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  • Statistics

统计课程

统计学课程可以帮助您学习数据分析、概率论、假设检验和 Regression 分析技术。您可以掌握解释 Data Set、做出明智预测和开展调查的技能。许多课程都会介绍 R、Python 和 Excel 等工具,这些工具支持执行统计分析和可视化结果。您还将探索描述统计、推断统计和实验设计等关键主题,掌握应对现实世界数据挑战的知识。


热门统计学课程和认证


  • 状态:免费试用
    免费试用
    G

    Google

    The Power of Statistics

    您将获得的技能: Sampling (Statistics), Descriptive Statistics, Statistical Hypothesis Testing, Data Analysis, Probability Distribution, Statistics, Data Science, Statistical Analysis, A/B Testing, Statistical Methods, Probability, Statistical Inference, Statistical Programming, Python Programming, Technical Communication

    4.8
    评分, 4.8 星,最高 5 星
    ·
    859 条评论

    高级设置 · 课程 · 1-3 个月

  • 状态:免费试用
    免费试用
    J

    Johns Hopkins University

    数据科学高级统计学

    您将获得的技能: 回归分析, 预测建模, 统计分析, 应用数学, 统计推理, 抽样(统计), 概率分布, 数据科学, 概率与统计, 统计假设检验, 贝叶斯统计, 数学建模, 数据分析, 概率, 统计建模, 统计, 统计方法, 线性代数, 生物统计学, R 语言程序设计(中文版)

    4.4
    评分, 4.4 星,最高 5 星
    ·
    782 条评论

    高级设置 · 专项课程 · 3-6 个月

  • 状态:新
    新
    状态:免费试用
    免费试用
    M

    Macquarie University

    Statistics and Data Analysis with Excel: Advanced

    您将获得的技能: Data-Driven Decision-Making, Microsoft Excel, Forecasting, Regression Analysis, Business Analytics, Statistical Analysis, Data Analysis Software, Time Series Analysis and Forecasting, Analytics, Data Visualization, Statistical Methods, Advanced Analytics, Data Analysis, Statistics, Probability & Statistics, Statistical Inference, Statistical Modeling, Variance Analysis

    4.7
    评分, 4.7 星,最高 5 星
    ·
    21 条评论

    高级设置 · 课程 · 1-3 个月

  • 状态:免费试用
    免费试用
    G

    Google

    Google Advanced Data Analytics

    您将获得的技能: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Presentation, Data Visualization Software, Regression Analysis, Feature Engineering, Descriptive Statistics, Statistical Hypothesis Testing, Advanced Analytics, Data Analysis, Data Science, Tableau Software, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming

    攻读学位

    4.7
    评分, 4.7 星,最高 5 星
    ·
    1万 条评论

    高级设置 · 专业证书 · 3-6 个月

  • 状态:免费试用
    免费试用
    E

    ESSEC Business School

    战略商业分析

    您将获得的技能: 数据叙事, 统计分析, 企业战略, 营销分析, 预测, 数字化转型, 统计编程, 商业分析, 高级分析, 预测分析, 分析技能, 客户分析, 数据分析, 统计方法, 信息技术, 解决复杂问题, 同行评审, 人力资源分析, R 语言程序设计(中文版), 案例研究

    4.4
    评分, 4.4 星,最高 5 星
    ·
    1275 条评论

    高级设置 · 专项课程 · 3-6 个月

  • 状态:免费试用
    免费试用
    G

    Google

    Foundations of Data Science

    您将获得的技能: Data Ethics, Data-Driven Decision-Making, Data Analysis, Analytics, Business Analytics, Data Storytelling, Big Data, Data Science, Communication, Stakeholder Communications, Workflow Management, Project Management, Artificial Intelligence

    4.7
    评分, 4.7 星,最高 5 星
    ·
    3690 条评论

    高级设置 · 课程 · 1-3 个月

是什么让您今天来到 Coursera?

  • 状态:新
    新
    状态:免费试用
    免费试用
    U

    University of Michigan

    More Applied Data Science with Python

    您将获得的技能: Unsupervised Learning, Data Mining, Social Network Analysis, ChatGPT, Data Processing, Anomaly Detection, Machine Learning Methods, Data Science, Supervised Learning, Machine Learning, Data Analysis, Data Manipulation, Python Programming, Exploratory Data Analysis, Machine Learning Algorithms, Jupyter, Probability Distribution

    4.3
    评分, 4.3 星,最高 5 星
    ·
    8 条评论

    高级设置 · 专项课程 · 3-6 个月

  • 状态:免费试用
    免费试用
    C

    Corporate Finance Institute

    Advanced Tableau

    您将获得的技能: Star Schema, Data Modeling, Tableau Software, Business Intelligence, Extract, Transform, Load, Dashboard, Performance Tuning, Technical Analysis, Database Design, Data Visualization Software, Data Warehousing, Market Data, Data Transformation, Data Presentation, Investment Banking, Exploratory Data Analysis, Tree Maps, Interactive Data Visualization, Data Storytelling, Data Visualization

