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所在平台: Udemy |
课程主页: https://www.udemy.com/course/lean-six-sigma-green-belt-certification-course-with-python/
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课程名称：使用Python的精益六西格玛绿带在线课程 概述： 2023年新增内容包括第3讲“在AI和工业4.0时代，精益六西格玛是否仍然相关”和第12讲“质量成本”。同时，新增资源第68讲为“样本大小速查表”。在当今就业市场中，数据科学和精益六西格玛的重要性不言而喻。Python作为当前数据科学最流行的工具，在数据分析和统计发现中发挥着重要作用。传统上，精益六西格玛的数据分析使用Minitab和Excel，但在当前环境中，若不学习使用Python进行精益六西格玛绿带数据分析，显然会错失良机。本课程将结合精益六西格玛绿带认证与Python数据科学，为学员提供一次全面的学习体验。 课程内容： - 准备ASQ/IASSC CSSGB认证 - 176节讲座，共17小时内容 - 使用Python进行精益六西格玛分析的逐步数据分析，无需程序设计经验 - Python中的数据处理 - 描述性统计，包括直方图、分布曲线、置信区间、箱线图等 - 数据可视化，包括散点图、热图、皮尔逊相关、多元线性回归、方差分析等 - 控制图（SPC），涵盖多种图表 - Python包使用：Numpy、Pandas、Matplotlib、Seaborn、Statsmodels、Scipy、PySPC等 - 完整的精益六西格玛案例研究及其解决方案（Python脚本） - 超过100个可下载资源，包括所有分析的Python源文件 - 练习题，包括19个关于各种六西格玛主题的填字游戏问题 该课程是学习精益六西格玛绿带的数据分析和Python技能的理想选择，帮助学员在职场中更具竞争力。
New in 2023New Lecture added (Lecture 3) - Is Lean Six Sigma Relevant in the Age of AI and Industry 4.0 New Lecture added (Lecture 12) - Cost of Poor QualityNew Resource Added (Lecture 68) - Sample Size Cheat Sheet added in resourcesWhy you should consider the FIRST LEAN SIX SIGMA GREEN BELT CERTIFICATION COURSE USING PYTHON?There is no need to emphasize the importance of Data Science or Lean Six Sigma in today's Job MarketPython is the most popular and trending tool for Data Science nowLean Six Sigma involves a lot of Data Analysis & Statistical DiscoveryTraditionally Lean Six Sigma Data Analysis uses Minitab & ExcelIN CURRENT SCENARIO, if you are NOT learning Lean Six Sigma Green Belt Data Analysis using Python, it's obvious what you are missing!GET THE BEST OF LEAN SIX SIGMA GREEN BELT CERTIFICATION & DATA SCIENCE WITH PYTHON IN ONE COURSE & AT ONE SHOTWhat to Expect in this Course?Prepare for ASQ / IASSC CSSGB Certification 176 Lectures / 17 Hours of ContentData Analysis in Python with Step by Step Procedure for All Six Sigma Analysis - No Programming Experience NeededData Manupulation in PythonDescriptive StatisticsHistogram, Distribution Curve, Confidence levelsBoxplotStem & Leaf PlotScatter PlotHeat MapPearson's CorrelationMultiple Linear RegressionANOVAT-tests - 1t, 2t and Paired tProportions Test - 1P, 2PChi-square TestSPC (Control Charts - mR, XbarR, XbarS, NP, P, C, U charts)Python Packages - Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, Scipy, PySPC, StemgraphicFull Fledged Lean Six Sigma Case Study with Solutions (in Python Scripts)More than 100 Resources to Download (including Python Source Files for all the analysisPractice questions - 19 Crossword puzzle questions on various six sigma topics included
