Goal of the Day

Gain a working understanding of the data analysis lifecycle, and get hands-on exposure to the core tools you'll use throughout this project: Excel, SQL, Python, and Looker Studio (or Power BI).
You will set up your toolkits today and begin connecting each tool with parts of the supply chain workflow.

Detailed Tasks with U2xAI Prompts and Interview Preparation Focus

1. Study the Data Analysis Life Cycle

  • What to Do:
    Learn about the six typical phases: data collection, cleaning, exploration (EDA), analysis/modeling, interpretation, and reporting.
  • U2xAI Prompt:
    "Explain the data analysis life cycle with an example relevant to supply chain analytics."
  • How This Helps for Interviews:
    Interviewers frequently ask, “Walk me through how you handle a data project.” This gives you a structured framework to answer such questions clearly.
  • Time Recommendation: 1 hour

2. Understand the Purpose and Strengths of Each Tool

  • What to Do:
    Explore what each tool is best used for in the project:
    • Excel: Cleaning, quick calculations, small-scale visualizations.
    • SQL: Querying structured data (filtering, joining, aggregating).
    • Python: Automation, large-scale cleaning, statistical modeling.
    • Looker Studio / Power BI: Dashboards and business reporting.
  • U2xAI Prompt:
    "Compare the roles of Excel, SQL, Python, and Looker Studio in a supply chain analytics project."
  • How This Helps for Interviews:
    Interviewers often ask: “Which tools do you prefer and why?” or “What tool would you use for X task?” This gives you confident answers based on purpose.
  • Time Recommendation: 1 hour

3. Practice Loading a Sample Dataset in Each Tool

  • What to Do:
    Load a CSV file in Excel, import it into SQL (e.g., SQLite or MySQL), read it in Python using pandas, and upload to Looker Studio or Power BI.
  • U2xAI Prompt:
    "Show me step-by-step how to load a CSV file in Excel, SQL (e.g., SQLite or MySQL), Python (pandas), and Looker Studio."
  • How This Helps for Interviews:
    Technical assessments often start with: “Can you load this dataset and show me X?” This task gets you hands-on and technically ready.
  • Time Recommendation: 1.5 hours
  • What to Do:
    Make a table mapping tools to lifecycle stages and supply chain domains. Example: SQL for raw data filtering → Inventory KPIs; Python for modeling → Forecasting.
  • U2xAI Prompt:
    "Create a table that maps Excel, SQL, Python, and Looker Studio to the data lifecycle stages and supply chain use cases."
  • How This Helps for Interviews:
    You’ll be able to answer cross-functional tool questions confidently. For example: “When would you use Python over SQL in logistics analysis?”
  • Time Recommendation: 45 minutes

5. Document Your Tool Setup Process and Observations

  • What to Do:
    Write a summary for each tool:
    • Was it easy to use?
    • What strengths/weaknesses did you notice?
    • Where would you use this in the Titan supply chain project?
  • U2xAI Prompt:
    "Help me write a short journal entry on setting up Excel, SQL, Python, and Looker Studio for a supply chain data project."
  • How This Helps for Interviews:
    Being able to communicate setup challenges and tool selection rationale shows technical awareness and self-management.
  • Time Recommendation: 30 minutes

6. Plan Your Tool Practice for the Week

  • What to Do:
    Based on today’s experience, write down one practice task you’ll do later in the week with each tool (e.g., run a pivot in Excel, build a bar chart in Power BI, etc.).
  • U2xAI Prompt:
    "Suggest one beginner-level supply chain data task I can practice in each of these tools: Excel, SQL, Python, Looker Studio."
  • How This Helps for Interviews:
    Shows initiative and planning ability. Great for “Tell me how you stay updated with your tools” type questions.
  • Time Recommendation: 15 minutes

Step-by-Step BUILDUP Application for Day 2

  • Breakdown:
    Understand how different tools support the data analysis lifecycle.
  • Understand:
    Learn about Excel, SQL, Python, Looker Studio, and how they’re used in supply chain projects.
  • Implement:
    Load the same dataset in all four tools.
  • Link:
    Map tools to project stages and departments (e.g., use Excel in Procurement, Python in Forecasting, etc.).
  • Document:
    Write journal notes on tool strengths, challenges, and use cases.
  • Upgrade Progress:
    Plan one mini-practice task for each tool later this week.

Deliverables for Today

  • Table mapping tools to supply chain stages and lifecycle phases
  • Notes on each tool’s strength and ease-of-use
  • One-page tool setup journal
  • Practice plan for each tool (Excel, SQL, Python, Looker Studio)

Practice Interview Questions for Day 2

  1. Walk me through the data analysis life cycle using a supply chain example.
  2. What is the difference between using SQL and Python for data cleaning?
  3. In your supply chain project, when would you use Looker Studio instead of Excel?
  4. Which tool do you find most intuitive and why?
  5. How do you decide which tool to use for a given data problem?

Bonus Tip:
Try explaining the data lifecycle using a real example like “Procurement Orders” from Titan. This makes your response stand out in interviews.