# Exploratory Data Analysis with Python

(EDA-PYTHON.AJ1)/ISBN:978-1-64459-298-4

This course includes
Lessons
TestPrep
Hand-on Lab

#### Lessons

13+ Lessons | 47+ Exercises | 63+ Quizzes | 80+ Flashcards | 80+ Glossary of terms

#### TestPrep

35+ Pre Assessment Questions | 35+ Post Assessment Questions |

#### Hand on lab

77+ LiveLab | 13+ Video tutorials | 20+ Minutes

# Here's what you will learn

### Lessons 1: Preface

• Who this course is for?
• What this course covers?
• To get the most out of this course
• Conventions used

### Lessons 2: Exploratory Data Analysis Fundamentals

• Understanding data science
• The significance of EDA
• Making sense of data
• Comparing EDA with classical and Bayesian analysis
• Software tools available for EDA
• Getting started with EDA
• Summary

### Lessons 3: Visual Aids for EDA

• Technical requirements
• Line chart
• Bar charts
• Scatter plot
• Area plot and stacked plot
• Pie chart
• Table chart
• Polar chart
• Histogram
• Lollipop chart
• Choosing the best chart
• Other libraries to explore
• Summary

### Lessons 4: Activity: EDA with Personal Email

• Technical requirements
• Data transformation
• Data analysis
• Summary

### Lessons 5: Data Transformation

• Technical requirements
• Background
• Merging database-style dataframes
• Transformation techniques
• Benefits of data transformation
• Summary

### Lessons 6: Descriptive Statistics

• Technical requirements
• Understanding statistics
• Measures of central tendency
• Measures of dispersion
• Summary

### Lessons 7: Grouping Datasets

• Technical requirements
• Understanding groupby()
• Groupby mechanics
• Data aggregation
• Pivot tables and cross-tabulations
• Summary

### Lessons 8: Correlation

• Technical requirements
• Introducing correlation
• Types of analysis
• Discussing multivariate analysis using the Titanic dataset
• Correlation does not imply causation
• Summary

### Lessons 9: Activity: Time Series Analysis

• Technical requirements
• Understanding the time series dataset
• TSA with Open Power System Data
• Summary

### Lessons 10: Hypothesis Testing and Regression

• Hypothesis testing
• p-hacking
• Understanding regression
• Model development and evaluation
• Summary

### Lessons 11: Model Development and Evaluation

• Technical requirements
• Types of machine learning
• Understanding supervised learning
• Understanding unsupervised learning
• Understanding reinforcement learning
• Unified machine learning workflow
• Summary

### Lessons 12: Activity: EDA on Wine Quality Data Analysis

• Technical requirements
• Disclosing the wine quality dataset
• Analyzing red wine
• Analyzing white wine
• Model development and evaluation
• Summary

### Appendix

• String manipulation
• Using pandas vectorized string functions
• Using regular expressions

# Hands-on LAB Activities

### Exploratory Data Analysis Fundamentals

• Styling a Dataframe
• Applying Function to a Dataframe
• Slicing and Subsetting
• Dividing NumPy Arrays
• Inspecting NumPy Arrays
• Defining NumPy arrays
• Selecting rows
• Reading Data from a CSV File
• Creating a Dataframe

### Visual Aids for EDA

• Creating a Line chart
• Creating a Bar Chart
• Creating a Scatter Plot
• Creating a Bubble Chart
• Creating an Area Plot
• Creating a Pie Chart
• Creating a Table Chart
• Creating a Polar Chart
• Adding the Best-Fit Line for the Normal Distribution
• Creating a Histogram
• Creating a Lollipop Chart

### Activity: EDA with Personal Email

• Performing EDA with Email Data
• Extracting Email Using Regex
• Converting a Field to datetime
• Removing NaN Values
• Dropping a Column

### Data Transformation

• Stacking a Dataframe
• Concatenating Dataframes
• Analyzing Dataframes
• Combining Dataframes
• Merging on Index
• Permuting a Dataframe
• Removing Duplicate Data
• Replacing Values
• Interpolating Missing Values
• Backward and Forward Filling
• Handling NaN values
• Counting Missing Values
• Renaming Axis Indexes
• Binning
• Detecting Outliers

### Descriptive Statistics

• Generating a Binomial Distribution Plot
• Generating an Exponential Distribution Plot
• Generating a Normal Distribution Plot
• Generating a Uniform Distribution Plot
• Using Statistical Functions
• Calculating Standard Deviation
• Finding Skewness and Kurtosis
• Creating a Box Plot
• Calculating Inter-Quartile Range

### Grouping Datasets

• Finding Maximum Value for Each Group
• Grouping a Dataset
• Filtering Data
• Applying Aggregation Functions
• Creating a Pivot Table
• Creating a Cross-Tabulation Table

### Correlation

• Calculating Correlation Coefficient

### Activity: Time Series Analysis

• Sampling the Data
• Resampling the Data
• Changing the Index of a Dataframe

### Hypothesis Testing and Regression

• Performing Z-Test
• Calculating the P-Value
• Performing T-test
• Scoring the Model
• Understanding the Linear Regression Model

### Model Development and Evaluation

• Using TfidfVectorizer

### Activity: EDA on Wine Quality Data Analysis

• Plotting a Heatmap
• Visualizing the Data in 3D Form

### Appendix

• Accessing Characters
• String Slicing
• Updating a String
• Escape Sequencing
• Formatting Strings
• Displaying Last 10 items from a Dataframe
• Using String Functions with a Dataframe
• Finding Words from a String
• Counting Full Stops using Regex
• Matching Characters