Data Science, Research methods and statistics course

Start Date: July 18th, 2022

End Date: July 22nd, 2022

Venue: Zoom (Virtual)


About this training Course
Data science and analytics is unquestionably an important component in all forms of social, biological, natural resources and process engineering research for knowledge discovery so as to draw meaningful and actionable insights from data. Knowledge and innovation creation through research is at the heart of sustainable development and data analytics is key in research for development. Appropriate use and application of analytical methods help navigate common problems that can lead to incorrect conclusions for decision making. This training course is therefore intended to enhance participant’s capacity to be able to derive the most out of research data. It is assumed that participants have some knowledge of basic statistics as it is expected that they must have had statistics course at both the Bachelor’s and Master’s degree levels.

Learning outcomes
To build capacity of participants in the use of R software for data analytics and Ms. Excel for data
management and be able to incorporate and use these tools in their research work.

Overall objective
To enhance researcher’s capacity in the use of data analytics in research for sound results and conclusion.
The specific objectives include:
• To understand basic data science concepts and principles
• To apply basic data science concepts and principles in data analysis
• To equip scholars with statistical computing skills and data analytics
• To understand open data, reproducible science, and data management

Training approach
The course will consist of both lectures and practical computing sessions with emphasis on illustrating
techniques using real life examples. More time will be devoted to computer practical sessions which will be based on software R/Python and Excel.

Expected outcomes
By the end of the course, scholars should be able to:
• Identify the appropriate statistical tool for specified hypothesis and data
• Efficiently use R/Python/Ms. Excel software for data analytics
• Present, interpret and communicate research results appropriately
• Manage scientific data for open science

Advance registration:

After registering, you will receive a confirmation email containing information about joining the meeting.