Technical References

Analyze Corporation uses various techniques to evaluate large data-sets in a timely manner. Although the following references are not developed by us, they do represent some of the capabilities that we possess to solve analytical problems.

PYTHON:  PANDAS

•Tutorial: Key Pandas Features (Crosstab, Groupby,Plotting, Handling missing values, etc).

•Explanation: Pandas Pivot Tables.

•Explanation: Manipulating Data via Python Pandas.

R: ExampleS AND TUTORIALS

•Explanation: How to make a Pivot Table in R.

•Explanation: Counting and Aggregating Data in R.

•Explanation: Data Binning and Plotting in R.

•Explanation: Showing and Hiding Data using R Leaflet.

EXCEL: ExampleS AND TUTORIALS

•Explanation: Calculate the difference between two dates: DATEDIF Function.

•Explanation: Formulas for Conditional Formatting within Excel worksheets.

•Explanation: Improving Excel’s Performance with large data-sets via VBA.

•Explanation: Creating in-memory Recordsets during runtime via VBA and ADODB.

GEOSPATIAL: ExampleS AND TUTORIALS

•Tutorials: Different techniques to map US Census Data.

•Explanation: Calculate the distance between any two GPS coordinates.

MACHINE LEARNING: ExampleS AND TUTORIALS

•Explanation: The difference between supervised and unsupervised machine learning.

•Tutorials: A collection of Weka tutorials.

•Tutorials: Data-mining with Weka.

PRINCIPAL COMPONENT ANALYSIS:

•Explanation: Using Python and Matplotlib library to classify data.