🔍 MemoLearning Exploratory Data Analysis

Discover patterns, anomalies, and insights through systematic data exploration

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Exploratory Data Analysis Curriculum

11
Core Units
~80
EDA Techniques
30+
Visualization Methods
15+
Statistical Tests
1

EDA Fundamentals

Learn the principles and methodology of exploratory data analysis for effective data investigation.

  • What is exploratory data analysis
  • EDA vs confirmatory analysis
  • The EDA process and workflow
  • Forming hypotheses and questions
  • Iterative exploration approach
  • Documentation and reproducibility
  • Tools and environments
  • Best practices and pitfalls
2

Data Profiling and Overview

Get familiar with your dataset through comprehensive profiling and initial data assessment.

  • Dataset structure examination
  • Data types and formats
  • Missing value patterns
  • Data quality assessment
  • Summary statistics overview
  • Memory usage and performance
  • Data source documentation
  • Initial data sanity checks
3

Univariate Analysis

Analyze individual variables to understand their distributions, central tendencies, and variability.

  • Distribution analysis
  • Central tendency measures
  • Variability and spread
  • Skewness and kurtosis
  • Outlier detection
  • Frequency distributions
  • Percentiles and quantiles
  • Variable transformation needs
4

Bivariate Analysis

Explore relationships between pairs of variables using correlation and association measures.

  • Correlation analysis
  • Scatter plot interpretation
  • Linear and non-linear relationships
  • Categorical variable associations
  • Cross-tabulation analysis
  • Statistical significance testing
  • Confounding variables
  • Simpson's paradox
5

Multivariate Analysis

Understand complex relationships among multiple variables and identify patterns in high-dimensional data.

  • Correlation matrices
  • Principal component analysis
  • Cluster analysis
  • Dimensionality reduction
  • Feature interactions
  • Multicollinearity detection
  • Variable selection techniques
  • High-dimensional visualization
6

Data Visualization for EDA

Create effective visualizations to uncover patterns and communicate findings during exploration.

  • Choosing appropriate chart types
  • Distribution plots (histograms, density)
  • Relationship plots (scatter, correlation)
  • Categorical data visualization
  • Time series plots
  • Small multiples and faceting
  • Interactive exploration tools
  • Annotation and storytelling
7

Anomaly and Outlier Detection

Identify unusual observations that may indicate errors, fraud, or interesting phenomena.

  • Statistical outlier methods
  • Isolation forest technique
  • Local outlier factor
  • Clustering-based detection
  • Time series anomalies
  • Multivariate outliers
  • Domain-specific anomalies
  • Outlier treatment strategies
8

Time Series Exploration

Analyze temporal data patterns including trends, seasonality, and cyclical behaviors.

  • Time series decomposition
  • Trend analysis
  • Seasonal pattern detection
  • Autocorrelation analysis
  • Stationarity testing
  • Change point detection
  • Lag analysis
  • Forecasting implications
9

Text Data Exploration

Explore and analyze textual data through frequency analysis, sentiment, and content patterns.

  • Text preprocessing for EDA
  • Word frequency analysis
  • N-gram exploration
  • Text length distributions
  • Sentiment analysis
  • Topic modeling overview
  • Word clouds and visualization
  • Text similarity measures
10

Statistical Testing in EDA

Apply statistical tests to validate findings and quantify the significance of observed patterns.

  • Hypothesis testing framework
  • Normality tests
  • Correlation significance
  • Chi-square tests
  • T-tests and ANOVA
  • Non-parametric tests
  • Multiple testing corrections
  • Effect size interpretation
11

EDA Reporting and Communication

Create comprehensive EDA reports that effectively communicate insights and guide next steps.

  • EDA report structure
  • Key findings summarization
  • Visual storytelling
  • Data quality documentation
  • Hypothesis generation
  • Recommendations for modeling
  • Stakeholder communication
  • Reproducible analysis

Unit 1: EDA Fundamentals

Learn the principles and methodology of exploratory data analysis for effective data investigation.

What is Exploratory Data Analysis

Understand EDA as an approach for analyzing datasets to summarize main characteristics with visual methods.

Detective Work Pattern Discovery Hypothesis Generation
EDA is detective work on your data - looking for clues, patterns, and unexpected findings that guide deeper analysis.

EDA vs Confirmatory Analysis

Distinguish between exploratory analysis (discovering patterns) and confirmatory analysis (testing hypotheses).

# EDA: What patterns exist in the data?
df.describe()
df.corr()

# Confirmatory: Is this pattern significant?
stats.ttest_ind(group1, group2)

The EDA Process and Workflow

Learn the systematic approach to conducting thorough exploratory data analysis.

1. Data Overview → 2. Quality Check → 3. Univariate → 4. Bivariate → 5. Multivariate → 6. Insights
# Systematic EDA workflow
def eda_workflow(df):
  data_overview(df)
  quality_check(df)
  univariate_analysis(df)
  bivariate_analysis(df)
  generate_insights(df)

Forming Hypotheses and Questions

Develop meaningful questions and hypotheses to guide your exploratory analysis effectively.

Good EDA questions: "What factors influence sales?", "Are there seasonal patterns?", "What defines customer segments?"
# Question-driven exploration
# Q: What drives customer churn?
churn_by_feature = df.groupby('feature')['churn'].mean()
churn_by_feature.plot(kind='bar')

Iterative Exploration Approach

Embrace the iterative nature of EDA, where each finding leads to new questions and deeper investigation.

# Iterative discovery process
# 1. Find pattern → 2. Ask why → 3. Investigate deeper
# 4. Validate finding → 5. Generate new questions

Documentation and Reproducibility

Maintain clear documentation of your exploration process and ensure your analysis can be reproduced.

# Jupyter notebook with markdown cells
# Clear variable naming
# Version control with git
# Requirements.txt for dependencies

Tools and Environments

Choose appropriate tools and set up efficient environments for exploratory data analysis.

Jupyter Pandas Seaborn Plotly
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Best Practices and Pitfalls

Learn common EDA best practices and avoid typical pitfalls that can lead to incorrect conclusions.

✓ Start simple, then go complex
✓ Question everything
✗ Don't assume causation from correlation
✗ Don't ignore data quality issues

Unit 2: Data Profiling and Overview

Get familiar with your dataset through comprehensive profiling and initial data assessment.

Dataset Structure Examination

Understand the basic structure, dimensions, and organization of your dataset.

# Basic dataset information
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
df.info()
df.head()

Data Types and Formats

Examine data types to ensure they're appropriate for analysis and identify conversion needs.

# Check data types
df.dtypes
df.select_dtypes(include=['object'])
df.select_dtypes(include=['number'])

Missing Value Patterns

Identify and visualize missing data patterns to understand completeness and potential biases.

import missingno as msno
# Missing data overview
df.isnull().sum()
msno.matrix(df)
msno.heatmap(df)

Data Quality Assessment

Evaluate overall data quality including accuracy, completeness, consistency, and validity.

Completeness Accuracy Consistency Validity
# Quality checks
duplicates = df.duplicated().sum()
unique_counts = df.nunique()
# Check for impossible values
negative_ages = (df['age'] < 0).sum()

Summary Statistics Overview

Generate comprehensive summary statistics to understand central tendencies and distributions.

# Comprehensive summary
df.describe(include='all')
df.describe(percentiles=[.1, .25, .5, .75, .9])
# Custom summary function
df.agg(['count', 'mean', 'std', 'min', 'max'])

Memory Usage and Performance

Assess memory usage and identify opportunities for optimization in large datasets.

# Memory usage analysis
df.info(memory_usage='deep')
df.memory