Exploratory Data Analysis with Pandas and Python 3.x

Exploratory Data Analysis with Pandas and Python 3.x, available at $44.99, has an average rating of 4.05, with 32 lectures, based on 123 reviews, and has 502 subscribers.

You will learn about Improve your understanding of descriptive statistics and apply them over a dataset. Learn how to deal with missing data and outliers to resolve data inconsistencies. Explore various visualization techniques for bivariate and multivariate analysis. Enhance your programming skills and master data exploration and visualization in Python. Learn multidimensional analysis and reduction techniques. Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding This course is ideal for individuals who are This course is for Python developers, data analysts, and IT professionals who want to move toward a career as a full-fledged data scientist/analytics expert; anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from it. It is particularly useful for This course is for Python developers, data analysts, and IT professionals who want to move toward a career as a full-fledged data scientist/analytics expert; anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from it.

Enroll now: Exploratory Data Analysis with Pandas and Python 3.x

Summary

Title: Exploratory Data Analysis with Pandas and Python 3.x

Price: $44.99

Average Rating: 4.05

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Improve your understanding of descriptive statistics and apply them over a dataset.

Learn how to deal with missing data and outliers to resolve data inconsistencies.

Explore various visualization techniques for bivariate and multivariate analysis.

Enhance your programming skills and master data exploration and visualization in Python.

Learn multidimensional analysis and reduction techniques.

Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding

Who Should Attend

This course is for Python developers, data analysts, and IT professionals who want to move toward a career as a full-fledged data scientist/analytics expert; anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from it.

Target Audiences

This course is for Python developers, data analysts, and IT professionals who want to move toward a career as a full-fledged data scientist/analytics expert; anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from it.

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on course shows non-programmers how to process information that’s initially too messy or difficult to access. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently.

This course will take you from Python basics to explore many different types of data. Throughout the course, you will be working with real-world datasets to retrieve insights from data. You’ll be exposed to different kinds of data structure and data-related problems. You’ll learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

About the Author

Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he worked as a Python developer at Qualcomm. He completed his Master’s degree in Computer Science from IIT Delhi, with a specialization in data engineering. His areas of interests include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the Higher-Education industry.

Course Curriculum

Chapter 1: Descriptive Statistics

Lecture 1: The Course Overview

Lecture 2: Basic Statistical Measures

Lecture 3: Variance and Standard Deviation

Lecture 4: Visualizing Statistical Measures

Lecture 5: Calculating Percentiles

Lecture 6: Quartiles and Box Plots

Chapter 2: Dealing with Missing Data

Lecture 1: Finding Missing Values

Lecture 2: Dealing with Missing Values

Lecture 3: Hands-on with Dealing with Missing Values

Lecture 4: Case Study: Missing Data in Titanic Dataset

Chapter 3: Dealing with Outliers

Lecture 1: What are Outliers?

Lecture 2: Using Z-scores to Find Outliers

Lecture 3: Modified Z-scores

Lecture 4: Using IQR to Detect Outliers

Chapter 4: Univariate Analysis

Lecture 1: Types of Variables

Lecture 2: Introduction to Univariate Analysis

Lecture 3: Skewness and Kurtosis

Lecture 4: Univariate Analysis over Olympics Dataset

Chapter 5: Bivariate Analysis

Lecture 1: Introduction to Bivariate Analysis

Lecture 2: Correlation Coefficient

Lecture 3: Scatter Plots and Heatmaps

Lecture 4: Bivariate Analysis: Titanic Dataset

Lecture 5: Bivariate Analysis: Video Game Sales

Chapter 6: Multivariate Analysis

Lecture 1: Introduction to Multivariate Analysis

Lecture 2: Multivariate Analysis over Titanic Dataset

Lecture 3: Multivariate Analysis over Pokemon Dataset

Lecture 4: Simpson’s Paradox

Lecture 5: Correlation Is Not Causation

Chapter 7: Bringing It All Together

Lecture 1: Wine Data Analysis: Initial Setup

Lecture 2: Red Wine Analysis

Lecture 3: White Wine Analysis

Lecture 4: White Wine versus Red Wine: Analysis

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 6 votes

2 stars: 7 votes

3 stars: 17 votes

4 stars: 46 votes

5 stars: 47 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!