jm53-sps/jm-bus336projects

Repository of my projects for the BUS336 Course

Starbucks Data Manipulation and Analysis Project

This project demonstrates the application of Python and pandas for data manipulation and analysis. The focus was on handling a raw CSV file, cleaning the data, and deriving meaningful insights through various steps. The project showcases key skills like correcting data inconsistencies, handling missing values, and saving the processed data for further analysis.

Steps and Methodology

1. Saving the Initial CSV File
2. Loading Data with pandas
3. Exploring the Data Structure
4. Handling Missing Values (NaN)
5. Correcting Column Names
6. Sorting and Filtering Data
7. Grouping Data for Analysis
8. Analyzing Data Trends and Patterns
9. Saving the Cleaned Data
10. Reflecting on the Insights Gained

Reflection

Through this project, I gained valuable experience in data manipulation and analysis using Python and pandas. The first task involved saving the initial CSV file, marking the starting point of data analysis. This was a foundational step, as importing pandas as pd with enabled me to work with a raw text dataset.

Next, I began to explore and manipulate the data. Pandas provided a powerful toolkit for this. It was fascinating to see how these operations could quickly transform a cluttered dataset into something more organized and insightful by running certain code.

With the data now more manageable, I focused on cleaning it up—addressing missing values (NaN) and correcting column names. Handling NaNs was crucial for ensuring that my analysis would be accurate and reliable. By filling or dropping these missing values, I could maintain the integrity of the dataset. Correcting the column names was not an essential step, but one that made the data more visually presentable to the client changing this column title to match the standard set in other columns.

Through this experience, I’ve deepened my understanding of how data can be leveraged to inform decisions and drive actions. It’s a reminder that data analysis is not just about crunching numbers—it's about uncovering stories and insights that can make a real impact.

Reflecting on the course, I gained valuable experience in data manipulation and analysis using Python and pandas. The course aimed to build foundational skills in Python, explore data and create business analytics analyses. The Signature Term Project, effectively met these objectives by guiding me through the stages of data preparation and manipulation.

Before starting the course, I anticipated learning the basics of Python and understanding its applications in data analysis. I expected to gain skills that would help me manage and interpret data more effectively. What I’m taking away is a deeper understanding of how to use Python to handle real-world data and communicate findings effectively.

Python and programming initially seemed abstract and complex, but they proved to be powerful tools for making sense of business data. Learning basics to Python Data Analysis was both rewarding and debugging code required patience and problem-solving skills. Python’s simplicity and readability were advantageous, but understanding the nuances of pandas and data manipulation presented a learning curve. DataCamp's interactive exercises were instrumental in bridging these gaps, making complex concepts more accessible. Without the hints provided in the PDF, I would have struggled to complete the project, relying heavily on ChatGPT for guidance. Professor Foy’s exact code examples were particularly helpful in navigating through the project. Otherwise I might not have known the code since it was not exact in DataCamp excersises since we did each step one by one but did not bridge together until the project.

While the course covered many essential aspects, I would have appreciated more in-depth exploration of advanced data visualization techniques. Understanding GitHub seems crucial for future collaboration and was helpful in project presentation. Moving forward, I intend to study Python further through using DataCamp on my own and practical applications to deepen my skills with SQL. My friend who is a Data Analyst used DataCamp to learn SQL and land her current position!