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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-analysis-with-python
课程评论：没有评论
课程名称：使用Python进行数据分析 课程概述：本课程旨在教您如何使用Python进行数据分析。课程将从Python基础知识开始，逐步探索不同类型的数据。您将学习如何准备数据进行分析，执行简单的统计分析，创建有意义的数据可视化，从数据中预测未来趋势等内容！ 课程内容包括： 1) 数据集导入 2) 数据清理 3) 数据框操作 4) 数据总结 5) 构建机器学习回归模型 6) 构建数据管道 课程将通过讲座、实验和作业进行授课，涵盖以下部分： 数据分析库：您将学习使用Pandas、Numpy和Scipy库来处理样本数据集。我们将介绍开源库Pandas，并用它加载、操作、分析和可视化有趣的数据集。之后，您将接触另一个开源库scikit-learn，我们将使用其机器学习算法来构建智能模型并做出有趣的预测。 如果您选择参加本课程并获得Coursera证书，您还将获得IBM数字徽章。 限时优惠：订阅仅需每月39美元，以获取评分材料和证书。 课程大纲： 1) 数据集导入：学习如何理解和准备数据集，使用Python包导入和导出数据以进行分析。 2) 数据整理：学习清理、转换和准备数据的方法，处理缺失值、格式化和标准化数据。 3) 探索性数据分析：学习计算基本的描述性统计信息，利用相关方法比较变量，理解数据分布。 4) 模型开发：学习构建和评估多种回归模型，简化工作流程，使用关键指标评估模型表现。 5) 模型评估与优化：学习如何评估和调整模型以获得最佳性能，减少过拟合。 6) 最终作业：应用完整的数据分析流程，完成实践项目和同伴评审的最终项目，巩固技能并应对真实数据分析挑战。 通过完成此课程，您将掌握数据分析的关键概念，为未来的职业生涯打下坚实基础。
Name:Importing Data Sets
Description:In this module, you will develop foundational skills in Python-based data analysis by learning how to understand and prepare datasets, utilize essential Python packages, and import and export data for analysis. You’ll gain hands-on experience using tools like Pandas, Numpy, and SQLite to begin analyzing real-world datasets, including a laptop pricing dataset. In addition, you’ll be provided with a cheat sheet that serves as a handy reference throughout this learning journey.
Name:Data Wrangling
Description:In this module, you will enhance your data wrangling skills using Python by learning techniques to clean, transform, and prepare data for analysis. You’ll work with real-world datasets to handle missing values, format and normalize data, bin numerical values, and convert categorical variables. Through guided labs, you’ll apply these skills to both the Laptop and Used Car Pricing datasets. You will also receive a cheat sheet to support you as a quick reference throughout the learning process.
Name: Exploratory Data Analysis
Description:In this module, you will build essential skills in exploratory data analysis (EDA) using Python. You will learn to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn how to group data to better visualize patterns, use the Pearson correlation method to compare two continuous numerical variables, and apply the chi-square test to assess associations between categorical variables and interpret the results. Further, you will be provided with a cheat sheet that will serve as a quick reference for commonly used EDA functions and methods.
Name:Model Development
Description:In this module, you will explore the fundamentals of model development in data analysis using Python. You’ll learn how to build, visualize, and evaluate different types of regression models, including simple linear, multiple linear, and polynomial regression models, along with pipelines to streamline your workflows. You’ll also interpret model performance using key metrics and visual tools such as kernel density estimation (KDE) plots. Hands-on labs will reinforce your learning with practical datasets like used car and laptop pricing. Additionally, the cheat sheet will serve as a quick reference for building and evaluating predictive models.
Name:Model Evaluation and Refinement
Description:In this module, you will refine your predictive modeling skills by learning how to evaluate, tune, and select models for optimal performance. You’ll explore concepts such as overfitting, underfitting, and hyperparameter tuning using grid search. You will also learn about using ridge regression to regularize and reduce standard errors to prevent overfitting a regression model. Through hands-on labs, you'll apply these techniques to real datasets to build robust, generalizable models. A cheat sheet is included to guide you in choosing the right tools and metrics for model optimization.
Name:Final Assignment
Description:In this final module, you will apply the complete data analysis workflow, from importing and cleaning data to building and evaluating models on real-world datasets. You’ll complete a hands-on practice project and a peer-reviewed final project based on datasets related to insurance costs and house pricing. For the final project, you will take on the role of a Data Analyst at a real estate investment trust looking to invest in residential properties. You’ll work with a dataset containing detailed information on house prices and various property features, and your task will be to analyze the data and predict housing market values. These projects are designed to consolidate your skills and prepare you for real-world data analysis challenges. Finally, you will demonstrate comprehension and application of key data analysis concepts through a final exam.
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! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
