Goal of the Day

Today you will master the principles of Exploratory Data Analysis (EDA) and perform detailed EDA across Titan’s integrated supply chain datasets (procurement, forecasting, logistics, sales, suppliers).
You will link your analysis to critical business questions and prepare visual EDA summaries for reporting and storytelling.

Detailed Tasks with U2xAI Prompts and Interview Preparation Focus

1. Identify Clear Objectives for EDA

  • What to Do:
    Define what you are trying to discover through EDA:
    • Find patterns (e.g., top suppliers, high stockout SKUs)
    • Spot anomalies (e.g., extreme freight costs, unusual sales dips)
    • Understand relationships (e.g., forecast errors vs. stockouts)
  • U2xAI Prompt:
    "How do you define clear objectives before starting exploratory data analysis (EDA) in a supply chain analytics project?"
  • How This Helps for Interviews:
    Shows project thinking: you’ll be ready for questions like, “Before analyzing data, how do you decide what to look for?”
  • Time Recommendation: 30 minutes

2. Study EDA Case Studies and Real-World Examples

  • What to Do:
    Study examples of:Focus on how simple analyses (summary stats, charts) uncover hidden issues.
    • EDA revealing inventory problems
    • EDA discovering demand forecasting inaccuracies
    • EDA identifying supplier delays through data
  • U2xAI Prompt:
    "Give me examples of real-world EDA case studies where simple data exploration helped solve supply chain problems."
  • How This Helps for Interviews:
    You’ll be able to give examples when asked, “Tell me about a time EDA revealed an important insight.”
  • Time Recommendation: 1 hour

3. Conduct Advanced EDA in Python Using pandas and matplotlib

  • What to Do:
    Using the integrated datasets:
    • Calculate basic stats: mean, median, standard deviation
    • Visualize distributions (histograms, boxplots)
    • Create scatter plots to study relationships (e.g., Forecast Error vs Stockouts)
    • Detect outliers (e.g., extremely high lead times, abnormally high freight costs)
  • U2xAI Prompt:
    "Show me Python code for conducting EDA using pandas and matplotlib, including basic statistics, histograms, boxplots, and outlier detection."
  • How This Helps for Interviews:
    Technical assessments often ask for simple EDA code and interpretations. Being fluent here makes you stand out.
  • Time Recommendation: 2.5 hours
  • What to Do:
    Prepare a table:
    • Observation (e.g., 20% of SKUs have very high stockouts)
    • Business Impact (e.g., Lost Sales Risk)
    • Possible Root Cause (e.g., inaccurate forecasts)
  • U2xAI Prompt:
    "Help me prepare a simple EDA insights table linking findings to business risks and opportunities in supply chain analysis."
  • How This Helps for Interviews:
    Shows that you don’t just analyze — you link data patterns to business problems, a highly prized skill.
  • Time Recommendation: 1 hour

5. Document EDA Charts, Observations, and Summaries

  • What to Do:
    Create a visual EDA Summary Document:
    • 4–5 Key Charts (e.g., top 10 suppliers by spend, SKU fill rates)
    • Summary Observations for each chart (2–3 sentences)
    • Highlight important anomalies found
  • U2xAI Prompt:
    "Suggest a clean format for documenting EDA charts, key findings, and business interpretations for a supply chain analytics project."
  • How This Helps for Interviews:
    Shows you can present data visually and narratively, not just run analysis behind the scenes — a huge advantage during final interviews and presentations.
  • Time Recommendation: 1 hour

6. Plan Insights Extraction for Final Reporting

  • What to Do:
    Write 5–7 big insights you want to highlight in the final report based on today’s EDA:
    • Procurement savings opportunities
    • Inventory improvement areas
    • Freight optimization possibilities
    • Supplier risk insights
    • Forecasting adjustments needed
  • U2xAI Prompt:
    "Help me list 5 important types of insights that should be extracted after exploratory data analysis of a full supply chain dataset."
  • How This Helps for Interviews:
    You’ll easily answer questions like, “What business insights did you extract from your supply chain data project?”
  • Time Recommendation: 30 minutes

Step-by-Step BUILDUP Application for Day 14

  • Breakdown:
    Set goals: Understand hidden patterns, outliers, relationships in integrated datasets.
  • Understand:
    Study EDA examples and understand why EDA is crucial before modeling or decision-making.
  • Implement:
    Perform detailed EDA using pandas and matplotlib — generate summary stats and plots.
  • Link:
    Relate EDA findings to business problems (stockouts, supplier issues, fulfillment gaps).
  • Document:
    Create an EDA Summary Report with charts and 2–3 line business interpretations per chart.
  • Upgrade Progress:
    Plan how to integrate these insights into tomorrow’s final report and dashboard storytelling.

Deliverables for Today

  • EDA Insights Table (Findings ➔ Business Implications)
  • Python Scripts/Excel Sheets of EDA
  • EDA Charts (Histograms, Boxplots, Scatter Plots)
  • EDA Summary Document with Key Observations
  • List of Insights for Final Reporting

Practice Interview Questions for Day 14

  1. What is the role of EDA in the data analysis process?
  2. How do you spot outliers and anomalies during EDA?
  3. What techniques would you use to find patterns and relationships across supply chain datasets?
  4. After EDA, how do you decide which insights are most important for business reporting?
  5. Can you explain one real-world example where EDA helped solve a supply chain problem?

Bonus Practice:
Prepare a 90-second pitch answering:
"After completing EDA on Titan’s datasets, what three major risks or opportunities did you uncover?"