Univariate Exploratory Data Analysis in Python

This course focuses on exploring some of the most  commonly applied univariate analysis technique on both continuous data and categorical data, along with their Python implementation.

  • icon-videos 1 Video
  • icon-data 1 Coding Case
  • icon-reading 1 Reading
  • icon-quiz 1 Quiz
Abstract Blue Tech Background
  • 1 bar graph
    Difficulty: Beginner

    No prior knowledge or experience with data required.

  • Asset 1
    Duration: Approximately 3 hours

Course Overview

  • Exploratory data analysis (EDA) is done by data scientists to analyze, explore, summarize and visualize some of the key characteristics in the data.
  • Univariate analysis is a kind of EDA that involves analyzing one variable at a time.
  • Learn how to apply univariate analysis techniques using Python.
  • Gain practical experience by applying the coding skills and understanding in a real-world business case.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Exploratory Data Analysis

Interpretation of Visualizations

Kurtosis

Skewness

Univariate Analysis

Segmented Univariate Analysis

Associated Learning Tracks

Syllabus

In this module, we will understand how to analyse the data, one variable at a time. Learn how to implement the different techniques in Python and apply your learnings on the hands-on datacase.

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

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