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Applied Data Science Chapter 1: Introduction to Applied Data Science Overview of Applied Data Science Setting Up the Environment Review of Basic Concepts Chapter 2: Data Collection and Cleaning Data Sources and Acquisition Data Cleaning Techniques Handling Missing Data Chapter 3: Exploratory Data Analysis Descriptive Statistics Data Visualization Techniques Identifying Patterns and Outliers Chapter 4: Advanced Analytical Techniques Regression Analysis Classification Methods Clustering Techniques Chapter 5: Machine Learning Applications Model Selection and Evaluation Feature Engineering Deployment Strategies Chapter 6: Communicating Results Creating Effective Visualizations Reporting and Documentation Storytelling with Data

Exploratory Data Analysis

Exploratory Data Analysis (EDA) serves as an indispensable bridge between raw data and meaningful insights in any data science workflow. As you navigate through this chapter, you'll delve into techniques that empower you to unveil patterns, identify anomalies, and test hypotheses. Through EDA, data sets unveil their narratives, providing a fundamental understanding crucial for more advanced analysis.

You'll acquire the skills to leverage visualization tools and statistical measures to effectively summarize data characteristics. This chapter will guide you through interpreting distributions, exploring relationships between variables, and detecting potential outliers. By the end, you'll have sharpened your ability to formulate insightful questions and evaluate data sets with a critical eye, paving the way for more sophisticated modeling and predictive analytics.

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