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
 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
 Onetoone merging
 Onetomany merging
 Manytoone merging
 Manytomany merging
 Using merge to update data sets
 Introduction to the software
 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
 Introduction to statistical concepts
 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
 Introduction to graphing
 Hypothesis testing
 Hypothesis testing background
 Definitions
 Statistical inference
 Generalizability
 Confidence intervals in clinical research
 Pvalues 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
 Chisquare goodness of fit test
 Chisquare test of independence
 Chisquare test of homogeneity
 Proportion test
 Fisher’s exact test
 McNemar matched pairs for binary response
 Nonparametric methods
 Sign test
 Wilcoxon signedrank test
 Median test
 Wilcoxon signedsum (MannWhitney) test
 KolmogorovSminorv goodnessoffit test
 KruskalWallis one way analysis of variance
 Friedman twoway analysis of variance
 Spearman's rank order correlation
 Kendall's correlation
 Hypothesis testing background
 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
 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
 Explanatory data analysis in Stata
 Hypothesis testing
 Hypothesis testing background
 Definitions
 Statistical inference
 Generalizability
 Confidence intervals in clinical research
 Pvalues 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
 Chisquare goodness of fit test
 Chisquare test of independence
 Chisquare test of homogeneity
 Proportion test
 Fisher’s exact test
 McNemar matched pairs for binary response
 Nonparametric methods
 Sign test
 Wilcoxon signedrank test
 Median test
 Wilcoxon signedsum (MannWhitney) test
 KolmogorovSminorv goodnessoffit test
 KruskalWallis one way analysis of variance
 Friedman twoway analysis of variance
 Spearman's rank order correlation
 Kendall's correlation
 Hypothesis testing background
 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
 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
 MantelHaenzsel 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
 Epidemiological studies
 Clinical research designs
 Study population
 Exposure and outcome
 Study designs
 Causation
 Case report and series
 Crosssectional studies
 Cohort studies
 Cohort study design
 Ascertainment
 Advantages
 Disadvantages
 Poisson regression for cohort studies
 Casecontrol studies
 Casecontrol study design
 Advantages
 Disadvantages
 Unconditional logistic regression
 Conditional logistic regression
 Clinical research designs
 Other analysis
 Misclassification
 Definition
 Simple linear regression
 Nondifferential 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 KaplanMeier curve)
 Logrank tests
 Hazard rates
 Cox proportional hazard models
 Misclassification
Course content
 Getting started
 Overview
 Programming editors
 Modules (Python only)
 Structure of commands
 Creating variables and arrays
 Working with strings
 Input and output functions
 Accessing help
 Arithmetic operations and builtin functions
 Real Numbers
 Complex Numbers
 Lists (Python only)
 Tuples (Python only)
 Round functions
 Mathematical functions
 Functions and program control flow
 Introduction
 Scripts
 User defined functions
 Program control flow
 Vectors and matrices
 Introduction
 Creating scalars and arrays
 Sequences
 Subscripting arrays
 Special matrices
 Restructuring matrices
 Operations on matrices
 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
 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
 Direct solutions of linear systems of equations
 Introduction
 Elementary row operations
 Elementary row operation applications
 LU factorization
 Solutions of linear systems with builtin functions
 Iterative and conjugate gradient methods
 Introduction
 Vector norms
 Matrix norms
 Iterative techniques
 Conjugate gradient methods
 Solutions of single variable equations
 Introduction
 Closed domain methods
 Open domain methods
 Other methods
 Equations with multiple roots
 Solutions of systems of nonlinear equations
 Introduction
 Fixed point iteration
 Newton’s method
 QuasiNewton methods: Broyden method
 Steepest gradient techniques: Steepest descent
 Homotopy and continuation methods: Continuation algorithm
 Numerical differentiation
 Introduction
 Direct polynomial fit
 Newton difference methods
 Three point formulas
 Five point formulas
 Richardson extrapolation
 Second derivative midpoint formula
 Numerical integration
 Introduction
 Direct polynomial fit
 NewtonCotes formulas
 Composite rules
 Romberg integration
 Gaussian quadrature
 Double integration
 Curve fitting and interpolation
 Introduction
 Least square regression
 Linearizing nonlinear data
 Polynomial interpolation
 Interpolation using Splines
 Initial value problems
 Introduction
 Single step methods
 Adaptive RungeKutta methods
 Multistep methods
 Predictorcorrector methods
 Extrapolation method
 Systems of ordinary differential equations
 Higher order ordinary differential equations
 Boundary value problems
 Introduction
 Shooting method
 Finite difference method
 RayleighRitz method
Course content
Course content
Month  Dates  Course name 
Quarter 3, 2019  
July 
Start: 01Jul2019
End: 12Jul2019 
Numerical Analysis, Modelling and Simulation (Python) 
Start: 15Jul2019 End: 26Jul2019 
Numerical Analysis, Modelling and Simulation (SageMath)  
August 
Start: 05Aug2019 End:16Aug2019 
Data Management and Analysis for Project Managers (Stata) Data Management and Analysis for Project Managers (R) 
Start: 19Aug2019 End: 30Aug2019 
Biostatistics and Epidemiology for Public Health Professionals (Stata) Biostatistics and Epidemiology for Public Health Professionals (R) 

September 
Start: 02Sep2019 End: 13Sep2019 
Data Analytics and Machine Learning for Data Scientists (Python) 
Start: 16Sep2019 End: 27Sep2019 
Data Analytics and Machine Learning for Data Scientists (R)  
Start: 23Sep2019 End: 27Sep2019 
Data Visualization for Enhanced Reporting (Tableau)  
Quarter 4, 2019  
October 
Start: 07Oct2019
End: 18Oct2019 
Numerical Analysis, Modelling and Simulation (Python) 
Start: 21Oct2019 End: 01Oct2019 
Numerical Analysis, Modelling and Simulation (SageMath)  
November 
Start: 04Sep2019 End: 15Sep2019 
Data Management and Analysis for Project Managers (Stata) Data Management and Analysis for Project Managers (R) 
Start: 18Sep2019 End: 29Sep2019 
Biostatistics and Epidemiology for Public Health Professionals (Stata) Biostatistics and Epidemiology for Public Health Professionals (R) 

December 
Start: 02Dec2019 End: 14Dec2019 
Data Analytics and Machine Learning for Data Scientists (Python) Data Analytics and Machine Learning for Data Scientists (R) 
Start: 02Dec2019 End: 06Dec2019 
Data Visualization for Enhanced Reporting (Tableau) 