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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-statistics-for-data-science-with-python-and-r/
课程评论：没有评论
课程名称：统计学2025 A-Z：数据科学中的Python与R 概述：数据科学和分析是一个非常有前景的职业，它可以帮助解决一些世界上最有趣的问题，而统计学是所有分析和机器学习模型的基础。这使得统计学成为学习过程中的必要部分。没有统计学的分析是没有依据的，随时可能走向错误的方向。对于大多数分析专业人士和初学者来说，统计学往往是一个令人生畏和怀疑的话题，这就是我们为那些希望学习统计学并应用各种统计方法进行分析的人设计本课程的原因。本课程旨在为您提供所需的全部知识，使您在职业道路的起始阶段，无需反复查阅其他资料。这门课程是您获得所需知识、技巧和诀窍的终极目的地，以便启动您的职业生涯。我们全面覆盖统计学的所有知识。 课程内容包括： 1. 统计学的基本概念：了解统计学的必要性，区分总体与样本，以及各种抽样技术。 2. 描述性统计：包括集中趋势的测量（均值、中位数、众数）和变异性的测量（方差、标准差、四分位间距、贝塞尔校正）。 3. 分布形状：学习钟形曲线、峰度和偏度等。 4. 变量类型及其交互作用：相关性、协方差、多重共线性、特征创建与选择。 5. 推断统计：掌握各种估计技术、正态曲线的性质、中心极限定理的计算及Z分数和置信区间的表示。 6. 假设检验：学习如何制定原假设及相应的对立假设。 7. 选择和执行各种假设检验：如Z检验、单样本T检验、独立样本T检验、配对T检验、卡方拟合优度检验、卡方独立性检验、方差分析（ANOVA）。 8. 回归分析：学习从变量创建、选择、数据转换到模型构建与评价的完整过程，包括线性回归和逻辑回归。 9. 深入讲解统计方法，包括来自实践的真实技巧，帮助你超越仅有基础知识的初学者。 10. 所有解释使用简单易懂的语言，使后续学习与工作变得轻松。 11. 在15个不同数据集上进行实践，以便迅速上手并获得处理不同数据集与问题的学习优势。 本课程为希望在数据科学领域取得成功的学习者提供了全面而深入的统计学知识。
Data Science and Analytics is a highly rewarding career that allows you to solve some of the world's most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction.For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples!This course is made to give you all the required knowledge at the beginning of your journey, so that you don't have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.This course provides Full-fledged knowledge of Statistics, we cover it all.Our exotic journey will include the concepts of:1. What's and Why's of Statistics - Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.2. Descriptive Statistics will include the Measures Of central tendency - Mean, Median, Mode and the Measures of Variability - Variance, SD, IQR, Bessel's Correction3. Further you will learn about the Shapes Of distribution - Bell Curve, Kurtosis, Skewness.4. You will learn about various types of variables, their interactions like Correlation, Covariance, Collinearity, Multicollinearity, feature creation and selection.5. As part of Inferential statistics, you will learn various Estimation Techniques, Properties of Normal Curve, Central Limit Theorem calculation and representation of Z Score and Confidence Intervals.6. In Hypothesis Testing you will learn how to formulate a Null Hypothesis and the corresponding Alternate Hypothesis.7. You will learn how to choose and perform various hypothesis tests like Z - test, One Sample T Test, Independent T Test, Paired T Test, Chi Square - Goodness Of Fit, Chi-Square Test for Independence, ANOVA8. In regression Analysis you will learn about end-to-end variable creation selection data transformation, model building and Evaluation process for both Linear and Logistic Regression.9. In-depth explanation for Statistical Methods with all the real-life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.10. All explanations provided in a simple language to make it easy to understand and work on in future.11. Hands-on practice on more than 15 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.
