数值分析课程
数值分析课程可以帮助您学习解方程、数值积分、插值和误差分析等技术。您可以掌握优化算法、实施数值方法和分析计算效率的技能。许多课程介绍了 MATLAB、Python 和 R 等工具，支持将这些方法应用于工程、物理和数据科学领域的实际问题。
热门数值分析课程和认证
状态：免费试用University of Colorado Boulder
您将获得的技能: 线性代数, 数据科学, 数值分析, 衍生产品, 微积分, 应用数学, 数学理论与分析, 积分微积分
状态：免费试用The Hong Kong University of Science and Technology
您将获得的技能: 绘图（图形）, 线性代数, 模拟和模拟软件, 估算, Algorithm, 编程原则, 模拟, 科学可视化, 数学建模, 微分方程, 脚本, 数值分析, 应用数学, 工程分析, 数学软件, 微积分, 工程计算, 计算思维, Matlab, 积分微积分
状态：新状态：免费试用The Hong Kong University of Science and Technology
您将获得的技能: Probability, Computational Logic, Combinatorics, Bayesian Statistics, Algorithms, Theoretical Computer Science, Mathematical Software, Mathematical Theory & Analysis, Cryptography, Logical Reasoning, Deductive Reasoning, Computational Thinking, Computer Science, Encryption, Data Structures, Arithmetic, General Mathematics, Public Key Cryptography Standards (PKCS), Problem Solving, Cybersecurity
状态：新状态：预览您将获得的技能: Trend Analysis, Financial Data, Market Trend, Data-Driven Decision-Making, Financial Analysis, Data Visualization, Technical Analysis, Financial Market, Matplotlib, Financial Modeling, Correlation Analysis, Pandas (Python Package), Python Programming, Data Manipulation, Jupyter, Data Structures, Data Import/Export
状态：新状态：免费试用您将获得的技能: Financial Statements, Dashboard, Variance Analysis, Financial Analysis, Pivot Tables And Charts, Excel Formulas, Financial Forecasting, Financial Modeling, Budget Management, Microsoft Excel, Performance Reporting, Data Validation, Spreadsheet Software, Financial Reporting, Auditing, Cost Management, Data Presentation, Data Storytelling, Business Intelligence, Performance Analysis
状态：新状态：免费试用University of London
您将获得的技能: Combinatorics, Probability & Statistics, Data Analysis, Advanced Mathematics, Linear Algebra, Mathematics and Mathematical Modeling, Mathematical Theory & Analysis, Applied Mathematics, Statistics, Analysis, Statistical Analysis, Probability, Algorithms, Algebra
是什么让您今天来到 Coursera？
状态：免费试用The Hong Kong University of Science and Technology
您将获得的技能: 模拟和模拟软件, 线性代数, 几何学, 代数, 数学建模, 工程分析, 工程学, 数值分析, 电磁学, 微积分, 数学软件, 应用数学, 微分方程, 有限元方法, 工程计算, 计算思维, 积分微积分, Matlab, 高等数学, 机械工程
状态：预览Ludwig-Maximilians-Universität München (LMU)
您将获得的技能: Python 程序设计, 分布式计算, 模拟, 振动, 微分方程, 数学建模, 数值分析, 机械, 应用数学, Jupyter, 有限元方法, 工程计算, 工程分析
状态：新状态：免费试用University of London
您将获得的技能: Computational Logic, Algorithms, Applied Mathematics, Advanced Mathematics, Mathematics and Mathematical Modeling, Deductive Reasoning, Mathematical Theory & Analysis, Trigonometry, Statistics, General Mathematics, Analysis, Graphing, Computer Science, Mathematical Modeling, Programming Principles, Business Mathematics, Business Logic, Algebra, Arithmetic, Problem Solving
状态：免费试用DeepLearning.AI
您将获得的技能: Python 程序设计, 数据操作, 线性代数, 代数, 数据预处理, 数学建模, 机器学习, 应用数学, Machine Learning 方法, NumPy, 降维, 高等数学
状态：免费试用University of Pittsburgh
您将获得的技能: Statistical Analysis, NumPy, Probability Distribution, Matplotlib, Statistics, Pandas (Python Package), Data Science, Probability & Statistics, Probability, Statistical Modeling, Predictive Modeling, Data Analysis, Linear Algebra, Predictive Analytics, Statistical Methods, Mathematics and Mathematical Modeling, Applied Mathematics, Python Programming, Machine Learning, Logical Reasoning
状态：免费试用University of Pennsylvania
您将获得的技能: 业务建模, 回归分析, 统计建模, 风险分析, 微软Excel, 财务分析, 财务敏锐性, 收益表, 资本支出, 财务报告, 财务数据, 金融, 数学建模, 投资回报, 金融报表, 金融建模, 资本预算编制, 电子表格软件, Excel 公式, 企业财务
是什么让您今天来到 Coursera？
总之，以下是 10 最受欢迎的 numerical analysis 课程
- 数据科学的积分微积分和数值分析: University of Colorado Boulder
- 工程师数值方法: The Hong Kong University of Science and Technology
- Discrete Mathematical Tools for Computer Science: The Hong Kong University of Science and Technology
- Apply Python for Practical Financial Data Analysis: EDUCBA
- Financial Data Analysis with Excel: Coursera
- Applied Mathematical Methods for Computing: University of London
- 工程师数学: The Hong Kong University of Science and Technology
- 计算机、波浪、模拟：使用 Python 的数值方法实用入门: Ludwig-Maximilians-Universität München (LMU)
- Essential Mathematics for Computer Science: University of London
- 机器学习和数据科学线性代数: DeepLearning.AI
