You are an accounting professional who specializes in data analytics. You recently transferred from the corporate accounting department to a new finance transformation team that is dedicated to using financial and employee data to generate meaningful insights for management and executives. You are tasked with performing data analytics using data from employee satisfaction surveys for the past five years. The data includes the following fields: - Employee name - Department - Date hired, which is used to calculate the length of employment - Salary - Overall employee satisfaction rating (scale of 1-5) - Work-life balance rating (scale of 1-5) - Training and development rating (scale of 1-5) - Corporate culture rating (scale of 1-5) - Feedback, which is an open text field where employees enter commentary to communicate with executive leadership You have developed the following analytics use cases. For each of them, identify the best data analytics technique you could use to perform that analysis. 1. You want to determine if variable factors are influencing an employee's overall employee satisfaction rating. For example, does an employee's department, length of employment, or salary relate to the overall employee satisfaction rating? 2. You want to use the results of your analysis of the case above to predict whether the company's overall employee satisfaction rating will increase or decrease next year. 3. You want to analyze the employee feedback to identify whether the overall commentary is positive or negative. 4. You want to analyze the relationships between employees in different departments by visualizing the connections between departmental employees, department managers, and executive leadership. 5. You want to identify any patterns or trends in the data without specifying a specific field to analyze. 6. You want to see all possible outcomes of future overall employee satisfaction ratings related to a number of different changes in relevant factors by modeling scenarios and results. Classification, which is supervised Clustering, which is unsupervised Forecasting NLP/ sentiment analysis Linear regression Monte Carlo simulation Network analysis Process mining You are an accounting professional who specializes in data analytics. You recently transferred from the corporate accounting department to a new finance transformation team that is dedicated to using financial and employee data to generate meaningful insights for management and executives. You are tasked with performing data analytics using data from employee satisfaction surveys for the past five years. The data includes the following fields: - Employee name - Department - Date hired, which is used to calculate the length of employment - Salary - Overall employee satisfaction rating (scale of 1-5) - Work-life balance rating (scale of 1-5) - Training and development rating (scale of 1-5) - Corporate culture rating (scale of 1-5) - Feedback, which is an open text field where employees enter commentary to communicate with executive leadership You have developed the following analytics use cases. For each of them, identify the best data analytics technique you could use to perform that analysis. 1. You want to determine if variable factors are influencing an employee's overall employee satisfaction rating. For example, does an employee's department, length of employment, or salary relate to the overall employee satisfaction rating? 2. You want to use the results of your analysis of the case above to predict whether the company's overall employee satisfaction rating will increase or decrease next year. 3. You want to analyze the employee feedback to identify whether the overall commentary is positive or negative. 4. You want to analyze the relationships between employees in different departments by visualizing the connections between departmental employees, department managers, and executive leadership. 5. You want to identify any patterns or trends in the data without specifying a specific field to analyze. 6. You want to see all possible outcomes of future overall employee satisfaction ratings related to a number of different changes in relevant factors by modeling scenarios and results. Classification, which is supervised Clustering, which is unsupervised Forecasting NLP/ sentiment analysis Linear regression Monte Carlo simulation Network analysis Process mining