Budget Builder for Data Science
Data Analytics + Management
Files
Redcap form
A step by step budget builder.
Overview
This free tool will help you estimate a budget for data science services from CHI.
An interactive budget building tool
If you are planning a data-driven study and preparing a budget for a grant application, you may want to consider the services provided by CHI and budget for them at the outset of planning. This builder is designed to support you through the budgeting process. In the end, we will suggest a time and cost estimate for Data Science-related services. Please request a consult if you would like to discuss in more detail.
The key steps and services we provide
- Research question framing
- Experimental design coding including randomization & blocking
- Sample size calculation, etc.
- Database design, managment with REDCap (DCC)
- Data collecting tools designs and implementation with REDCap (DCC)
- Population-based data access and data linkage (MCHP)
Note: If you require support with REDCap, our Data Coordinating Centre (DCC) from academic Contract Research Organization(aCRO) can help you. Please contact CHI at info@chimb.ca. If you require population-based data and data linkage, contact MCHP at info@cpe.umanitoba.ca or visit their website for more information.
Data Pre-processing is an essential part of the data analysis process, yet it is often overlooked when researchers prepare their budgets. Therefore, we highlight it separately to help clarify its importance. The time required for Data Pre-processing, by nature, depends on several factors: including the data quality, data quantity, your research questions and the analytical plan.
Data Analysis will likely require Data Pre-processing, so we strongly recommend you also select the Data Preprocessing to budget. Time required for Data Analysis when data is ready (i.e., pre-processing is completed) depends on your research questions, the analytical plan and complexity of the chosen analytical methods.
- Generate publication-quality figures, tables.
- Draft manuscripts, especially DS related sections, e.g. Methods, Results, and/or Discussion.
- Proofread manuscripts to ensure correct description and interpretation of output, etc.
- Prepare a summary report detailing the analysis process and results.