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Optimize inventory management of a store by cleaning its data(Python)

A.Overview of the Project:

In this project, I took on the challenge of addressing critical issues faced by BottleNeck, a prestigious online wine merchant, regarding its Enterprise Resource Planning (ERP) system. The tools in use were characterized as artisanal, leading to complexities in stock management and limited visibility into online sales analysis due to restricted access to the back-office. The choice of Python is justified due to its extensive open-source ecosystem, particularly beneficial for data analysis, data visualization, and cleaning tasks. The email from Laurent includes necessary data exports, along with additional information on alcohol characteristics (caracs_vins.csv).

The project aims to present the results during the next COPIL meeting, showcasing progress and insights gained. A presentation template (modèle de présentation) and additional slides for personal reflection are provided to assist in summarizing the work and lessons learned.

Laurent emphasizes the importance of a comprehensive dataset after merging the files, intending to share it with colleagues eagerly awaiting the results.

B.Steps:

The primary mission is divided into three key points:

1. Data Integration:

  • I merged data from the ERP export (erp.xlsx) and the online store product table export (web.xlsx).

  • To establish connections between product references in the ERP and the online store, I utilized Sylvie's Excel table (liaison.xlsx).

    2.Revenue Analysis:

  • I calculated product-wise revenue and the total revenue generated online after successfully integrating the data.

    3.Price Analysis:

  • I investigated potential errors in product prices by analyzing the data.

  • Sylvie had already initiated work on this aspect using a Jupyter Notebook, recommending the use of Python and Pandas for efficient data cleaning.

C.Resources:

  • Google Colab
  • Google Colab - Apprendre Python - Tutoriel pour Vrai Débutants(In French)
  • Google colab(In French)
  • Initiate yourself to Python for data analysis(In French)

    SKILLS:

    HARD SKILLS:

    • Data Analysis: Proficiency in merging and analyzing data from different sources (erp.xlsx, web.xlsx).

    • Python Programming: Utilizing Python, especially Pandas, for data cleaning, analysis, and error detection.

    • Data Visualization: Skills in creating interactive and quality visualizations using Plotly.

    • Excel Skills: Competency in handling Excel files for data manipulation and integration.

    • Jupyter Notebooks: Ability to work with and continue tasks using Jupyter Notebooks.

      SOFT SKILLS:

    • Communication: Effective communication to present findings during the COPIL meeting and reporting progress to the team.

    • Team Collaboration: Working collaboratively with Sylvie's previous work and sharing the dataset with colleagues.

    • Problem-Solving: Identifying and addressing potential errors in product prices requires problem-solving skills.

    • Attention to Detail: Sylvie's note on naming issues highlights the importance of attention to detail in data work.

    • Adaptability: Being open to learning and adapting to new tools and technologies, as recommended by Sylvie.

      REFERENCES:

  • Learn programming with Python(In French)
  • Documentation of the Pandas Library
  • Documentation of the Plotly Express
  • The guide to the lightweight markup language(In French)
  • 10 Pandas tips that will make your work more efficient(In French)
  • Personality Traits of A Great Data Scientist