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Probability & Statistics courses

Probability and statistics courses explore mathematical concepts for analyzing random events and interpreting data through models and inference. Use tools such as Python, R, Excel and Google Sheets to apply your theoretical knowledge in statistics.

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Recommended for Probability & Statistics beginners

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课程

R 统计学入门

中级技能水平
4.8+
2,179 条评价
4小时
提升统计技能,学习如何收集、分析数据并得出准确结论。

学习路径

统计学家 在 R 中

4.5+
7 条评价
52小时
统计学家收集和分析数据,帮助公司理解定量数据,包括发现趋势和进行预测。

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课程

R 统计面试题实战练习

高级技能水平
4.7+
23 条评价
4小时
在本课程中,你将为最常见的统计主题做好准备,从分布到假设检验、回归模型等更多内容。

课程

使用 R 预测产品需求

中级技能水平
4.7+
30 条评价
4小时
掌握识别需求关键驱动因素、分析季节性影响,并通过真实案例预测产品层级需求。

课程

R 异常检测入门

中级技能水平
4.8+
28 条评价
4小时
学习用于识别异常值的统计检验,以及如何使用复杂的异常评分算法。

课程

R 中的概率谜题

基础技能水平
4.7+
69 条评价
4小时
通过解决各种概率谜题,学习在 R 中回答概率问题的策略。

课程

R 中的混合模型

中级技能水平
4.7+
27 条评价
4小时
学习混合模型:一种用于概率聚类和分类的便捷且正式的统计框架。

课程

R 中的营销选择建模

高级技能水平
4.8+
42 条评价
4小时
学习如何在 R 中分析和建模客户选择数据。

课程

在 R 中构建响应模型

中级技能水平
4.8+
31 条评价
4小时
学习构建市场响应的简单模型,提升营销方案的效果。

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Frequently asked questions

How does probability and statistics related to data science?

Probability and statistics are foundational to data science, offering the tools and frameworks necessary for analyzing data, making predictions, and deriving meaningful insights. They enable data scientists to understand patterns, assess uncertainties, and make informed decisions based on data analysis.

Why is it important to develop knowledge in probability and statistics?

Developing knowledge in probability and statistics is crucial for effectively interpreting data and making reliable predictions. This understanding forms the basis for designing experiments, analyzing results, and validating conclusions in various fields, ensuring decisions are data-driven and evidence-based.

What careers can I pursue with knowledge in probability and statistics?

With knowledge in probability and statistics, you can pursue a wide array of careers such as data scientist, market researcher, machine learning engineer, statistical analyst, and risk manager. These roles span various industries including finance, healthcare, technology, and government, where interpreting data and making evidence-based decisions are key.

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