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
Data management, analysis and visualization for project managers
Participants will learn the principles of statistics and gain skills in using statistical tools to describe, study and investigate the various variables in survey data sets using Stata. The statistical background required to conduct research, describe, summarize, develop hypothesis, assess associations, analyze data, interpret and communicate results will be studied comprehensively.
The course targets professionals in research (or research related) organizations / institutions who wish to acquire or increase their computational skills in the Stata software. The course is developed to benefit data managers, research officers, project managers, statisticians, data analysts, students (undergraduate and postgraduate), trainers/facilitators, research organizations, nongovernmental organizations, policy makers, among others.
Learning objectives:
 Perform basic and advanced data management routines on survey data sets.
 Perform explanatory data analysis tasks with easy.
 Create univariate and bivariate graphs / charts.
 Carry out classical tests of hypothesis and draw clear inferences and conclusion to them.
 Perform nonparametric tests and draw conclusions.
 Come up with linear, poisson and logistic regression models, tests their validity, make conclusions and use such models to make prediction about the general population.
Dates 
Monthly: Starts every First Monday Duration: 2 weeks (10 days) 

Time  0830Hrs  1630Hrs EAT (Mon  Fri)  
Delivery  Classroom (online also available)  
Charges  KES 125,000 ($1,250) 
The course can be customized to fit your specific needs in terms of course content, date, time and venue (including online sessions). Please talk to us for the customization.
 Data management and manipulation
 Introduction to Stata
 Starting Stata
 Setting layout
 Directory management commands
 Data types in Stata
 Using Stata as a calculator
 Stata command and options
 Stata dofiles
 Creating data sets directly in Stata
 Display and viewing 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
 The in and if qualifiers
 The by prefix
 Create subsets (keep and drop)
 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 Stata
 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: tabstat command
 Tables of descriptive statistics: table command
 Exporting Stata tables to Excel with tabout command
 Multiple responses with mrtab command
 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
 Fisherâ€™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