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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!