Python for Data Analytics and Machine Learning (Oracle) — Lesson 2: Data Cleaning, Exploration, Visualization, and Statistical Reasoning

Learning Objectives

• Explain the architecture, workflow, or business purpose behind missing values, duplicates, outliers, types, and quality rules.
• Apply descriptive statistics, distributions, correlations, and segmentation and Matplotlib-based visual analysis and communication choices in a controlled scenario.
• Validate outcomes and troubleshoot risks involving sampling, leakage awareness, hypotheses, assumptions, and EDA report.

Detailed Topics

• missing values, duplicates, outliers, types, and quality rules
• descriptive statistics, distributions, correlations, and segmentation
• Matplotlib-based visual analysis and communication choices
• sampling, leakage awareness, hypotheses, assumptions, and EDA report

Instructor Demonstration

Demonstrate missing values, duplicates, outliers, types, and quality rules within a customer-churn analytics project taken from raw extracts to a validated and explainable prediction workflow, then capture the exact configuration, command, transaction, report, model, query, code, or process evidence that proves the result for lesson 2.

Hands-On Exercise

Implement a controlled exercise covering descriptive statistics, distributions, correlations, and segmentation and Matplotlib-based visual analysis and communication choices, then introduce and resolve an exception involving sampling, leakage awareness, hypotheses, assumptions, and EDA report while preserving before-and-after evidence.

1. Confirm the environment, identities, inputs, dependencies, risks, and expected outcome.
2. Complete the course-specific configuration, development, analysis, or process workflow.
3. Capture settings, commands, data, outputs, logs, reports, screenshots, calculations, or test results.
4. Compare the observed result with acceptance criteria and document corrective action.

Scenario Analysis

• Identify the technical or business decision that must be made.
• Compare valid approaches and their security, performance, reliability, cost, compliance, and support trade-offs.
• Define rollback, recovery, ownership, communication, and evidence requirements.

Troubleshooting Guidance

Diagnose issues across missing values, duplicates, outliers, types, and quality rules, descriptive statistics, distributions, correlations, and segmentation, Matplotlib-based visual analysis and communication choices, sampling, leakage awareness, hypotheses, assumptions, and EDA report without making an unverified production change. Separate symptoms from causes, gather evidence, test the smallest reversible correction, rerun validation, and record preventive action.

Validation Checklist

• The requirement, scope, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by course-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Data Cleaning, Exploration, Visualization, and Statistical Reasoning in a realistic Python for Data Analytics and Machine Learning (Oracle) scenario and validate the result with documented evidence.