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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-visualizations-using-python-with-data-preparation/
课程评论：没有评论
课程名称：使用 Python 进行数据可视化与数据准备 **课程概述：** 本课程旨在教授学员如何使用 Python 进行数据可视化，并涵盖数据准备的关键步骤。课程首先阐述了学习数据分析与数据科学的五大理由：培养解决问题的能力、高需求性、数据分析的普及性、日益增长的重要性以及跨学科的技能融合。课程强调，数据可视化是数据挖掘流程（CRISP-DM）中的“数据理解”阶段，而数据处理则属于“数据准备”阶段。学习本课程需要具备一定的 Python 编程基础，可以参考讲师的另一门 Python 基础课程。此外，本课程还将教授应用统计中的 Python 编程。 **课程内容：** 课程内容涵盖从入门到高级的数据可视化和数据处理技术，具体包括： * **入门：** 介绍课程的开始部分。 * **数据挖掘流程：** 概述数据挖掘的步骤。 * **数据处理：** * 下载和读取数据集。 * 使用 `pandas` 库进行数据探索，包括 `DF.head()`, `DF.tail()`, `DF.describe()`。 * 数据选择：选择变量、选择行、同时选择变量和行。 * 数据操作：删除变量、追加行、排序变量、重命名变量。 * 数据分组：使用 `GroupBy` 进行聚合分析。 * 缺失值处理：检测、替换和删除缺失值。 * 重复值处理：删除重复项。 * **数据可视化（使用 Matplotlib 和 Seaborn 库）：** * **基础图表：** * 条形图 (Bar Chart) * 直方图 (Histogram) * 折线图 (Line Chart) * 多折线图 (Multiple Line Chart) * 饼图 (Pie Chart) * 箱线图 (Box Plot) * 散点图 (Scatterplot) * 散点图矩阵 (Scatterplot Matrix) * **Seaborn 增强图表：** * 使用 Seaborn 绘制条形图、直方图、折线图、散点图、类别图、箱线图、散点图矩阵。 * **保存图表：** 将图表保存为图片。 * **交互式图表：** 介绍创建交互式图表的方法。 **学习路径与认证：** 学员可以按照以下路径学习，并有机会通过 EMHAcademy 的考试获得“SVBook Certified Data Miner using Python”证书： 1. Create Your Calculator: Learn Python Programming Basics Fast (R Basics) 2. Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation) 3. Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation, in the future) 4. Machine Learning with Python (Modeling and Evaluation)
Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. Gain problem solving skillsThe ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life. 2. High demandData Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase. 3. Analytics is everywhereData is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.4. It's only becoming more importantWith the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities. 5. A range of related skillsThe great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities. This is a bite-size course to learn Python Programming for Data Visualization. In CRISP-DM data mining process, Data Visualization is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage. You will need to know some Python programming, and you can learn Python programming from my "Create Your Calculator: Learn Python Programming Basics Fast" course. You will learn Python Programming for applied statistics. You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using Python certificate: - Create Your Calculator: Learn Python Programming Basics Fast (R Basics)- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation, in the future)- Machine Learning with Python (Modeling and Evaluation)ContentGetting StartedGetting Started 2Getting Started 3Data Mining ProcessDownload Data setRead Data setBar ChartHistogramLine ChartMultiple Line ChartPie ChartBox PlotScatterplotScatterplot MatrixSave To ImageBar Chart with SeabornHistogram with SeabornLine Chart with SeabornScatterplot with SeabornCategorical PLot with SeabornBoxplot with SeabornScatterplot Matrix with SeabornSave To ImageInteractive ChartsInteractive ChartsInteractive ChartsInteractive ChartsData Processing: DF.head()Data Processing: DF.tail()Data Processing: DF.describe()Data Processing: Select VariablesData Processing: Select RowsData Processing: Select Variables and RowsData Processing: Remove VariablesData Processing: Append RowsData Processing: Sort VariablesData Processing: Rename VariablesData Processing: GroupBYData Processing: Remove Missing ValuesData Processing: Is THere Missing ValuesData Processing: Replace Missing ValuesData Processing: Remove Duplicates
