Scientific Data Management Training 2019 | UCC

Scientific Data Management Training by the University of Cape Coast (UCC) – 2019

Scientific Data Management

Date/Time/Duration

Friday, November 29, 2019 – 9:55am

Venue/Location

University of Cape Coast, Ghana

Organized By

Directorate of Research, Innovation and Consultancy

Speaker(s)

Various Speakers

Description

Scientific data management enhances the capacity of postgraduate students to meaningful engage in conducting quality research by developing appropriate research proposals, design of studies, collection and analysis of data for meaningful reporting.

PhD and MSc students are heavily involved in large-scale experiments or surveys that sometimes lead to complex designs and to subsequent messy data. Figuring out how to handle data resulting from such experiments/surveys takes time, and getting appropriate assistance is difficult.

The students are also constrained on how to effectively analyze data using appropriate statistical software, interpret the results and communicate well to the target audience. In recognition of these shortcomings, this course is structured to encompass broad biometrical needs that will equip the postgraduate students with skills required in conducting their research efficiently and effectively.

The content incorporated in this course is drawn from broader topics ranging from planning of experiments/surveys, designing and implementing experiments, conducting data analysis for qualitative and quantitative data. The students will also be exposed to key statistical software (Genstat and SPSS) for data analysis and reporting.

Key Features / Side Attractions

Confidently illustrate fundamental concepts behind experimental/survey designs and statistical data analyses within the context of developing countries;

Apply key statistical concepts such as correlation& regression analysis; categorical data analysis techniques and generalized linear models, etc; and

Use statistical software to describe, analyze and model the state of a biological or agricultural system in both a quantitative and qualitative manner.