    4.6
    评分, 4.6 星,最高 5 星
    ·
    26 条评论

    高级设置 · 专项课程 · 3-6 个月

  • 状态:免费试用
    免费试用
    C

    Corporate Finance Institute

    Practical Data Science for Data Analysts

    您将获得的技能: Feature Engineering, Data Import/Export, Matplotlib, Exploratory Data Analysis, Data Science, Regression Analysis, Financial Data, Data Analysis, Business Analytics, Data Cleansing, Data Literacy, Pandas (Python Package), Machine Learning Algorithms, Statistical Visualization, Financial Analysis, Predictive Modeling, Applied Machine Learning, Statistical Modeling, Statistical Analysis, Predictive Analytics

    4.7
    评分, 4.7 星,最高 5 星
    ·
    26 条评论

    高级设置 · 专项课程 · 3-6 个月

  • 状态:新
    新
    状态:免费试用
    免费试用
    U

    University of Colorado Boulder

    Modern AI Models for Vision and Multimodal Understanding

    您将获得的技能: Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Unsupervised Learning, Linear Algebra, Supervised Learning

    攻读学位

    高级设置 · 课程 · 1-4 周

  • 状态:免费试用
    免费试用
    J

    Johns Hopkins University

    数据科学高级线性模型 1:最小二乘法

    您将获得的技能: 回归分析, 统计分析, 应用数学, 预测建模, 数据科学, 数学建模, 统计, 统计建模, 高等数学, 线性代数, R 语言程序设计(中文版)

    4.5
    评分, 4.5 星,最高 5 星
    ·
    188 条评论

    高级设置 · 课程 · 1-3 个月

  • 状态:新
    新
    状态:免费试用
    免费试用
    U

    University of Michigan

    Network Modeling and Analysis in Python

    您将获得的技能: Social Network Analysis, Data Analysis, Python Programming, Jupyter, Probability Distribution

    高级设置 · 课程 · 1-4 周

与 statistics 相关的搜索

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statistics for data analysis
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statistics with python
statistics in psychological research
statistics and applied data analysis
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1234…6

总之,以下是 10 最受欢迎的 statistics 课程

  • The Power of Statistics: Google
  • 数据科学高级统计学: Johns Hopkins University
  • Statistics and Data Analysis with Excel: Advanced: Macquarie University
  • Google Advanced Data Analytics: Google
  • 战略商业分析: ESSEC Business School
  • Foundations of Data Science: Google
  • More Applied Data Science with Python: University of Michigan
  • Advanced Tableau: Corporate Finance Institute
  • Practical Data Science for Data Analysts: Corporate Finance Institute
  • Modern AI Models for Vision and Multimodal Understanding: University of Colorado Boulder

关于 统计 的常见问题

Statistics is the branch of mathematics that deals with collecting, analyzing, interpreting, presenting, and organizing data. It is crucial because it provides the tools and methodologies to make informed decisions based on data. In an increasingly data-driven world, understanding statistics allows individuals and organizations to identify trends, make predictions, and validate hypotheses. Whether in business, healthcare, social sciences, or technology, statistics plays a vital role in guiding strategies and improving outcomes.‎

A background in statistics can open doors to various career opportunities. Jobs in this field include data analyst, statistician, biostatistician, market researcher, and quantitative analyst. These roles often require the ability to interpret complex data sets and communicate findings effectively. Additionally, industries such as finance, healthcare, and technology are increasingly seeking professionals skilled in statistics to help drive decision-making processes and improve operational efficiency.‎

To pursue a career in statistics, you should develop a range of skills. Key competencies include proficiency in statistical software (like R or Python), a solid understanding of probability theory, data visualization techniques, and the ability to interpret and communicate statistical results. Additionally, critical thinking and problem-solving skills are essential, as they enable you to approach data analysis with a strategic mindset. Familiarity with data collection methods and experimental design is also beneficial.‎

There are many excellent online statistics courses available that cater to different levels of expertise. For beginners, the Foundations of Probability and Statistics Specialization offers a solid introduction. For those looking to apply statistics in data science, the Data Science: Statistics and Machine Learning Specialization is highly recommended. Additionally, the Business Statistics and Analysis Specialization provides practical skills for applying statistics in a business context.‎

Yes. You can start learning statistics on Coursera for free in two ways:

  1. Preview the first module of many statistics courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in statistics, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

Learning statistics can be approached through various methods. Start by identifying your learning style‚Äîwhether you prefer structured courses, hands-on projects, or self-study. Online platforms like Coursera provide a range of courses that cater to different preferences. Engage with practical exercises to apply what you learn, and consider joining study groups or forums to discuss concepts with peers. Regular practice and real-world application will reinforce your understanding and build confidence.‎

Typical topics covered in statistics courses include descriptive statistics, probability theory, inferential statistics, hypothesis testing, regression analysis, and data visualization. More advanced courses may explore Bayesian statistics, multivariate analysis, and statistical modeling. These topics provide a comprehensive foundation for understanding how to analyze and interpret data effectively, which is essential for making informed decisions in various fields.‎

For training and upskilling employees, courses like the Statistics and Applied Data Analysis Specialization are particularly beneficial. This specialization focuses on practical applications of statistics in real-world scenarios. Additionally, the Business Statistics and Analysis Specialization equips learners with essential skills for data-driven decision-making in business contexts. These courses can enhance workforce capabilities and drive organizational success.‎

此常见问题解答内容仅供参考。建议学生多做研究,确保所追求的课程和其他证书符合他们的个人、专业和财务目标。

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