Exploratory Data Analysis with Python (10 credits)


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Exploratory Data Analysis (EDA) involves multiple techniques that extract meaningful information and valuable insights from the data. The main purpose of EDA is to investigate datasets to reveal the underlying structures and the challenges and opportunities that come with data without attempting to apply any machine learning model. This course will introduce the student to the practical knowledge and the main pillars of EDA, including data exploration, preparation, visualization, relationships, and clustering using Python programming language. Apart from the intuitions, the student will get familiar with how EDA steps are implemented by various Python libraries such as NumPy, Pandas, and Matplotlib. By the end of this course, the student will be prepared to enter the fantastic world of data analysis towards amazing job opportunities in the industry.

Learning outcomes

  • Learn the general framework of EDA and why it is important
  • Learn the general concepts of descriptive statistics and how to extract them using the Pandas library
  • Learn how to plot various visualizations to extract meaningful insights from data using Matplotlib and Seaborn libraries
  • Get familiar with standard practices of data preparation and hypothesis testing
  • Learn how to perform missing value imputation, outlier detection, and feature engineering using NumPy and Pandas
  • Get familiar with the general framework of data relationships and learn the intuitions behind correlation analysis and association rules
  • Learn how to perform dimensionality reduction to represent and visualize data in lower-dimensional space
  • Learn how to identify group patterns and perform clustering using the k-Means method

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 1.1. Introduction to Data Science
  • 1.2. Data Science Workflow
  • 1.3. Data
  • 1.4. Sources of Data
  • 1.5. What Is Exploratory Data Analysis?
  • 1.6. Python Libraries for EDA

Chapter 3: Describing Data

  • 3.1. Introduction
  • 3.2. Observations and Variables
  • 3.3. Categorical Variables
  • 3.4. Quantitative Variables
  • 3.5. Central Tendency
  • 3.6. Data Variability
  • 3.7. Distribution Functions

Chapter 4: Importing Data

  • 4.1. Introduction
  • 4.2. Vectors and Matrices
  • 4.3. NumPy Arrays
  • 4.4. Working with NumPy Arrays
  • 4.5. Loading Data with NumPy
  • 4.6. Pandas Series
  • 4.7. Working with Series
  • 4.8. Pandas DataFrame
  • 4.9. Working with DataFrames

Chapter 5: Data Exploration

  • 5.1. Extracting Descriptive Statistics
  • 5.2. Extracting Descriptive Statistics: Preliminaries
  • 5.3. Extracting Descriptive Statistics: Implementation
  • 5.4. Mathematical Operations on DataFrame
  • 5.5. Applying Functions to DataFrame
  • 5.6. Querying a DataFrame
  • 5.7. Filtering Data
  • 5.8. Groupby
  • 5.9. Cross Tabulation

Chapter 6: Data Visualization

  • 6.1. Univariate Analysis
  • 6.2. Histogram
  • 6.3. Frequency Polygons
  • 6.4. Boxplot
  • 6.5. Bar Chart
  • 6.6. Pie Chart
  • 6.7. Bivariate Analysis
  • 6.8. Scatter Plot
  • 6.9. Hexbins
  • 6.10. Stacked Column Chart

Chapter 7: Data Preparation

  • 7.1. Introduction
  • 7.2. Incorrect Values and Categories
  • 7.3. Feature Engineering: Creating New Features
  • 7.4. Outlier Detection: Univariant
  • 7.5. Outlier Detection: Multivariant
  • 7.6. Removing Missing Values
  • 7.7. Imputing Missing Values: Constant Imputation
  • 7.8. Imputing Missing Values: K-NN Imputation
  • 7.9. Feature Encoding: Label Encoding
  • 7.10. Feature Encoding: One-Hot Encoding
  • 7.11. Feature Scaling: Normalization
  • 7.12. Feature Scaling: Standardization

Chapter 8: Data Relationships

  • 8.1. Introduction
  • 8.2. Covariance Matrix
  • 8.3. Correlation
  • 8.4. Heatmap of Correlation Matrix
  • 8.5. Non-linear Relationship
  • 8.6. Hypothesis Testing

Chapter 9: Identifying and Understanding Groups

  • 9.1. Introduction
  • 9.2. Clustering
  • 9.3. Hierarchical Clustering
  • 9.4. K-Means Clustering

Chapter 10: Next Steps

  • 10.1. What’s More?
  • 10.2. EDA for Text Data
  • 10.3. Model Development and Evaluation

Chapter 11: Final Tasks

  • 11.1. Project
  • 11.2. Self-study Essay
  • 11.3. Congrats! You did it!

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