Chapter 13: Data Science Workflows

Data science combines programming, statistics, and domain knowledge to extract insights from data. This chapter presents practical workflows and patterns you can use to analyze data, build simple models, and create recommendation systems using the Python skills you've learned.

Complete Data Analysis Workflow

A typical data science workflow follows these steps:

This pattern applies to most data science projects: load, explore, clean, transform, analyze.

Statistical Analysis Patterns

Calculate key statistics to understand your data:

Understanding distribution helps identify patterns and outliers.

Correlation Analysis

Explore relationships between variables:

Correlation reveals relationships: positive (increase together), negative (inverse), or zero (unrelated).

Data Normalization

Scale features for consistent comparison:

Normalization ensures all features contribute equally to analysis.

Feature Engineering

Create new features from existing data:

Good features improve analysis and model performance.

Outlier Detection

Identify unusual values in your data:

Outliers can be data errors or interesting anomalies worth investigating.

Similarity Measures: Tanimoto Coefficient

Measure similarity between sets for recommendation systems:

Tanimoto coefficient is perfect for comparing sparse binary features like purchase histories.

Building a Simple Recommender

Use similarity to recommend products:

This content-based filtering approach recommends items with similar attributes.

Time Series Analysis Basics

Analyze data over time:

Rolling averages smooth out noise to reveal trends.

Data Aggregation Patterns

Summarize data effectively:

Aggregation reveals patterns hidden in individual records.

Cross-Validation Concept

Validate model performance reliably:

Cross-validation prevents overfitting by testing on unseen data.

Data Quality Checks

Ensure data integrity:

Data quality checks catch issues early in the pipeline.

Building a Classification Rule

Create simple decision rules:

Rule-based systems are interpretable and effective for simple classification.

Practical Example: Product Ranking

Build a scoring system:

Scoring systems combine multiple criteria into actionable rankings.

Quiz: Test Your Knowledge

Summary

Data science workflows combine programming, statistics, and domain knowledge. Start with exploration and cleaning, create meaningful features, apply appropriate analysis techniques, and validate results. Simple techniques like correlation analysis, normalization, and rule-based systems solve many real-world problems without complex machine learning.

Key takeaways:

• Follow the Load → Explore → Clean → Transform → Analyze workflow
• Use statistics to understand distribution and relationships
• Normalize features for fair comparison
• Engineer features to capture domain knowledge
• Build simple recommenders with similarity measures
• Validate quality and detect outliers
• Start simple: rule-based systems often suffice

Combining these techniques with Python's data science libraries enables you to extract actionable insights from data.

Related Courses

Take your data science skills to the next level with these courses from Pragmatic AI Labs:

Machine Learning Fundamentals

Build your ML foundation:

• Supervised vs unsupervised learning
• Classification and regression algorithms
• Model evaluation and validation
• Feature engineering techniques
• Bias-variance tradeoff

Explore ML Fundamentals →

Advanced Statistical Analysis

Master statistical methods:

• Hypothesis testing
• A/B testing and experimentation
• Time series analysis
• Bayesian statistics
• Causal inference

Explore Statistical Analysis →

Recommendation Systems

Build personalized experiences:

• Collaborative filtering
• Content-based filtering
• Hybrid recommendation approaches
• Cold start problem solutions
• Evaluation metrics (precision@k, NDCG)

Explore Recommendation Systems →

Feature Engineering Mastery

Create powerful features:

• Domain-driven feature creation
• Automated feature generation
• Feature selection techniques
• Handling categorical variables
• Time-based features

Explore Feature Engineering →

Ready to become a data science professional? Check out our Data Science Career Track for a complete path from fundamentals through machine learning and deployment.