My attached work is below. You can also view it through this link.

docs.google.com

In this project, I analyzed employee data that included attributes such as name, age, department, salary, and years of experience. Using spreadsheet functions, I calculated a cumulative average salary across the dataset to observe overall salary trends. Additionally, I filtered and computed the average salary specifically for the HR department ($5,208.33) to benchmark against other roles. A final Boolean column was added to indicate whether each employee's salary was above the HR department's average. This allowed for quick identification of salary outliers across departments and highlighted pay distribution patterns. The exercise demonstrates my skills in data organization, formula application (e.g., AVERAGE, AVERAGEIF, IF), and logical analysis in Excel or Google Sheets.

Data 2

After completing the initial analysis of individual employee data in Sheet 1, I created a pivot table in Sheet 2 to summarize the salary information by department. This pivot table was built directly from the data in Sheet 1, where each employee’s salary, department, and other details were recorded. The pivot aggregates the total salary per department, giving a clear overview of how salary resources are distributed across teams. For example, it shows that the Sales department has the highest total salary ($97,000), followed by Finance ($86,000), and so on. The pivot table provides a concise summary that supports higher-level insights, budget planning, and departmental comparisons.

Data 3

My attached work is below. You can also view it through this link.

https://docs.google.com/spreadsheets/d/1M-lssW01SxTDo3zYeZgf95bdqEZDvaBGiuY7bPeHoKU/edit?usp=sharing

In this assignment, I analyzed the engagement performance of three LinkedIn posts, evaluating metrics such as likes, comments, reposts, and engagement rate. The posts included a mix of text and image content, each focusing on different topics:

1. "ChatGPT is old news, because of the new DeepSeek" (Text) – 522,200% engagement rate, which resonated by educating and empowering the audience with timely insights on AI technology.

2. "BREAKING: DeepSeek just released an image model" (Image) – 493,300% engagement rate, tapping into the viral potential of disruptive, affordable, and accessible new tech.

3. "LinkedIn isn’t broken. But the rules have changed." (Image) – 396,200% engagement rate, engaging the professional community with insights on evolving LinkedIn strategies.

Through this analysis, I identified key trends, including the higher engagement rates of AI-related and disruptive technology content, as well as the superior performance of image-based posts. This project demonstrated my ability to analyze social media data, extract actionable insights, and provide recommendations for enhancing future content strategies and audience engagement.

My attached work is below. You can also view it through this link.

https://docs.google.com/spreadsheets/d/1ZjAxgYsfER-DCT9TNNzq0zyPGhlYnRpsGBVDlCUnr78/edit?usp=sharing

In this sample, I provide a list of 30 CEOs in the US, including their names, company names, websites, Facebook URLs, LinkedIn URLs, Instagram URLs, phone numbers, and location. I utilize both LinkedIn and Google to find different CEOs across the country and then I store the collected information in Google Sheets.

Here's another sample of Lead generator.

I've searched through 300+ rental homes in LA. Since I can only screenshot 40, you can't see the rest. I made this from scratch. I used Yellow Pages to find rental homes and Insta Data Scraper to extract all available rental homes in LA. Then, I used Excel to store the information.