|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-data-visualization-with-python-plotly-and-power-bi/
课程评论：没有评论
课程名称：使用Python、Plotly和Power BI学习数据可视化 课程概述：在本课程中，您将学习如何使用Microsoft Power BI、Python和Plotly创建各种数据可视化图表。许多专业人士需要具备数据可视化技能，以便绘制各种图表，从数据集中找出关键洞察，包括市场营销、销售、开发者、分析师和数据科学家等职业。为了能够有效地对数据进行建模和呈现，创造性地将复杂的数据用直观的可视化图表展现出来，专业人士不仅依赖基本的图表，如柱状图、折线图和饼图等，而是需要创建一些自定义图表来解决复杂问题。 本课程将介绍如何通过编写少量Python代码来创建大多数自定义或高级可视化图表。您将学习使用Python库（如pandas、matplotlib和seaborn）进行以下概念和可视化图表的创建：安装Python包及定义路径、使用matplotlib创建折线图、添加标签并创建虚线散点图、使用seaborn绘制小提琴图、更多小提琴图、不规则图、箱形图、线性图或对齐图等。 数据可视化使任务变得更加便捷高效。通过可视化图表，我们可以轻松识别异常值、空值、随机值、不同记录、日期格式、空间数据的敏感性、字符串和字符编码等。此外，您将学习不同类型数据的不同图表表示，如分类数据、数值数据、空间数据和文本数据等，包括：柱状图（水平和垂直）、折线图、饼图、甜甜圈图、散点图、分组柱状图（水平和垂直）、分段柱状图（水平和垂直）、时间序列图、太阳图、蜡烛图、OHLC图、气泡图、点图、多条折线图等。 数据科学家通常对图表的关注不足，主要集中于数值计算，这可能会导致误导。数据可视化是实现数据分析或数据科学目标的重要步骤，以获得有意义的洞察，或在机器学习中构建准确的模型。您在本课程中学到的技能可以广泛应用于与数据科学、数据分析、商业智能和机器学习相关的各个领域。
In this course you will learn to create various types of Data Visualization charts using Microsoft Power BI, Python and Plotly. There are a wide range of professionals who require data visualization skills to plot various charts to find critical insights from the dataset. From Marketing and sales professionals to Developers, Analysts and Data Scientists, a large number of professionals require some kind of knowledge to adequately model and represent data into creative visuals that makes it easy and intuitive to understand the complex data value in form for comparable and easy to understand visual charts. Most of the basic charts such as bar, line, pie, tree map and other charts in Power BI and other visualization software are just inefficient to represent various kinds of data with complex information. Professionals just don't rely on few basic charts, rather they could create some custom chart to solve complex problem. Most of the custom or advanced visualization charts can be created by writing few lines of python code. In this course, you will be learning following concepts and visualization charts using python libraries such as pandas, matplotlib and seaborn-Installing python packages and defining pathCreating a Line chart with matplotlibPutting labels and creating dashed scatterplotViolin chart with seabornMore on Violin chartStripplotBoxplotLmplot or align plotData visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more. Moreover, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.Bar Charts (Horizontal and Vertical)Line ChartsPie ChartsDonut ChartsScatter ChartsGrouped Bar Chart (Horizontal and Vertical)Segmented Bar Chart (Horizontal and Vertical)Time and series ChartSunburst ChartCandlestick ChartOHLC ChartsBubble ChartsDot ChartsMultiple Line Charts and so on.Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model. The skills you learn in this course can be used in various domains related to data science and data analytics to business intelligence and machine learning.
