Create a dictionary named employee_records to Question 2 in python language: Managing Employee Performance Records and Data Analysis Step 1: Database Creation - Employee Records: manage employee details. Store the dictionary with employee IDs as keys and dictionaries containing employee information (name, department, performance ratings) as values. employee_ records = {"EMP001": {"name": "John Doe", "department": "Sales", "ratings": [4, 5, 4]}, "EMP002":{"name": "Emma Smith", "department": "Marketing", "ratings": [5, 4, 5] }} Step 2: String Operations - Data Validation using only (Regex): Implement string operations for data validation and standardization: Validate employee IDs: Ensure IDs follow a specific format (start with 'EMP' followed by numbers). Implement error handling for invalid or inconsistent IDs. Standardize department names: Validate department names for consistent formatting. Handle potential duplicates or inconsistencies in department names. Error Handling using (Try - Except): Implement robust error handling for any inconsistencies or invalid data in the records. Step 3: Advanced Data Analysis: Perform advanced analysis on employee records stored in the dictionary: Calculate Average Ratings: Calculate the average performance rating for each employee across all evaluation periods. Identify Top Performers: Identify employees with exceptional performance (highest ratings) in each department. After implementing the above question, please solve the below questions: 1. Describe the data validation process implemented for employee records. Provide examples of error handling mechanisms for handling inconsistencies or invalid data. 2. Explain the importance of data standardization in managing employee records. Discuss how standardized data contributes to data integrity and analysis accuracy. 3. Demonstrate the process of calculating average performance ratings for employees and identifying top performers within each department. Provide examples showcasing the implementation of these data analysis techniques.
Create a dictionary named employee_records to Question 2 in python language: Managing Employee Performance Records and Data Analysis Step 1: Database Creation - Employee Records: manage employee details. Store the dictionary with employee IDs as keys and dictionaries containing employee information (name, department, performance ratings) as values. employee_ records = {"EMP001": {"name": "John Doe", "department": "Sales", "ratings": [4, 5, 4]}, "EMP002":{"name": "Emma Smith", "department": "Marketing", "ratings": [5, 4, 5] }} Step 2: String Operations - Data Validation using only (Regex): Implement string operations for data validation and standardization: Validate employee IDs: Ensure IDs follow a specific format (start with 'EMP' followed by numbers). Implement error handling for invalid or inconsistent IDs. Standardize department names: Validate department names for consistent formatting. Handle potential duplicates or inconsistencies in department names. Error Handling using (Try - Except): Implement robust error handling for any inconsistencies or invalid data in the records. Step 3: Advanced Data Analysis: Perform advanced analysis on employee records stored in the dictionary: Calculate Average Ratings: Calculate the average performance rating for each employee across all evaluation periods. Identify Top Performers: Identify employees with exceptional performance (highest ratings) in each department. After implementing the above question, please solve the below questions: 1. Describe the data validation process implemented for employee records. Provide examples of error handling mechanisms for handling inconsistencies or invalid data. 2. Explain the importance of data standardization in managing employee records. Discuss how standardized data contributes to data integrity and analysis accuracy. 3. Demonstrate the process of calculating average performance ratings for employees and identifying top performers within each department. Provide examples showcasing the implementation of these data analysis techniques.
Chapter3: Data Representation
Section: Chapter Questions
Problem 3RP
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![Create a dictionary named employee_records to
Question 2 in python language: Managing Employee Performance Records and Data Analysis Step 1: Database Creation - Employee Records:
manage employee details. Store the dictionary with employee IDs as keys and dictionaries containing employee information (name, department, performance ratings) as values. employee_
records = {"EMP001": {"name": "John Doe", "department": "Sales", "ratings": [4, 5, 4]}, "EMP002":{"name": "Emma Smith", "department": "Marketing", "ratings": [5, 4, 5] }} Step 2: String Operations
- Data Validation using only (Regex): Implement string operations for data validation and standardization: Validate employee IDs: Ensure IDs follow a specific format (start with 'EMP' followed
by numbers). Implement error handling for invalid or inconsistent IDs. Standardize department names: Validate department names for consistent formatting. Handle potential duplicates or
inconsistencies in department names. Error Handling using (Try - Except): Implement robust error handling for any inconsistencies or invalid data in the records. Step 3: Advanced Data Analysis:
Perform advanced analysis on employee records stored in the dictionary: Calculate Average Ratings: Calculate the average performance rating for each employee across all evaluation
periods. Identify Top Performers: Identify employees with exceptional performance (highest ratings) in each department. After implementing the above question, please solve the below
questions: 1. Describe the data validation process implemented for employee records. Provide examples of error handling mechanisms for handling inconsistencies or invalid data. 2. Explain the
importance of data standardization in managing employee records. Discuss how standardized data contributes to data integrity and analysis accuracy. 3. Demonstrate the process of calculating
average performance ratings for employees and identifying top performers within each department. Provide examples showcasing the implementation of these data analysis techniques.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7c9abf31-5bb3-4895-975b-9e9f51713a9b%2Fdd51200a-3390-4360-aa0b-4bdb619ef384%2Fntok4xc_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Create a dictionary named employee_records to
Question 2 in python language: Managing Employee Performance Records and Data Analysis Step 1: Database Creation - Employee Records:
manage employee details. Store the dictionary with employee IDs as keys and dictionaries containing employee information (name, department, performance ratings) as values. employee_
records = {"EMP001": {"name": "John Doe", "department": "Sales", "ratings": [4, 5, 4]}, "EMP002":{"name": "Emma Smith", "department": "Marketing", "ratings": [5, 4, 5] }} Step 2: String Operations
- Data Validation using only (Regex): Implement string operations for data validation and standardization: Validate employee IDs: Ensure IDs follow a specific format (start with 'EMP' followed
by numbers). Implement error handling for invalid or inconsistent IDs. Standardize department names: Validate department names for consistent formatting. Handle potential duplicates or
inconsistencies in department names. Error Handling using (Try - Except): Implement robust error handling for any inconsistencies or invalid data in the records. Step 3: Advanced Data Analysis:
Perform advanced analysis on employee records stored in the dictionary: Calculate Average Ratings: Calculate the average performance rating for each employee across all evaluation
periods. Identify Top Performers: Identify employees with exceptional performance (highest ratings) in each department. After implementing the above question, please solve the below
questions: 1. Describe the data validation process implemented for employee records. Provide examples of error handling mechanisms for handling inconsistencies or invalid data. 2. Explain the
importance of data standardization in managing employee records. Discuss how standardized data contributes to data integrity and analysis accuracy. 3. Demonstrate the process of calculating
average performance ratings for employees and identifying top performers within each department. Provide examples showcasing the implementation of these data analysis techniques.
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