University of Michigan
Understanding Data: Navigating Statistics, Science, and AI 专项课程
University of Michigan

Understanding Data: Navigating Statistics, Science, and AI 专项课程

Navigate the World of Data with Confidence. Gain essential skills in data analysis, scientific reasoning, and AI for informed decision-making

Elle O'Brien

位教师:Elle O'Brien

1,702 人已注册

包含在 Coursera Plus

深入学习学科知识
4.7

(31 条评论)

初级 等级

推荐体验

3 月 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入学习学科知识
4.7

(31 条评论)

初级 等级

推荐体验

3 月 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Assess the credibility and limitations of claims involving statistics and scientific research

  • Understand the fundamentals of machine learning and navigate conversations about AI with confidence

  • Interpret and evaluate statements about data to make informed decisions in your personal and professional life

要了解的详细信息

可分享的证书

添加到您的领英档案

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

精进特定领域的专业知识

  • 向大学和行业专家学习热门技能
  • 借助实践项目精通一门科目或一个工具
  • 培养对关键概念的深入理解
  • 通过 University of Michigan 获得职业证书

专业化 - 3门课程系列

How to Describe Data

How to Describe Data

第 1 门课程9小时

您将学到什么

  • Learn the fundamentals of data interpretation, collection, and summarization

  • Learn the capabilities and limitations of data and discuss criteria for determining which statistics are reliable

  • Learn to interpret and evaluate the effectiveness of data visualizations

您将获得的技能

类别:Data Analysis
类别:Data Storytelling
类别:Sampling (Statistics)
类别:Data Validation
类别:Descriptive Statistics
类别:Statistical Visualization
类别:Data Literacy
类别:Data Presentation
类别:Statistics
类别:Data Visualization
类别:Histogram
How Science Turns Data Into Knowledge

How Science Turns Data Into Knowledge

第 2 门课程11小时

您将学到什么

  • Learn the rationale and limitations of significance testing within scientific inquiry, including crafting hypotheses and interpreting p-values

  • Learn how scientific experiments are proposed, designed, reviewed, and published

  • Identify common biases and mistranslations in science research reporting and the challenges of conveying science research to the public effectively

  • Assess the credibility of claims about research and recognize the role of replicability and generalizability in scientific progress

您将获得的技能

类别:Experimentation
类别:Scientific Methods
类别:Statistical Hypothesis Testing
类别:Statistical Methods
类别:Media and Communications
类别:Statistical Inference
类别:Research Design
类别:Peer Review
类别:Data Literacy
类别:Probability & Statistics
类别:Research
类别:Data Analysis

您将学到什么

  • Learn key concepts and terminology in artificial intelligence (AI), including machine learning, generative AI, and deep learning

  • Learn the core components of machine learning systems, including data, models, and evaluation techniques

  • Recognize why AI systems can fail and identify the kinds of work required to make useful technology

  • Identify common pitfalls in conversations about AI and recognize conflicts of interest when interpreting claims about AI systems

您将获得的技能

类别:Artificial Intelligence
类别:Predictive Modeling
类别:Generative AI
类别:Data-Driven Decision-Making
类别:Big Data
类别:Responsible AI
类别:Data Ethics
类别:Data Literacy
类别:Machine Learning
类别:Data Analysis
类别:Media and Communications

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Elle O'Brien
University of Michigan
10 门课程6,470 名学生

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