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
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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!
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