This document discusses exploring data by performing preliminary analysis to understand it. It identifies explaining the importance of exploring data and identifying exploration methods as learning outcomes. It recommends exploring data through visualizing techniques like histograms, heat maps, boxplots, line graphs and scatter plots to find correlations, general trends and outliers in order to gain an understanding of the data before conducting further analysis. The goal of data exploration is to gain data understanding to inform subsequent analysis.

1. Step 2-A:
Exploring Data

2. After this video you will be able to..
• Explain the importance of exploring data
• Identify methods to perform preliminary
analysis of your data

3. ACQUIRE PREPARE ANALYZE REPORT ACT
Step 2-A: Explore
Step 2-B: Pre-process
Computational Big Data Science
Big Data Engineering

4. Why Explore?
Goal: Understand your data

5. Why Explore?
Correlations

6. Why Explore?
Correlations
General trends

7. Why Explore?
Correlations
General trends
Outliers

8. Describe Your Data

9. Visualize Your Data

10. Visualize Your Data
Heat maps

11. Visualize Your Data
Histogram
Heat maps

12. Visualize Your Data
Histogram
Heat maps
Boxplots

13. Visualize Your Data
Histogram
Heat maps
Line graphs
Boxplots

14. Visualize Your Data
Histogram
Heat maps
Scatter plots
Line graphs
Boxplots

15. Data
Exploration
Data
Understanding
Informed
Analysis