How to Solve Data Cleanup and Data Analysis Assignments like a Pro

Understanding the Assignment Requirements

Before tackling a statistics assignment like the Glassdoor Cleanup and Analysis project, it's essential to thoroughly understand the task requirements. Break down the project into manageable sections, focusing on key objectives such as calculating average salaries by firm size, sector, or rating. Clarifying these requirements early ensures efficient workflow and accurate results. The first step in solving any data analysis assignment is to thoroughly understand the requirements. In the case of a Glassdoor Cleanup and Analysis assignment, the key tasks typically involve:

1. Cleaning the raw data to remove unnecessary or incorrect entries.
2. Splitting columns to create new variables for analysis.
3. Calculating summary statistics based on specific categories, such as firm size or sector.
4. Sorting and presenting the data in a meaningful way using tables and charts.
5. Using pivot tables to automate data aggregation and analysis.

The assignment generally includes multiple deliverables, such as:

• Tables summarizing salary information by firm size and sector.
• Lists of average midpoint salaries by company name, sorted by the number of job postings.
• Summaries of salary information by company rating.
• Analysis of job titles to identify trends in salaries for different roles.

Understanding these deliverables is essential before diving into the data cleaning and transformation process.

Step 1: Importing and Familiarizing Yourself with the Data

Start by importing the raw dataset into your Excel or statistical software. Familiarize yourself with the file structure, identifying key columns like salary estimates, company names, and job titles. Pay close attention to inconsistencies or missing values, as understanding the data layout is crucial for effective analysis The first task is to import the CSV file into an Excel workbook. After importing the data, spend some time familiarizing yourself with the structure of the dataset. Look for:

• Columns that contain useful information, such as Job Title, Salary Estimate, Company Name, and Location.
• Irregularities in the data, such as missing values, text in numeric columns, or redundant information.

Key Points to Check:

• Are there any rows with missing salary information?
• Are there salary estimates recorded as hourly wages instead of annual salaries?
• Does the company name column include additional information, such as ratings, that needs to be cleaned?

Once you have a clear understanding of the data, you can move on to cleaning and transforming it.

Step 2: Data Cleaning and Transformation

Cleaning the dataset is a vital step to ensure accurate analysis. Remove irrelevant rows, split combined columns (like Salary Estimate into HighEst and LowEst), and convert text fields into numerical values. These transformations make the data usable and ready for further exploration. Data cleaning is one of the most critical steps in any data analysis assignment. The Glassdoor dataset will likely contain several inconsistencies that need to be addressed before analysis can begin.

Tasks to Perform:

• Splitting the Salary Estimate Column
• The Salary Estimate column often contains both the low and high salary estimates as a text string (e.g., “$50,000 - $70,000”).
• Use Excel’s Text-to-Columns feature to split this column into two separate columns: LowEst and HighEst.
• Converting Text to Numeric Values
• Ensure that the new LowEst and HighEst columns contain numeric values by removing any non-numeric characters, such as dollar signs or commas.
• Creating a Midpoint Salary Column
• Calculate the midpoint salary by taking the average of the LowEst and HighEst columns:

MidEst = (LowEst + HighEst) / 2

• Handling Missing and Irregular Data
• Delete rows with missing salary information or hourly wages, as they cannot be used in the analysis.
• Cleaning the Company Name Column
• Remove any ratings or additional information from the Company Name column to ensure consistency.
• Splitting the Location Column
• Use Text-to-Columns to split the Location column into City and State.

Step 3: Conducting the Required Analyses

After cleaning the data, create pivot tables or use functions to calculate average salaries and job counts by categories such as firm size, sector, or company rating. This step involves summarizing the data to extract meaningful insights, ensuring it meets the assignment requirements. After cleaning the data, you can begin the analysis. This section outlines how to complete each required analysis using Excel’s tools.

1. Salaries by Firm Size

Create a pivot table to calculate the average low, high, and midpoint salaries, along with the count of job postings for each firm size category. Use the following steps:

• Insert a Pivot Table in a new worksheet.
• Drag the Size field to the Rows area.
• Drag the LowEst, HighEst, and MidEst fields to the Values area.
• Drag the Job Title field to the Values area and set it to count.

Ensure the table is sorted in ascending order of firm size.

2. Salaries by Sector

To calculate salaries by sector:

• Create a new pivot table.
• Drag the Sector field to the Rows area.
• Drag the salary fields (LowEst, HighEst, MidEst) and Job Title to the Values area.
• Sort the table alphabetically by sector.

3. Salaries by Company

For this analysis:

• Create another pivot table.
• Drag the Company Name field to the Rows area.
• Drag the MidEst field to the Values area.
• Drag the Job Title field to the Values area and set it to count.
• Sort the table in descending order by the number of job postings.

4. Salaries by Rating

To analyze salaries by company rating:

• Create a pivot table.
• Drag the Rating field to the Rows area.
• Drag the MidEst field to the Values area.
• Drag the Job Title field to the Values area and set it to count.
• Sort the table by rating in descending order.

Create a meaningful chart to visualize the results.

5. Job Title Analysis

For the final analysis, you need to filter job titles to identify those containing specific keywords, such as “analyst,” “scientist,” “engineer,” and “manager.”

• Insert new columns to flag job titles containing these keywords using formulas like:

=IF(ISNUMBER(SEARCH("analyst", [Job Title])),"Yes","No")

• Use pivot tables to calculate the average midpoint salary and the number of job postings for each job category.

Step 4: Presenting the Final Results

Organize your findings into clear, well-labeled tables and charts. Use separate worksheets for each analysis to keep your report structured. A polished presentation of the results enhances clarity and demonstrates your understanding of data analysis processes. Ensure your final Excel workbook contains the following worksheets:

• GlassdoorFinal – the cleaned and transformed data.
• SalariesBySize – summary table for firm size categories.
• SalariesBySector – summary table for sector categories.
• SalariesByCompany – average midpoint salaries by company.
• SalariesByRating – salary analysis by company rating.
• SalariesByJob – analysis of salaries by job title.

Conclusion

Assignments like Glassdoor Cleanup and Analysis are excellent opportunities to apply data cleaning and analysis skills. By following a structured approach, you can effectively transform raw data into meaningful insights. The key to success lies in understanding the assignment requirements, performing thorough data cleaning, and leveraging Excel’s tools to conduct the required analyses.

If you're struggling with similar assignments, seeking help with data analysis assignments from experts can make a significant difference. Our team at FinanceAssignmentHelp.com offers personalized guidance on data cleaning, Excel pivot tables, and insightful analysis techniques. Whether it’s a Glassdoor project or another complex dataset, we’re here to help you excel in your academic journey!