Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
使用 Python 进行数据分析
提供方
IBM
课程信息
1,383,162 次近期查看
学生职业成果
42%
完成这些课程后已开始新的职业生涯
41%
通过此课程获得实实在在的工作福利
您将获得的技能
Predictive ModellingPython ProgrammingData AnalysisData Visualization (DataViz)Model Selection
学生职业成果
42%
完成这些课程后已开始新的职业生涯
41%
通过此课程获得实实在在的工作福利
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为13 小时
建议：This course requires approximately two hours a week for six weeks.
英语（English）
字幕：阿拉伯语（Arabic）, 越南语, 韩语, 土耳其语（Turkish）, 英语（English）
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
Importing Datasets
完成时间为 2 小时
6 个视频 （总计 20 分钟）
6 个视频
The Problem1分钟
Understanding the Data2分钟
Python Packages for Data Science2分钟
Importing and Exporting Data in Python4分钟
Getting Started Analyzing Data in Python4分钟
Accessing Databases with Python4分钟
5 个练习
Understanding the Data4分钟
Python Packages for Data Science4分钟
Importing and Exporting Data in Python4分钟
Getting Started Analyzing Data in Python4分钟
Importing Datasets16分钟
完成时间为 2 小时
Data Wrangling
完成时间为 2 小时
6 个视频 （总计 19 分钟）
6 个视频
Dealing with Missing Values in Python6分钟
Data Formatting in Python3分钟
Data Normalization in Python3分钟
Binning in Python1分钟
Turning categorical variables into quantitative variables in Python2分钟
5 个练习
Dealing with Missing Values in Python6分钟
Data Formatting in Python4分钟
Data Normalization in Python4分钟
Turning categorical variables into quantitative variables in Python4分钟
Data Wrangling12分钟
完成时间为 2 小时
Exploratory Data Analysis
完成时间为 2 小时
6 个视频 （总计 19 分钟）
6 个视频
Descriptive Statistics4分钟
GroupBy in Python3分钟
Correlation2分钟
Correlation - Statistics2分钟
Analysis of Variance ANOVA4分钟
5 个练习
Descriptive Statistics4分钟
GroupBy in Python4分钟
Correlation4分钟
Correlation - Statistics4分钟
Exploratory Data Analysis14分钟
完成时间为 2 小时
Model Development
完成时间为 2 小时
6 个视频 （总计 27 分钟）
6 个视频
Linear Regression and Multiple Linear Regression6分钟
Model Evaluation using Visualization4分钟
Polynomial Regression and Pipelines4分钟
Measures for In-Sample Evaluation3分钟
Prediction and Decision Making5分钟
5 个练习
Linear Regression and Multiple Linear Regression4分钟
Model Evaluation using Visualization4分钟
Polynomial Regression and Pipelines4分钟
Measures for In-Sample Evaluation4分钟
Model Development15分钟
审阅
4.7
来自使用 PYTHON 进行数据分析的热门评论
由 RP 提供Apr 19, 2019
perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.
由 AB 提供Feb 12, 2020
Great introduction to data manipulation and analysis for common problems that arise in data science. Also allows you to gain a further understanding of Python syntax, specifically the pandas library.
由 RJ 提供Oct 16, 2018
It will be helpful if a video is added on:\n\n1) how to store multiple results from different models in single dataframe\n\n2) how to automate the process. More example needed on Grid and Pipeline.
由 BD 提供Nov 22, 2019
Really interesting course, if one wants learn programming language. Well designed and structured. Only suggestion is, if the small videos contains example that be really great to understand it well
关于 IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
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