Practical Analysis Through Business Data

Day 1: Data Awareness and Thinking

• What data analysis is and what it is not
• Types of business data and common sources
• Framing questions before touching the data
• Ethical handling and responsible use

Day 2: Organizing Raw Information

• Identifying relevant fields and records
• Detecting duplicates and errors
• Creating tidy, consistent structures
• Documenting steps for transparency

Day 3: Describing Data Clearly

• Summaries such as totals and averages
• Understanding spread and variability
• Spotting outliers carefully
• Choosing appropriate summaries for different data types

Day 4: Exploring Relationships

• Comparing groups and categories
• Looking for associations between factors
• Distinguishing coincidence from meaning
• Avoiding common interpretation traps

Day 5: Working With Time-Based Data

• Identifying trends and seasonality
• Separating signal from noise
• Simple forecasting concepts
• Visual techniques for showing change over time

Day 6: Visual Communication

• Selecting the right visual for the message
• Designing clear charts and tables
• Reducing clutter and distraction
• Explaining visuals in plain language

Day 7: Data Quality and Reliability

• Sources of bias and mismeasurement
• Checking consistency across datasets
• Handling missing or incomplete values
• Recording assumptions openly

Day 8: From Analysis to Insight

• Connecting results to business questions
• Translating numbers into practical actions
• Comparing alternatives fairly
• Explaining risks and uncertainty

Day 9: Reporting and Presentation

• Structuring reports for quick understanding
• Writing concise explanations and key messages
• Building simple dashboards and summaries
• Tailoring communication for different audiences

Day 10: Integration and Application

• Designing a repeatable analysis workflow
• Planning improvement of current reporting practices
• Setting personal development goals
• Building habits that sustain data-driven culture