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.