Exploratory Data Analysis With Python and Pandas
Apply practical Exploratory Data Analysis (EDA) techniques on any tabular dataset using Python packages such as Pandas and Numpy.
Produce data visualizations using Seaborn and Matplotlib
In this 2-hour long project-based course, you will learn how to perform Exploratory Data Analysis (EDA) in Python. You will use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn etc. to conduct univariate analysis, bivariate analysis, correlation analysis and identify and handle duplicate/missing data. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
您要培养的技能
分步进行学习
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Initial Data Exploration: Read in data, take a glimpse at a few rows, calculate some summary statistics.
Univariate Analysis: Analyze continuous and categorical variables, one variable at a time.
Bivariate Analysis: Looking at the relationship between two variables at a time.
Identify and Handling Duplicate and Missing Data: Find and remove duplicate rows, and replace missing values with their mean and mode.
Correlation Analysis: Looking at the correlation of numerical variables in the dataset and interpreting the numbers.
指导项目工作原理
您的工作空间就是浏览器中的云桌面，无需下载
在分屏视频中，您的授课教师会为您提供分步指导
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来自EXPLORATORY DATA ANALYSIS WITH PYTHON AND PANDAS的热门评论
All the explanations were very clear and well explained. This was an excellent course! Please continue building up on this series and move to basic machine learning algorithms using regression.
Straight forward, easy to follow along and understand. Great for someone who needs a refresher or a basic overview on Data Analysis with Python and Pandas
Amazing course. Full concept coverage with easy to understand codes. I do not think i will have to look at any other material to get EDA done for my projects
This was an amazing course! In all honesty I didn't expect to learn this much from a 2-hour project, but Bassim proved me wrong! This was great.
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