CTU-AI340.AP1

Data Visualization and Exploratory Data Analysis

Practice in 79 Hands-On Labs — nothing to install
5 Interactive Lessons and 82 topics mapped to the official exam objectives

Intermediate Self-paced · 1 year access

79 Hands-On LiveLabs

Practice real IT tasks in guided environments.

Real environments
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5Interactive Lessons

82Topics

79LiveLab

48Videos

106Flashcards

106Glossary of terms

01 / Lessons & labs

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Lessons

5 Interactive Lessons · 82 topics

01 Handling Missing Data and Outliers 22 topics · 21 LiveLab

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
Technical requirements
Understanding statistics
Measures of central tendency
Measures of dispersion
Technical requirements
Introducing correlation
Types of analysis
Discussing multivariate analysis using the Titanic dataset
Outlining Simpson's paradox
Correlation does not imply causation

21 LiveLab in this lesson — see the labs panel →

02 Understanding Data Distributions with Descriptive Statistics 14 topics · 4 LiveLab

Combine Data Sets
Concatenation
Observational Units Across Multiple Tables
Merge Multiple Data Sets
Conclusion
What Is a NaN Value?
Where Do Missing Values Come From?
Working With Missing Data
Pandas Built-In NA Missing
Conclusion
Data Types
Converting Types
Categorical Data
Conclusion

4 LiveLab in this lesson — see the labs panel →

03 Dimensionality Reduction Techniques 19 topics · 7 LiveLab

Simple Linear Regression
Multiple Regression
Models with Categorical Variables
One-Hot Encoding in scikit-learn with Transformer Pipelines
Conclusion
About This Lesson
Logistic Regression (Binary Outcome Variable)
Poisson Regression (Count Outcome Variable)
More Generalized Linear Models
Conclusion
k-Means
Hierarchical Clustering
Conclusion
Technical requirements
Types of machine learning
Understanding supervised learning
Understanding unsupervised learning
Understanding reinforcement learning
Unified machine learning workflow

7 LiveLab in this lesson — see the labs panel →

04 Building Visualizations with Python 11 topics · 26 LiveLab

Introduction
Handling Data with pandas DataFrame
Plotting with pandas and seaborn
Tweaking Plot Parameters
Introduction
Creating Plots that Present Global Patterns in Data
Creating Plots That Present Summary Statistics of Your Data
Introduction
Static versus Interactive Visualization
Applications of Interactive Data Visualizations
Getting Started with Interactive Data Visualizations

26 LiveLab in this lesson — see the labs panel →

05 Advanced Data Cleaning and Outlier Analysis 16 topics · 21 LiveLab

Introduction
Interactive Scatter Plots
Other Interactive Plots in altair
Introduction
Temporal Data
Types of Temporal Data
Understanding the Relation between Temporal Data and Time-Series Data
Examples of Domains That Use Temporal Data
Visualization of Temporal Data
Choosing the Right Aggregation Level for Temporal Data
Resampling in Temporal Data
Interactive Temporal Visualization
Introduction
Data Formatting and Interpretation
Data Visualization
Cheat Sheet for the Visualization Process

21 LiveLab in this lesson — see the labs panel →

Hands-On Labs Our edge

79 LiveLabs

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
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
Calculating Correlation Coefficient
Performing Concatenation Using the concat() Function
Merging Multiple Data Sets Using the .merge() Function
Finding and Cleaning Missing Data
Performing Data Type Conversion
Performing Linear Regression
Performing Multiple Regression
Performing Logistic Regression
Performing Poisson Regression Using the poisson() Function
Performing k-Means Clustering
Using Hierarchical Clustering Algorithms
Using TfidfVectorizer
Creating a User-defined Function
Applying the ceil() Function on a DataFrame Column
Adding a Column to a DataFrame
Applying the describe() Function
Viewing Data from Dataset
Deleting Columns from a DataFrame
Reading Data from a File
Creating a Bar Plot and Calculating the Mean Growth Rate Distribution
Creating Bar Plot Grouped by a Specific Feature
Plotting a Histogram
Tweaking the Plot Parameters of a Grouped Bar Plot
Annotating a Bar Chart
Presenting Data across Time with Multiple Line Plots
Creating a Static Line Plot
Creating a Static Hexagonal Binning Plot
Creating a Static Scatter Chart
Creating a Static Contour Plot
Creating a Static Heatmap
Creating a Linkage in a Static Heatmap
Creating a Static Box Plot
Creating a Static Violin Plot
Creating the Base Static Plot for Interactive Data Visualization
Adding a Slider to the Static Plot
Adding a Hover Tool to a Scatter Plot Using bokeh
Creating an Interactive Scatter Plot
Using the merge() function
Adding Zoom-In and Zoom-Out to a Static Scatter Plot Using altair
Adding Hover and Tooltip Functionality to a Scatter Plot Using altair
Exploring Select and Highlight Functionality on a Scatter Plot Using altair
Performing Selection across Multiple Plots
Performing a Selection Based on the Values of a Feature
Adding the Zoom Feature and Calculating the Mean on a Static Bar Plot
Representing the Mean on a Bar Plot using a Shortcut
Linking a Bar Plot and a Heatmap Dynamically
Adding a Zoom Feature on a Static Heatmap
Creating a Bar Plot and a Heatmap Next to Each Other
Calculating zscore to Find Outliers in Temporal Data
Performing Upsampling and Downsampling in Temporal Data
Using shift and tshift to Shift Time in Data
Adding Zoom-in and Zoom-out Functionality on a Line Plot Using Bokeh
Adding Interactivity to Static Line Plots using Bokeh
Changing the Line Color and Width on a Line Plot
Adding Box Annotations to Find Anomalies in a Dataset
Visualizing Outliers in a Dataset with a Box Plot
Dealing with Outliers
Dealing with Missing Values
Creating a Confusing Visualization

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