In this course, students will learn how to use computational tools to address statistical-related tasks. It means that students will learn how to use some major statistical data analysis tools such as MS Excel, SPSS, and R programming.
Learning objectives:
1. Understood the basis of data classification and the contemporary definitions.
2. Understood the basis of data understanding and preprocessing.
3. Understood the basis of Exploratory Data Analysis (EDA).
4. Have the ability or capability to handle simple statistical analysis with MS Excel and SPSS
5. Have basic coding knowledge (syntax and programming) of R.
This course is an introductory course to all of these concepts, and students are expected to:
1. be able to use MS Excel for data analysis and perform simple interpretation of results of some statistical modelling outputs.
2. be able to use SPSS for data analysis and perform simple interpretation of results of some statistical modelling outputs.
3. be able to use R programming for data analysis and perform simple interpretation of results of some statistical modelling outputs.
Learning objectives:
1. Understood the basis of data classification and the contemporary definitions.
2. Understood the basis of data understanding and preprocessing.
3. Understood the basis of Exploratory Data Analysis (EDA).
4. Have the ability or capability to handle simple statistical analysis with MS Excel and SPSS
5. Have basic coding knowledge (syntax and programming) of R.
This course is an introductory course to all of these concepts, and students are expected to:
1. be able to use MS Excel for data analysis and perform simple interpretation of results of some statistical modelling outputs.
2. be able to use SPSS for data analysis and perform simple interpretation of results of some statistical modelling outputs.
3. be able to use R programming for data analysis and perform simple interpretation of results of some statistical modelling outputs.
- Teacher: Dr. O. J. Akintande
- Teacher: VICTORIA LAOYE
Skill Level: Beginner