This device is not compatible.
You will learn to:
Use Julia for Exploratory Data Analysis.
Use specialized libraries to generate various charts.
Skills
Data Analysis
Data Plotting
Data Cleaning
Prerequisites
Hands-on experience with Julia
Basic understanding of data analytics and visualization
Basic understanding of data handling
Technologies
Julia
Plotly
Project Description
Exploratory Data Analysis (EDA) is a statistical approach used for finding meaningful relationships in the data. EDA lies under the broader domain of data analytics. It uses various tools, including data plots, algorithms, and statistical models, to make sense of the data.
In this project, we’ll use EDA to draw meaningful deductions from the Titanic dataset. Our purpose will be to find out how different factors affect a passenger’s survival. We’ll start by importing and cleaning the dataset, filling in the missing values through data imputation, and displaying various plots to visualize the data.
We’ll implement this project using the Julia language, which is a good choice for computational projects due to its fast numeric computation.
Project Tasks
1
Getting Started
Task 1: Import Libraries
Task 2: Import the Dataset
Task 3: Display Basic Statistical Information
2
Data Cleaning
Task 4: Filter Unique Entries in the Dataset
Task 5: Drop Unnecessary Columns
Task 6: Find the Column-Wise Count of Missing Data
Task 7: Perform Data Imputation
3
Data Visualization
Task 8: Find Unique Column Entries in the Dataset
Task 9: Create Bar Plots
Task 10: Create Box Plots
Task 11: Create Violin Plots
Task 12: Plot the Correlation Matrix
Task 13: Create the Correlation Plot
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.