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• Evaluate the research purpose and propose appropriate research methods (qualitative method, quantitative method, mixed methods)

• Develop the research process for the selected method

• Research design: propose detailed research designed based on the research purpose and methods (survey, experiment, archival, case study, interview); calculate sample sizes, prepare the data collection procedure

• Measurement: develop the steps for the instrument development and reliability and validity assessment

• Data examination and coding: explore and examine the customer’s datasets, identify variables and parameters, code data

• Data treatment and merging: prepare data for analysis, merge data

• Statistical data analysis: determine appropriate statistical methods based on the analysis purpose, perform statistical analysis, interpret the results

• Qualitative data analysis: code data, assess coding reliability and validity, perform qualitative data analysis, interpret the results

• Machine learning algorithms: classification and regression modeling, decision tree, random forest, gradient boosting, subject vector machine, bayesian network, neural network

• Text mining: text parsing, text filter, text cluster, text topic

• Data mining using machine learning: data partition, data transformation, modeling, model assessment and comparison, ensemble learning