Training on Practical Techniques in Data Management

Before delving into the analysis, the practitioner must be able to transform the data to a structure required by a statistical model. The "Practical Techniques in Data Management" is an introductory course to practical techniques in data management in using R, such as importing and exporting data, variable transformation, subsetting, summarization, data cleaning, and reshaping. Examples will be provided in discussing common data structures in the fields of market research, economics, finance, and other areas.

Course Description

This course serves as an introduction to practical techniques in data management using RStudio. Specifically, it covers the following:

• importing and exporting data
• transforming variables
• subsetting and summarizing data
• cleaning and reshaping data

Prior experience in R programming is not needed in order to take this course as it will also cover the basics of such language. The course serves as a launch pad in preparation for more advanced statistical modeling.

Target Audience

The training will equip practitioners with skills in order to start using R for data management. This will be particularly useful those who are involved in (but not limited to) the analysis of the following data structures:

• cross-section data (e.g. opinion polls, satisfaction surveys, needs assessment)
• time-series data (e.g. sales forecasting, economic modeling, overlay analysis)
• longitudinal data (e.g. client monitoring, market research, demand forecasting)

Prerequisite Knowledge

Knowledge of data management using other software (e.g. MS Excel) or programming languages will be helpful but not necessary.

About the resource person

Charlene Mae Celoso is an instructor from the UP School of Statistics. She obtained her BS Statistics degree from UP Diliman in 2015, graduating Magna Cum Laude. She finished her MS Statistics degree from the same institution. As an instructor, she has handled courses in elementary statistics, introduction to programming, introduction to exploratory data analysis, and introduction to regression analysis. She has also served as a resource person in workshops on introduction to R programming and time series analysis.

Download the following:

• 2018 PSAI Training Outline
• 2018 PSAI Training Personal Information Sheet
• 2018 PSAI Training Registration Form