To seek and maintain full-time position that offers professional challenges utilizing interpersonal skills, excellent time management and problem-solving skills.
Pre-Processing and cleansing of data
• Using software and languages most vital to weakest: Python, RStudio, MATLAB, MSQL, SAS, & Excel
• Perform predictions on missing data or delete it if appropriate
• Merging multiple files (creating unions based on key fields)
• Checking for outliers and dealing with them inappropriately manner
• Normalize, scale and max-min data based on regression type to standardize data for further investigations
• Dimension reduction techniques, i.e. PCA, SVD
Analysis & reporting findings
• Determining data distribution and hypothesis testing
• Determine whether there are imbalanced class labels in the dataset; majority/minority testing
• Predict future changes through machine learning techniques based on statistical and pure math (KNN, Bayesian, Auto-Decoders, LSTM [1])Predict
• For larger dataset modelling systems: SKLearn, Keras/TensorFlow 2.0
[1] This list is long and diverse depending on the problem and solution, the type and format of the data, n ›› m or n ‹‹ m, the attributes and metadata, and the contents of each feature being composed of categorical or numerical values