. Analytical Cleaning Service **NEW SERVICE** more info >
. Study Design more info >
. Sample Size and Power Calculation more info >
. Primary Data Collection and Questionnaire Construction more info >
. Data Cleansing and Scrubbing more info >
. Statistical Data Analysis more info >
. Graphical Data Analysis more info >
. Data Mining and Predictive Modeling more info >
. Machine Learning more info >
. Medical and Technical Writing more info >
. Geospatial Analysis more info >
. File Transfer more info >

Analytical Cleaning Service: Analytical shops of all sizes can benefit from a "cleaning service" that maximizes efficiency and minimizes error. Below are just a few examples.

Standardize Analytical Processes: Methodology can vary between analysts (and within an analyst over time) often producing heterogeneous results. Key infrastructural techniques can be created for analysts to "grab-n-go" standardized variables when needed. Choosing where to apply this standardization should be restricted to the business activities that have a high return on investment.

Automate Reporting: Reports are often created at predictable times (e.g. monthly, quarterly) however this is often a labor-intensive task (likely) due to the lack of data standardization. Designing a process that standardizes the data (and updates to these data) along with an automated query tool where the user can "plug and chug" to create the new report in seconds (rather hours or days) saves time and reduces error.

Optimize Database Queries: Databases are often designed to minimize hardware disc space without consideration for analysis and reporting resulting in significant post-hoc programming time. When large volumes of data accumulate, query run times are extended. To circumvent this queries need to be optimized to run effeciently saving CPU.

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Study Design

There may be many choices of study design that may answer your research questions, however data availability and budget are often the drivers of such decisions. Randomized clinical trial (rct), cross sectional, case-control, cohort, case series, case crossover, time series, nested case-control are some examples of the most common study designs.

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Study Design

There may be many choices of study design that may answer your research questions, however data availability and budget are often the drivers of such decisions. Randomized clinical trial (rct), cross sectional, case-control, cohort, case series, case crossover, time series, nested case-control are some examples of the most common study designs.

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. Primary Data Collection and Questionnaire Construction

Designing an effective questionnaire is no simple task! Proper safeguards must be employed to ensure analytical integrity.

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Data Cleansing and Scrubbing

This is the process of ensuring that your data are correct, consistent and useable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again.

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Statistical Data Analysis

Classical statistical data analysis is necesssary to make inferences from your data. For every research question, there is often more than one statistical test available to use and many factors play a role in deciding which one is most appropriate. Knowing the advantages and limitations of each will facilitate a smooth peer review process.

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Graphical Data Analysis

"A picture is worth a 1000 words" and there is no better example of this when the perfect graphical representation of your results is found!

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Data Mining, Predictive Modeling and Machine Learning

The terminology of these techiques have been re-branded over time. No matter what you call it these techniques have become very useful in answering contemporary analytical questions. These are distinguished from classical statistical analyses.

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Medical and Technical Writing

Translating analytical findings for peer review journals or technical reports is skill that take years to develop.

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Geospatial Analysis with GIS

Spatial analysis using geographical informational systems (GIS) is often a necessary component in modern epidemiological analyses. They provide the means to identify patterns of disease not possible with more traditional statistical analysis.

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File Transfer

session 1 tutorial (password-protected)
session 2 tutorial (password-protected)
session 3 tutorial (password-protected)
session 4 tutorial (password-protected)
session 5 tutorial (password-protected)
session 6 tutorial (password-protected)
session 8 tutorial (password-protected)
session 9 tutorial (password-protected)
session 10 tutorial (password-protected)
session 11 tutorial (password-protected)
session 12 tutorial (password-protected)


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