Project Showcase

Technologies:

• Jupyter Notebook: For documenting and running Python code.
• Python: Core language for analysis.
• Matplotlib: For creating bar charts, pie charts, box plots, line plots, scatter plots, and performing linear regression analysis.
• Numpy: For numerical calculations and handling arrays.
• SciPy: For performing statistical analysis.

Highlighted Skills:

• Data Cleaning: : Removed duplicate entries by mouse ID and timepoint.
• Statistical Analysis: Calculated summary statistics (mean, median, variance, standard deviation, and standard error of the mean) for tumor volumes across different drug regimens.
• Data Visualization: Created bar charts, pie charts, box plots, line plots, and scatter plots to present key findings visually.

Technologies:

• Python: Core language for analysis.
• Pandas: For data cleaning, manipulation, and analysis.
• Jupyter Notebook: For interactive data analysis and visualization.

Highlighted Skills:

• Data Preparation: Cleaned and organized the dataset to ensure the integrity of the analysis.
• District Summary: Calculated key metrics for the entire district, including total schools, total students, budget, and performance metrics for math and reading scores.
• School-Level Analysis: Generated a detailed summary of performance for each school, including average test scores, passing rates, and budgetary information.
• Performance Comparisons: Identified top and bottom performing schools based on overall passing percentages.
• Grade-Level Analysis: Segmented math and reading scores by grade level to observe trends within schools.
• Spending, Size, & Type Analysis: : Explored how school budget, size, and type relate to student performance, generating key insights about factors impacting success.

Technologies:

• Python: Core language for scripting and analysis.
• VSCode IDE For running the python script.
• Text File Export:: Results exported to .txt files for both financial and election data analysis.

Highlighted Skills:

• Data Preparation: Wrote scripts to read and clean financial and election data from CSV files.
• Financial Analysis:
  
  • Calculated total months, total net profits/losses, and changes in profit/loss over time.
  • Identified the greatest increase and decrease in profits and determined average monthly changes.
  • Printed results to the terminal and exported the analysis to a text file for record-keeping.
• Election Results Analysis:
  
  • Processed election data to count total votes and identify each candidate’s vote count and percentage.
  • Determined the election winner based on the highest vote count.
  • Printed results to the terminal and exported the analysis to a text file for record-keeping.

Technologies:

• Excel: Data analysis, processing, and visualization.

Highlighted Skills:

• Data Preparation: Cleaned and organized sales data for analysis using Query Editor.
• Filter Implementation: Applied filters for Region and Date to focus on relevant data subsets.
• KPI Calculation:: Developed formulas for key metrics:
  • Total Revenue, Previous Year Revenue, Previous Month Revenue, Year-over-Year % Change, and Month-over-Month % Change.
• Store and Product Performance Analysis: Assessed the performance of stores and identified top and bottom-performing products using advanced Excel functions like SUMIFS, VLOOKUP, and nested IF statements.
• Visualization and Reporting: Created dashboards and reports to present findings and support strategic decision-making.

Technologies:

• Excel: Data analysis, processing, and visualization.

Highlighted Skills:

• Data Cleaning: Processed and organized labor statistics data in Excel for accurate analysis.
• Metric Calculations:
  • Average Wage by Industry: Calculated average annual wages using industry-specific data.
  • Employees by Industry: Total employees calculated using.
  • Wage & Employee Trends: Analyzed wage trends using.
  • Comparisons by State: Calculated employees per 1000 capita and total employees.
• Visualization: Created charts to illustrate wage disparities and employment levels, enhancing the understanding of regional workforce dynamics.
• Reporting: Compiled findings into a report, detailing insights on labor market conditions and providing recommendations for workforce planning.

Technologies:

• Excel:: PData analysis, processing, and visualization.
  • Pivot Tables: For summarizing and calculating key metrics.

Highlighted Skills:

• Data Cleaning: Ensured data quality by processing booking data in Excel for accurate analysis.
• Trend Analysis: Investigated patterns related to booking statuses and revenue generation.
• Visualization:: Created visual representations of data using Excel charts to highlight key insights and trends.
• Reporting: Compiled a report detailing findings and actionable recommendations for hotel management.

Technologies:

• VBA (Visual Basic for Applications): Primary tool used for scripting the automation.
• Microsoft Excel: The environment where the stock data is processed and analyzed.
• Conditional Formatting: Applied within Excel to highlight significant changes visually.

Highlighted Skills:

• VBA Programming: Writing and executing VBA scripts to automate data extraction and analysis tasks in Excel.
• Data Processing: Extracting and manipulating stock data to calculate and format key financial metrics.
• Excel Automation: Automating tasks across multiple worksheets in Excel using VBA. Conditional Formatting: Applying conditional formatting to visualize data changes effectively.
• Results Reporting: Generating and formatting summary reports and visual representations of financial data within Excel.

Role/Contributions:

• Designed and Developed VBA Script: Wrote a robust VBA macro that automates the extraction and analysis of stock data, calculating key metrics such as Yearly Change, Percent Change, and Total Stock Volume.
• Applied Conditional Formatting: Automated the formatting of data to highlight positive and negative changes in stock performance.
• Error Handling and Optimization: Improved the script’s performance by ensuring it operates seamlessly across all worksheets in the workbook.
• Comprehensive Data Analysis: Implemented logic to calculate the greatest percentage increase, decrease, and total volume across the dataset.

Technologies:

• Microsoft Excel: Primary tool for data manipulation, analysis, and visualization.
  • Conditional Formatting: Used to highlight campaign outcomes and percentage funded.
  • Pivot Tables: Created to aggregate and summarize campaign success by category, sub-category, and date.
  • Formulas: COUNTIFS, AVERAGE, MEDIAN, MIN, MAX, VAR.P, STDEV.P used for goal analysis and statistical calculations.
  • Data Visualization: Stacked-column pivot charts and line graphs for goal vs success rate analysis.
• Microsoft Word: For generating a final report that summarizes the findings and provides conclusions.

Highlighted Skills:

• Data Preparation:
  • Cleaned and transformed the crowdfunding data, splitting categories into parent and sub-categories, converting Unix timestamps into readable dates, and calculating key metrics such as percentage funded and average donation.
• Conditional Formatting:
  • Applied conditional formatting to visualize campaign outcomes and funding percentages, making it easier to identify trends.
• Pivot Tables and Charts:
  • Built pivot tables to summarize campaign outcomes by category and sub-category.
  • Created a stacked-column pivot chart to show the distribution of successful, failed, and canceled campaigns filterable by country and category.
  • Developed a line chart to analyze the relationship between campaign goal amounts and their respective success, failure, or cancellation rates.
• Goal Analysis:
  • Performed a detailed analysis of success rates across different campaign goal ranges, using COUNTIFS formulas to count successful, failed, and canceled projects within specific goal ranges.
• Statistical Analysis:
  • Calculated summary statistics (mean, median, min, max, variance, standard deviation) for the number of backers in successful and unsuccessful campaigns to assess variability and central tendency.
• Reporting:
  • Calculated summary statistics (mean, median, min, max, variance, standard deviation) for the number of backers in successful and unsuccessful campaigns to assess variability and central tendency.