This video provides a practical session on data analysis, troubleshooting, and professional development.

Session Summary

• Revision and Data Preparation:
  • The participants reviewed the previous class, which covered using PivotTables, cleaning data, and creating a basic dashboard.
  • The group worked on a new dataset (“shopping trends”) in CSV format, which required data cleaning steps such as removing duplicates, checking for blank cells, and verifying data types.
  • Due to the large size of the dataset, participants discussed utilizing Power Query for efficient cleaning when Excel lags.
• Data Analysis Objectives:
  • The instructor emphasized the importance of “data recognizance”—understanding the problem being solved before beginning analysis.
  • The group analyzed the relationship between product categories, purchase amounts, and payment methods.
  • Key findings indicated that “credit card” was the most frequently used payment method overall, though preferences varied by gender and location.
• Dashboard Creation and Reporting:
  • Participants built a dashboard featuring charts based on their findings, incorporating slicers (e.g., location) to enable interactive data filtering.
  • The instructor outlined the five standard steps for an analytical report: extraction, defining the problem, data cleaning, analysis/visualization, and final presentation.
• Professional Development:
  • The instructor introduced “datascienceportfolio.io” as an easy-to-use platform for students to host their projects and link them directly to their LinkedIn profiles.
  • The session concluded with instructions to install Power BI in preparation for upcoming lessons.