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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistics-and-probability-essentials/
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Coursera Python数据科学统计、概率与探索性数据分析课程概述 本课程旨在为数据科学和机器学习领域提供坚实的统计学和概率论基础，并通过Python实践深入讲解。 **核心内容板块：** **一、统计学与概率论** * **统计学基础**：介绍统计学的基本概念、分类（描述性统计与推断性统计）、描述性统计的属性与局限性。 * **推断性统计**：“推断性统计”的概念、应用方式、变量类型及其相应的统计方法。 * **描述性统计详解**：深入讲解集中趋势度量（均值、中位数、众数等）和位置度量（四分位数、百分位数等）。 * **离差度量**：学习各种离差度量（方差、标准差、极差等）及其在Python中的实现。 * **概率论基础**：定义概率、解释概率相关术语，介绍随机变量及其类型。 * **概率函数**：理解概率分布、概率密度函数（PDF）和累积分布函数（CDF）的概念，并通过Python代码进行实践。 * **概率分布详解**：重点讲解正态分布、标准正态分布及其特性，以及如何在Python中应用PDF。 * **累积分布函数应用**：学习CDF的含义及Python实现。 * **分布的形态**：分析对称分布、偏度（Skewness）和峰度（Kurtosis）等分布特征，并结合Python代码演示。 * **数据可视化工具**：学习使用箱线图（Box-plot）、小提琴图（Violin Plot）展示数据分布，包括异常值处理。 * **核密度估计**：理解核函数（Kernel）概念，学习核密度估计（KDE）及其在单变量分析中的应用。 * **协方差与相关性**：学习协方差和相关性的概念、属性、计算方法及如何通过热力图（Heatmap）进行可视化，区分相关性与因果关系。 * **回归分析**：介绍回归模型的基本概念、类型、与相关性的区别，重点讲解简单线性回归、最小二乘法、残差分析以及Python实现。 **二、探索性数据分析（EDA）** * **EDA基础**：强调EDA在数据科学中的重要性，以及如何通过EDA发现模式、异常和检验假设。 * **多维度分析**：学习如何进行单变量（Univariate）、双变量（Bivariate）和多变量（Multivariate）分析。 * **Python可视化库应用**：熟练使用Seaborn库进行样式设置，创建散点图、核密度估计图、六边形图、回归图等。 * **案例研究**： * **教室数据集**：进行全面的单变量、双变量和多变量分析。 * **IMD降雨数据集**：利用热力图自动化数据可视化，总结分析结果。 * **房地产数据集**：进行相关矩阵分析、回归分析和分类变量的回归图分析。 * **IPL球员表现数据集**：通过条形图、计数图、散点图、蜂群图、箱线图和小提琴图等多种图表深入分析球员数据。 **课程优势：** * **理论与实践结合**：所有概念均通过实际案例和Python代码进行演示，强调动手能力。 * **实战导向**：通过多个真实数据集的EDA训练，使学员掌握解决实际问题的方法。 * **技能提升**：帮助学员建立对统计学和概率论的深刻理解，并能灵活应用于数据科学和机器学习项目中。 本课程将帮助您自信地处理任何数据集，并在数据驱动的决策中发挥关键作用。
Statistics and Probability:Statistics and Probability are essential pillars in Data Science and Machine Learning, providing the foundational tools needed for data analysis and interpretation. Our course is designed to give you a deep understanding of these crucial concepts through practical, hands-on learning.Throughout the course, we teach each concept in Statistics and Probability by working through real-world examples and implementing them using Python code. You'll explore Descriptive and Inferential Statistics, including measures of central tendencies, measures of dispersion, and various statistical methods and variables, all illustrated with practical examples. In Probability, you'll learn about random variables, Probability distributions, probability density functions, and cumulative distribution functions, each concept reinforced with Python coding exercises to solidify your understanding.By learning Statistics and Probability through practical examples and Python code, you'll not only grasp these critical concepts but also gain the confidence to apply them in real-world scenarios. This approach ensures that you are well-prepared to tackle advanced Data Science and Machine Learning challenges, making you proficient in these key areas and setting you up for success in the data-driven world.Please find the brief Syllabus to the Course.Statistics and Probability: 1. Introduction to StatisticsIntroduction to Statistics, Types of statistics, Descriptive Statistics and its attributes, Limitations of descriptive statisticsStatistics and Probability: 2. Introduction to Inferential StatisticsInferential Statistics and its attributes, Two ways to use inferential statistics, types of variables and their statistical methods.Statistics and Probability: 3. Descriptive StatisticsMeasures of Central Tendencies and its types, Statistical Measure of Positions and its types.Statistics and Probability: 4. Measures of DispersionMeasures of Dispersion and its types with examples and python code.Statistics and Probability: 5. Introduction to ProbabilityDefinition of Probability, Different terms in Probability with an example, Types of Random variable with examples.Statistics and Probability: 6. Types of Probability functionsDistribution, Probability Distribution, Types of Probability functions with Python CodeStatistics and Probability: 7. Probability density functionProbability density function and its attributes, Normal and Standard Normal Distribution, Properties of Normally distributed Curve with a Python Code, Density of a value in the Distribution.Statistics and Probability: 8. Cumulative Distribution FunctionExplanation to Cumulative Distribution Function with a Python CodeStatistics and Probability: 9. Types and attribute of DistributionSymmetric