统计推理课程
统计 Inference 课程可以帮助您学习假设检验、置信区间、Regression 分析和数据分析。您可以掌握分析 Sampling 样本数据、进行 Prediction 预测以及对更大 Population 得出结论的技能。许多课程都会介绍 R 和 Python 等用于统计编程的工具，以及用于数据 Visualization 的软件，从而提高您有效应用统计方法的能力。
热门统计推理课程和认证
状态：新状态：免费试用您将获得的技能: Splunk, Dashboard, Business Intelligence, Scatter Plots, Data Visualization Software, Data-Driven Decision-Making, Software Installation, Graphing, Real Time Data, Trend Analysis, Event Monitoring, Transaction Processing, Anomaly Detection, System Configuration, Correlation Analysis, Statistical Analysis, Data Manipulation
状态：免费试用您将获得的技能: Prompt Engineering, Prompt Patterns, ChatGPT, Generative AI, Interactive Data Visualization, Business Intelligence, Data Presentation, Infographics, Data Visualization, Dashboard, Data Storytelling, Prototyping, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, AI Workflows, Deep Learning, SQL, Data Analysis, Machine Learning
状态：免费试用University of London
您将获得的技能: 概率与统计, 数据驱动的决策制定, 统计假设检验, 数据分析, 统计推理, 数据展示, 制图, 统计, 概率分布, 概率, 描述性统计, 柱状图, 数据可视化, 估算, 抽样（统计）
状态：免费试用您将获得的技能: 数据驱动的决策制定, 统计分析, 数据转换, 数据科学, 数据导入/导出, 数据分析, 数据清理, Matplotlib, 数据预处理, Scikit-learn （机器学习库）, 数据操作, Python 程序设计, 数据可视化, 探索性数据分析, 模型评估, Pandas（Python 软件包）, 回归分析, 预测建模, NumPy
状态：新状态：免费试用Real Madrid Graduate School Universidad Europea
您将获得的技能: Data Literacy, Technical Communication, Probability & Statistics, Web Scraping, Data Import/Export, JSON, Statistical Analysis, Pandas (Python Package), Analytical Skills, Descriptive Statistics, Data Manipulation, Probability, Data Collection, Data Capture, Restful API, Performance Measurement, Data Cleansing, Application Programming Interface (API)
状态：免费试用您将获得的技能: Prompt Engineering, Prompt Patterns, ChatGPT, Generative AI, Responsible AI, Google Gemini, Project Management, LLM Application, AI Enablement, Project Management Life Cycle, Case Studies, Team Oriented, Prototyping, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Project Documentation, AI Workflows, Deep Learning, Risk Management, Machine Learning
状态：免费试用IBM
您将获得的技能: 数据可视化软件, 数据科学, GitHub, 其他编程语言, 模型部署, 应用编程接口 (API), 大数据, 机器学习, 版本控制, Open Source 技术, Python 程序设计, R 语言程序设计（中文版）, 计算机编程工具, 云计算, Jupyter, R（软件）, Git（版本控制系统）
状态：免费试用University of Michigan
您将获得的技能: 统计分析, 相关性分析, 数据分析, 统计推理, 数据清理, Matplotlib, 数据预处理, Python 程序设计, 描述性统计, R 语言程序设计（中文版）, 散点图, Pandas（Python 软件包）, 回归分析
状态：新状态：免费试用您将获得的技能: Prompt Engineering, Prompt Patterns, ChatGPT, Generative AI, Responsible AI, Business Analysis, Data-Driven Decision-Making, Data Analysis, AI Enablement, Data Storytelling, Artificial Intelligence, Business Process Automation, Stakeholder Communications, Prototyping, Artificial Intelligence and Machine Learning (AI/ML), AI Workflows, Deep Learning, Business Strategy, Machine Learning, Context Management
状态：新状态：预览您将获得的技能: Object Oriented Programming (OOP), JSON, Python Programming, Debugging, Data Structures, Red Hat Enterprise Linux, Computer Programming, Statistical Programming, Web Development, Scripting, Scripting Languages, Development Environment, Text Mining, Software Installation
状态：免费试用您将获得的技能: Prompt Engineering, AI Product Strategy, Prompt Patterns, ChatGPT, Responsible AI, Generative AI, Google Gemini, Product Management, AI Enablement, Artificial Intelligence, Prototyping, Artificial Intelligence and Machine Learning (AI/ML), AI Workflows, Deep Learning, Machine Learning, Stakeholder Engagement, Context Management, Decision Making
状态：预览The University of North Carolina at Chapel Hill
您将获得的技能: 公共卫生, 统计分析, 卫生政策, 科学与研究, 流行病学, 研究方法, 健康的社会决定因素, 研究设计, 社区卫生, 慢性疾病, 传染病
总之，以下是 10 最受欢迎的 statistical inference 课程
- Splunk Fundamentals: Analyze & Visualize Data: EDUCBA
- Generative AI for Business Intelligence (BI) Analysts: IBM
- 国际商务统计: University of London
- 使用 Python 进行数据分析: IBM
- Conceptos imprescindibles - Requisitos para el análisis : Real Madrid Graduate School Universidad Europea
- Generative AI for Project Managers: IBM
- 数据科学工具: IBM
- 体育分析基础：体育运动中的数据、表示和模型: University of Michigan
- Generative AI for Business Analysts: IBM
- Python Programming with Red Hat: Red Hat
