We organize quartely workshops that are aimed at equiping scientists and researchers with the necessary skills they require to draw insight from real data sets. Through highly and intensive practical sessions, we are able to help them tackle scientific problems by carrying out analysis on real experimental data sets.
Duration: 5 days
Charges: $580

Participants will learn the principles of data science and gain skills in using Python or R programming languages and their respective libraries to examine, manipulate, clean, aggregate and visualize data sets. The basic statistical background required to describe, summarize, analyze (EDA) and visualize data will also be studied comprehensively.


Course objectives

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

  • Use and properly apply different data types and structures.
  • Write repetitive structures (for and while loops).
  • Write proper conditional statements to control program flow.
  • Perform array manipulation and common mathematics operations.
  • Perform data wrangling and cleaning tasks on data sets
  • Aggregate data sets and perform explantory data analysis tasks.
  • Visualize functions and data sets using common graphs.
Duration: 5 days
Charges: $580

Participants will learn the principles of data science and gain skills in using Python or R programming languages and their respective libraries to examine, manipulate, clean, aggregate and visualize data sets. The basic statistical background required to describe, summarize, analyze (EDA) and visualize data will also be studied comprehensively.


Course objectives

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

  • Use and properly apply different data types and structures.
  • Write repetitive structures (for and while loops).
  • Write proper conditional statements to control program flow.
  • Perform array manipulation and common mathematics operations.
  • Perform data wrangling and cleaning tasks on data sets
  • Aggregate data sets and perform explantory data analysis tasks.
  • Visualize functions and data sets using common graphs.
Duration: 5 days
Charges: $580

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 effectively communicate results will be studied.


Course objectives

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

  • Clearly understand and apply the different data types and structures.
  • Write repetitive data structures (for and while loops).
  • Write proper conditional statements to control program flow.
  • Perform basic and advanced data wrangling routines on survey data sets.
  • Use relevant commands / functions to validate and clean data sets.
  • Aggregate data and perform explanatory data analysis tasks with easy.
  • Create univariate and bivariate graphs / charts.
  • Run tests of differences in proportions and means (t tests and ANOVA) and draw clear inferences and conclusion to them.
  • Analyze contingency tables and fully understand the statistical background
  • Perform non-parametric tests and draw conclusions.
  • Carry our correlation analysis tests to assess the association between variables
  • Run linear models with quantitative response variable.
  • Run logistic regression models with qualitative response variable.
Duration: 5 days
Charges: $580

Participants will learn the principles of epidemiology and biostatistics and gain skills in using epidemiological and biostatistical tools to describe, monitor and investigate the determinants of population health. The statistical and epidemiological background required to conduct research, describe, summarize, develop hypothesis, assess associations, analyze data, interpret and communicate results will be studied comprehensively.


Course objectives

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

  • Understand the various experimental designs in epidemiological studies (e.g. cohort, case-control studies).
  • Perform basic and advanced data wrangling routines on health survey data sets.
  • Use relevant commands / functions to validate and clean data sets.
  • Aggregate data and perform explanatory data analysis tasks with easy.
  • Create univariate and bivariate graphs / charts.
  • Run tests of differences in proportions and means (t tests and ANOVA) and draw clear inferences and conclusion to them.
  • Analyze contingency tables and fully understand the statistical background
  • Perform non-parametric tests and draw conclusions.
  • Carry our correlation analysis tests to assess the association between variables
  • Run linear models with quantitative response variable.
  • Run logistic regression models with qualitative response variable.
  • Basic introduction to survival analysis

Remark: The indicated charges are inclusive of 16% VAT and cater for refresshments, lunch, stationary and certificate but DO NOT cater for other expenses e.g. travel, accomodation, e.t.c.

Month Start date End date Software
Data Science & Analytics for Business & Data Scientists
January 06/01/2020 10/01/2020

Python

or

R

March 02/03/2020 06/02/2020
May 04/05/2020 08/05/2020
Machine Learning with Applications to Business & Finance
January 13/01/2020 17/01/2020

Python

or

R

March 09/03/2020 13/03/2020
May 11/05/2020 15/05/2020
Data Wrangling and Analysis for Project Managers
February 10/02/2020 14/02/2020

Stata

or

R

Aprill 13/04/2020 17/04/2020
June 08/06/2020 12/06/2020
Biostatistics & Epidemiology for Public Health Professionals
February 10/02/2020 14/02/2020

Stata

or

R

April 13/04/2020 17/04/2020
June 08/06/2020 13/06/2020

We can also customize and/or arrange training that fits your needs/time outside the above calendar dates. Talks to us if you need training outside our calendar schedule.

We have trained the following local and international companies and institutions in Matlab, Stata, R, Python, SPSS and Excel.

  1. Pan African University, Africa
  2. Ministry of Health, Uganda
  3. Save The Children, International
  4. Kenyatta University, Kenya
  5. Kenya Livestock and Research Organization, Kenya
  6. Ministry of Health/CDC, Mozambique
  7. University of Mogadishu, Somalia
  8. Electricity Commission of Malawi, Malawi
  9. Juba University, South Sudan
  10. University of Nairobi, Kenya

Among others