distribution, Skewness, Kurtosis with a Python Code.Statistics and Probability: 10. Box-plot with Whiskers and Voilin PlotsBox-plot, Voilin Plot, Plotting a Boxplot and Voilin plot using a Python code, Calculation of Quantiles and whisker values, Dropping the outliers in our data.Statistics and Probability: 11. Kernel Density EstimationWhat is a Kernel, Properties of a Kernel, Kernel Density Estimation Plot and its properties, KDE visualizations, Univariate Analysis using KDE plot, Bivariate Analysis using contour plot.Statistics and Probability: 12. CovarianceCovariance its attributes and examples, Properties of Covariance Value, Comparison of Covariance between two variables, Creating a Covariance Matrix, Negative Covariance and Zero Covariance.Statistics and Probability: 13. CorrelationCorrelation and its properties, Analysis of Correlation between two variables, Assumptions before we calculate the Correlation, Correlation and its visualizations (Heatmap), 2. Coefficient of Determination, Causation and its relationship with Correlation with examples.Statistics and Probability: 14. RegressionRegression and its definition, Types of Variable, Use of Regression, Difference between Regression and Correlation, Simple Linear Regression, Calculating the Least Squares Regression Line, Standard error of Estimate and its Assumptions, Linear Regression using a Python code.Exploratory Data AnalysisExploratory Data Analysis (EDA) is a crucial step for anyone pursuing a career in Data Science and Machine Learning. It allows you to uncover patterns, identify anomalies, and test hypotheses within your datasets. Our course is specifically designed to equip you with the practical EDA skills necessary for success as a Machine Learning engineer or data scientist.In this course, you'll gain hands-on experience with EDA by working through various datasets, which is essential for developing real-world expertise. You'll begin with Univariate, Bivariate, and Multivariate Analysis, where you'll learn to set Seaborn styles and create visualizations like scatter plots, kernel density estimation plots, and hex plots. We then guide you through the EDA of the IMD Rainfall Dataset, using heatmaps to summarize data and automate visualizations. You'll also perform EDA on a Real Estate Dataset, conducting correlation matrix analysis, regression analysis, and examining categorical variables through regression plots. Finally, you'll analyze an IPL player performance dataset, using bar plots, count plots, strip plots, swarm plots, box plots, and violin plots to derive insights. This hands-on approach ensures that you not only understand EDA theoretically but also know how to apply it to real-world datasets, a vital skill for data professionals.Mastering Exploratory Data Analysis is essential for becoming a proficient Machine Learning engineer or data scientist. EDA is the foundation of deeper statistical analysis and machine learning model development, helping you make sense of raw data and identify key trends and outliers. The skills you gain from this course will empower you to transform raw data into actionable insights, a core competency in Data Science and Machine Learning. By the end of our course, you'll be proficient in EDA techniques, enabling you to approach any dataset with confidence and drive data-driven decision-making in your projects.Exploratory Data Analysis1. Exploratory Data Analysis using Classroom DatasetUnivariate, Bivariate and Multivariate Analysis of Classroom dataset, Setting Seaborn style, Univariate Analysis, Bivariate Analysis, Scatter Plot, Kernel Density Estimation Plot, Hex Plot, Regression Plot, Multivariate Analysis.2. Exploratory Data Analysis using IMD Rainfall DatasetAnalysis of Rainfall Dataset using Heatmap, leveraging Automation to generate multiple visualizations, Summarizing inferences from a Heatmap.3. Exploratory Data Analysis of Real Estate DatasetCorrelation Matrix of the Real Estate Dataset, Regression Analysis of Real Estate Dataset, Categorical Analysis of Regression plots.4. Exploratory Data Analysis using IPL player performance DatasetDifferent Analysis of dataset using Bar plot, Count plot, Strip plot, Swarm plot, Boxplot, Violin plot.Hurry!!! with no Worry and get enrolled today!! as Udemy provides you with a 30 day money back guarantee if you don't like the Course. Happy Learning!!!:)
