • Closed domain methods
    • Open domain methods
    • Other methods
    • Equations with multiple roots
  10. Solutions of systems of non-linear equations
    • Introduction
    • Fixed point iteration
    • Newton’s method
    • Quasi-Newton methods: Broyden method
    • Steepest gradient techniques: Steepest descent
    • Homotopy and continuation methods: Continuation algorithm
  11. Numerical differentiation
    • Introduction
    • Direct polynomial fit
    • Newton difference methods
    • Three point formulas
    • Five point formulas
    • Richardson extrapolation
    • Second derivative mid-point formula
  12. Numerical integration
    • Introduction
    • Direct polynomial fit
    • Newton-Cotes formulas
    • Composite rules
    • Romberg integration
    • Gaussian quadrature
    • Double integration
  13. Curve fitting and interpolation
    • Introduction
    • Least square regression
    • Linearizing nonlinear data
    • Polynomial interpolation
    • Interpolation using Splines
  14. Initial value problems
    • Introduction
    • Single step methods
    • Adaptive Runge-Kutta methods
    • Multi-step methods
    • Predictor-corrector methods
    • Extrapolation method
    • Systems of ordinary differential equations
    • Higher order ordinary differential equations
  15. Boundary value problems
    • Introduction
    • Shooting method
    • Finite difference method
    • Rayleigh-Ritz method

Course content

Course content under development ...

Course content

Course content under development ...
Month Dates Course name
Quarter 3, 2019
July Start: 01-Jul-2019

End: 12-Jul-2019

Numerical Analysis, Modelling and Simulation (Python)

Start: 15-Jul-2019

End: 26-Jul-2019

Numerical Analysis, Modelling and Simulation (SageMath)
August

Start: 05-Aug-2019

End:16-Aug-2019

Data Management and Analysis for Project Managers (Stata)

Data Management and Analysis for Project Managers (R)

Start: 19-Aug-2019

End: 30-Aug-2019

Biostatistics and Epidemiology for Public Health Professionals (Stata)

Biostatistics and Epidemiology for Public Health Professionals (R)

September

Start: 02-Sep-2019

End: 13-Sep-2019

Data Analytics and Machine Learning for Data Scientists (Python)

Start: 16-Sep-2019

End: 27-Sep-2019

Data Analytics and Machine Learning for Data Scientists (R)

Start: 23-Sep-2019

End: 27-Sep-2019

Data Visualization for Enhanced Reporting (Tableau)
Quarter 4, 2019
October Start: 07-Oct-2019

End: 18-Oct-2019

Numerical Analysis, Modelling and Simulation (Python)

Start: 21-Oct-2019

End: 01-Oct-2019

Numerical Analysis, Modelling and Simulation (SageMath)
November

Start: 04-Sep-2019

End: 15-Sep-2019

Data Management and Analysis for Project Managers (Stata)

Data Management and Analysis for Project Managers (R)

Start: 18-Sep-2019

End: 29-Sep-2019

Biostatistics and Epidemiology for Public Health Professionals (Stata)

Biostatistics and Epidemiology for Public Health Professionals (R)

December

Start: 02-Dec-2019

End: 14-Dec-2019

Data Analytics and Machine Learning for Data Scientists (Python)

Data Analytics and Machine Learning for Data Scientists (R)

Start: 02-Dec-2019

End: 06-Dec-2019

Data Visualization for Enhanced Reporting (Tableau)

Get the much needed skills in data science by signing up to any of our quarterly workshops in Data Analytics (KES 34,800) and Machine Learning (KES 43,500) using Python and R   Read more