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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-advanced-data-science-data-scientist-aiml-experts-tm/
课程评论：没有评论
课程名称：高级数据科学硕士 - 数据科学家 AIML 专家 TM 课程概述：本综合数据科学精通课程旨在为学习者提供整个数据科学生命周期中必需的技能和知识。课程涵盖数据科学的关键概念、工具和技术，从基本的数据收集与处理到高级机器学习模型。学习者将探索以下内容： 核心数据科学基础： - 数据科学基础与方法论的核心概念 - 现代数据科学与传统分析的比较 - 数据科学家的角色、技能及责任 - 数据科学项目的逐步过程概述 编程基础： - 针对数据科学任务的Python编程基础 - 深入探索Numpy、Pandas、Matplotlib和Seaborn等关键Python库 - R编程语言基础学习，用于统计分析 - Python与R中的数据结构及函数 数据收集与预处理： - 各种数据收集方法的理解 - 数据清洗、转换及分析准备 - 探索性数据分析（EDA）和数据清理 - 处理缺失数据和异常值的技术 可视化与分析： - 数据可视化技术的最佳实践 - 使用Tableau进行数据可视化 - 假设检验和置信区间的推论统计学 机器学习精通： - 机器学习的核心概念和应用 - 无监督学习（聚类、DBSCAN、降维）和有监督学习（回归、分类、决策树） - 评估回归和分类模型的性能指标及模型验证方法 数据科学的高级主题： - 高维数据集中的降维技术 - 特征工程和选择 - 数据科学中的SQL查询 - 数据科学的伦理挑战 动手应用与案例研究： - 实际数据科学项目的案例研究 - 深入学习Python和R在实际数据分析中的应用 - 在真实情境中应用数据科学技术 通过本课程，学习者将能够处理端到端的数据科学项目，包括数据收集、清洗、可视化、统计分析以及构建稳健的机器学习模型。结合动手项目、案例研究和顶点项目，这个课程将为学习者打下坚实的数据科学和机器学习基础，准备成为数据科学家和AI/ML专业人士。
This comprehensive Data Science Mastery Program is designed to equip learners with essential skills and knowledge across the entire data science lifecycle. The course covers key concepts, tools, and techniques in data science, from basic data collection and processing to advanced machine learning models. Here's what learners will explore:Core Data Science Fundamentals:Data Science Sessions Part 1 & 2 - Foundation of data science methodologies and approaches.Data Science vs Traditional Analysis - Comparing modern data science techniques to traditional statistical methods.Data Scientist Journey Parts 1 & 2 - Roles, skills, and responsibilities of a data scientist.Data Science Process Overview Parts 1 & 2 - An introduction to the step-by-step process in data science projects.Programming Essentials:Introduction to Python for Data Science - Python programming fundamentals tailored for data science tasks.Python Libraries for Data Science - In-depth exploration of key Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.Introduction to R for Data Science - Learning the R programming language basics for statistical analysis.Data Structures and Functions in Python & R - Efficient data handling and manipulation techniques in both Python and R.Data Collection & Preprocessing:Introduction to Data Collection Methods - Understanding various data collection techniques, including experimental studies.Data Preprocessing - Cleaning, transforming, and preparing data for analysis (Parts 1 & 2).Exploratory Data Analysis (EDA) - Detecting outliers, anomalies, and understanding the underlying structure of data.Data Wrangling - Merging, transforming, and cleaning datasets for analysis.Handling Missing Data and Outliers - Techniques to manage incomplete or incorrect data.Visualization & Analysis:Data Visualization Techniques - Best practices for choosing the right visualization method to represent data.Tableau and Data Visualization - Leveraging advanced data visualization software.Inferential Statistics for Hypothesis Testing & Confidence Intervals - Key statistical concepts to test hypotheses.Machine Learning Mastery:Introduction to Machine Learning - Core concepts, types of learning, and their applications.Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction) - Discovering patterns in unlabeled data.Supervised Learning (Regression, Classification, Decision Trees) - Building predictive models from labeled data.Evaluation Metrics for Regression & Classification - Techniques to evaluate model performance (e.g., accuracy, precision, recall).Model Evaluation and Validation Techniques - Methods for improving model robustness, including bias-variance tradeoffs.Advanced Topics in Data Science:Dimensionality Reduction (t-SNE) - Reducing complexity in high-dimensional datasets.Feature Engineering and Selection - Selecting the best features for machine learning models.SQL for Data Science - Writing SQL queries for data extraction and advanced querying techniques.Ethical Challenges in Data Science - Understanding the ethical implications in data collection, curation, and model deployment.Hands-on Applications & Case Studies:Data Science in Practice Case Study (Parts 1 & 2) - Real-world data science projects, combining theory with practical implementation.End-to-End Python & R for Data Science - Practical coding exercises to master Python and R in real data analysis scenarios.Working with Data Science Applications - Applying data science techniques in real-world situations.By the end of this program, learners will be equipped to handle end-to-end data science projects, including data collection, cleaning, visualization, statistical analysis, and building robust machine learning models. With hands-on projects, case studies, and a capstone, this course will provide a solid foundation in data science and machine learning, preparing learners for roles as data scientists and AI/ML professionals.
