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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-data-analytics-complete-bootcamp-by-takenmind/
课程评论：没有评论
课程名称：Python数据分析与可视化培训营 概述：欢迎来到由TakenMind提供的数据分析分析培训营！如果你对学习如何处理数据巨量（zetabytes）并分析数据以促进商业增长感兴趣，那么你来对地方了。对于初学者来说，现在正是学习这门课程的最佳时机。根据Indeed的研究，现今平均数据科学家的年薪为$123,000。数据科学在近几年爆发式发展，带来了越来越多的收益。 本课程以财务分析为基础，涵盖以下几个关键概念： - Python基础 - 使用Pandas进行高效数据分析 - 利用NumPy进行高速数值处理 - 使用Matplotlib进行数据可视化 - 利用Pandas进行数据操作和分析 - Seaborn数据可视化 - 具体案例分析 你将学习如何使用Python进行数据分析，从Python基础知识开始，逐步探索多种数据类型。课程内容包括数据准备、简单统计分析、有意义的数据可视化、根据数据预测未来趋势等。 你将学习到： - 导入数据集 - 清理和准备数据进行分析 - 操作Pandas DataFrame - 数据汇总 - 使用scikit-learn构建机器学习模型 - 建立数据管道 Python数据分析与可视化课程通过讲座、实践实验和作业进行授课。课程包括： - 数据分析库：学习使用Pandas DataFrames、NumPy多维数组和SciPy库处理各种数据集。我们将介绍开源库Pandas，学习如何加载、操作、分析和可视化数据集。随后，我们将介绍另一开源库scikit-learn，利用其机器学习算法构建智能模型并进行预测。 这个课程是数据分析的良好起点，适合希望提升数据科学技能的学习者。
Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind. Are you interested to learn how zetabytes of data are processed by top tech companies to analyse data inorder to boost their business growth? Well, for a beginner you are at the right place and this is the most probably the right time for you to learn this. The average data scientist today earns $123,000 a year, according to Indeed research. But the operating term here is "today," since data science has paid increasing dividends since it really burst into business consciousness in recent years.This course has its base on financial Analysis and the following concepts are covered:Python FundamentalsPandas for Efficient Data AnalysisNumPy for High Speed Numerical ProcessingMatplotlib for Data VisualizationPandas for Data Manipulation and AnalysisSeaborn Data VisualizationWorked-up examples.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 analyses, create meaningful data visualizations, predict future trends from data, and more!You will learn how to:Import data setsClean and prepare data for analysisManipulate pandas DataFrameSummarize dataBuild machine learning models using scikit-learnBuild data pipelinesData Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. 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.
