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所在平台: Udemy |
课程主页: https://www.udemy.com/course/easy-guide-to-statistical-analysis-data-science-analytics/
课程评论：没有评论
课程名称：统计分析与数据科学分析的简易指南 课程概述：本在线培训提供了一系列全面的分析技能，旨在帮助有兴趣学习应用统计学和数据科学的学生和研究人员，通过这些技能解决常见的复杂现实研究问题。培训内容涵盖从基础统计（如卡方检验和多因素方差分析）到多元统计（如结构方程模型和多层次模型）的全流程指导。同时，您将学习强大的无监督机器学习技术（如Apriori算法和tSNE），以及更复杂的监督机器学习方法（如深度学习和迁移学习）。无论您是初学者还是高级研究者，我们相信课程中都有适合您的内容！本研讨会旨在通过解密数据科学和统计概念与技术，帮助您更好地理解复杂的构念。这意味着您不需要理解所有内容。您的目标（至少目前）是能够从头到尾运行数据并得到结果。您可以随着时间的推移在自己的节奏下逐步积累知识。统计学和数据科学可能让人望而却步，但其实并没有必要！请记住，学习数据科学和统计分析的基本知识是个人和职业发展的重要投资，特别是在数字时代，这些都是保持竞争力的关键技能。 课程内容： - 动机 - R数据管理简介 - R编程 - 使用R进行统计分析 - 使用R进行分类统计 - 使用R进行数值统计 - 数据可视化 - 文本挖掘与Apriori算法 - 降维与无监督机器学习 - 特征选择技术 - 懒惰学习（k近邻算法） - k均值聚类 - 朴素贝叶斯分类 - 决策树分类 - 黑箱：神经网络与支持向量机 - 回归、预测与递归神经网络 - 模型评估、元学习与自动调优 - 深度学习 - 迁移学习 课程结束时，参与者预计将掌握一套统计和数据科学分析技能，能够对数据进行审查、管理，并为各自的研究问题提供推断和决策。
This online training provides a comprehensive list of analytical skills designed for students and researchers interested to learn applied statistics and data science to tackle common and complex real world research problems.This training covers end-to-end guide from basic statistics such as Chi-square test and multi-factorial ANOVA, to multivariate statistics such as Structural equation modeling and Multilevel modeling. Similarly, you will also learn powerful unsupervised machine learning techniques such as Apriori algorithm and tSNE, to more complex supervised machine learning such as Deep Learning and Transfer Learning. Whether you are a beginner or advanced researcher, we believe there is something for you! This workshop helps you better understand complex constructs by demystifying data science and statistical concepts and techniques for you. This also means you do not need to understand everything. Your goal (at least for now) is to be able to run your data end-to-end and get a result. You can build up on the knowledge over time, comfortably at your own pace.Statistics and data science can be intimidating but it does not have to be! Remember, learning the fundamentals of data science and statistical analysis for personal and professional usage is a great investment you will never regret, especially because these are essential skills to stay relevant in the digital era.Content: MotivationIntroduction to RR Data ManagementR ProgrammingStatistics with RStatistics with R (Categorical)Statistics with R (Numerical)Data visualizationText mining and Apriori algorithmDimensionality reduction and unsupervised machine learningFeature selection techniquesLazy learning (k-nearest neighbors)k-Means clusteringNaive Bayesian classificationDecision Trees classificationBlack box: Neural Network & Support Vector MachinesRegression, Forecasting & Recurrent NeuralNetModel Evaluation, Meta-Learning & Auto-tuningDeep LearningTransfer LearningAt the end of the training, participants are expected to be equipped with a tool chest of statistical and data science analytical skills to interrogate, manage, and produce inference from data to decision on respective research problems.
