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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-data-science-aiml-end-to-end-master-class-tm/
课程评论：没有评论
课程名称：深度数据科学：为数据科学家提供端到端的培训 课程概述： 本课程为数据科学领域提供全面的基础学习，涵盖数据科学的定义、工具和技术、与传统分析的对比、数据科学家的职责，以及数据科学过程中的各个环节。 1. **数据科学概论** - 介绍数据科学及其在各行业中的重要性。 - 学习成果：理解数据科学基础及其相关应用。 2. **数据科学工具与技术** - 深入探讨数据科学中使用的工具、数据清洗、探索和可视化等主题。 - 学习成果：分析实际案例，掌握数据科学工具和技术。 3. **数据科学与传统分析的对比** - 明确传统数据分析与数据科学之间的区别及优势。 - 学习成果：识别两者的演变和数据科学的优越性。 4. **数据科学家的角色：第一部分** - 探讨数据科学家的基本职责和必备技能。 - 学习成果：理解数据科学家的重要性及其所需技能。 5. **数据科学家的角色：第二部分** - 深入讨论数据科学家的实际挑战与职业发展机会。 - 学习成果：掌握数据科学面临的挑战及职业生涯的构建方法。 6. **数据科学过程概述** - 理解数据科学的各个阶段，如数据收集、清洗及模型建立。 - 学习成果：高层次了解数据科学流程的每个环节。 7. **数据科学过程概述（第二部分）** - 学习更高级的数据清洗、特征工程及模型优化技术。 - 学习成果：掌握数据科学过程中的高级技巧。 8. **Python在数据科学中的应用** - 介绍如何使用Python进行数据科学任务。 - 学习成果：设置Python环境，编写基本的Python代码。 9. **数据科学中的Python库** - 深入学习NumPy、Pandas、Matplotlib和Seaborn等核心库。 - 学习成果：熟练使用Python库进行数据处理和可视化。 10. **R在数据科学中的应用** - 介绍R作为数据科学的另一种工具及其生态系统。 - 学习成果：设置R环境，编写基本的R代码并有效使用RStudio。 本课程旨在为希望深入了解数据科学的学习者提供全方位的知识体系，帮助他们在快速发展的数据驱动行业中获得成功。
1. Introduction to Data ScienceOverview:-This section provides a broad introduction to Data Science, its significance, and its impact across various industries.Topics Covered:-What is Data Science?The Importance of Data in the Modern WorldApplications of Data Science in Various DomainsKey Roles in Data Science: Data Scientists, Data Engineers, and Data AnalystsLearning Outcomes:-Understand the basics of Data Science and its relevance.Identify key roles and applications of Data Science.2. Data Science Session Part 2Overview:-A continuation of the introduction, diving deeper into the tools and technologies used in Data Science.Topics Covered:-Overview of Data Science Tools and TechnologiesIntroduction to Data Wrangling, Exploration, and VisualizationUnderstanding Big Data and its ChallengesReal-World Case Studies in Data ScienceLearning Outcomes:-Gain insight into the tools and techniques used in Data Science.Analyze real-world examples of Data Science in action.3. Data Science Vs Traditional AnalysisOverview:-This section contrasts Data Science with traditional data analysis techniques, highlighting the differences and advancements.Topics Covered:-Traditional Data Analysis TechniquesEvolution from Traditional Analysis to Data ScienceDifferences in Methodology, Tools, and OutcomesAdvantages of Data Science over Traditional AnalysisLearning Outcomes:-Understand the evolution from traditional analysis to Data Science.Identify the key differences and advantages of Data Science.4. Data Scientist Part 1Overview:-Introduction to the role of a Data Scientist, including skills, tools, and responsibilities.Topics Covered:-Who is a Data Scientist?Essential Skills for Data Scientists: Programming, Statistics, and Domain KnowledgeCommon Tools Used by Data ScientistsThe Day-to-Day Responsibilities of a Data ScientistLearning Outcomes:-Understand the role and importance of a Data Scientist.Identify the essential skills and tools required for a Data Scientist.5. Data Scientist Part 2Overview:-A deeper dive into the practical aspects of being a Data Scientist, including challenges and career opportunities.Topics Covered:-Challenges Faced by Data ScientistsEthical Considerations in Data ScienceCareer Pathways and Growth Opportunities for Data ScientistsBuilding a Portfolio and Gaining Practical ExperienceLearning Outcomes:-Gain insight into the challenges and ethical considerations in Data Science.Learn about career opportunities and how to build a strong Data Science portfolio.6. Data Science Process OverviewOverview:-An overview of the Data Science process, including stages and methodologies.Topics Covered:-The Data Science LifecycleUnderstanding Data Collection and Data CleaningExploratory Data Analysis (EDA)Model Building and EvaluationDeployment and Monitoring of Data Science ModelsLearning Outcomes:-Understand the stages involved in the Data Science process.Gain a high-level understanding of each step from data collection to model deployment.7. Data Science Process Overview Part 2Overview:-Continuation of the Data Science process, focusing on more advanced concepts and tools.Topics Covered:-Advanced Data Cleaning TechniquesFeature Engineering and SelectionHyperparameter Tuning and Model OptimizationIntroduction to Model InterpretabilityPost-Deployment Monitoring and MaintenanceLearning Outcomes:-Master advanced techniques in the Data Science process.Learn how to optimize models and ensure they remain effective post-deployment.8. Introduction to Python for Data ScienceOverview:-This section introduces Python as a primary tool for Data Science.Topics Covered:-Why Python for Data Science?Setting Up the Python Environment for Data ScienceIntroduction to Jupyter NotebooksBasic Python Syntax and OperationsOverview of Python Libraries for Data ScienceLearning Outcomes:-Set up and use Python for Data Science tasks.Write basic Python code and use Jupyter Notebooks.9. Python Libraries for Data ScienceOverview:-A focused introduction to essential Python libraries used in Data Science.Topics Covered:Overview of NumPy, Pandas, Matplotlib, and SeabornWorking with NumPy ArraysData Manipulation with PandasData Visualization with Matplotlib and SeabornIntroduction to SciPy and Scikit-Learn for Machine LearningLearning Outcomes:-Gain proficiency in using essential Python libraries for data manipulation and visualization.Prepare data for analysis and build simple visualizations.10. Introduction to R for Data ScienceOverview:-Introduces R as an alternative tool for Data Science, focusing on its strengths and ecosystem.Topics Covered:-Why R for Data Science?Setting Up the R EnvironmentBasic R Syntax and OperationsIntroduction to RStudioOverview of R Libraries for Data Science (e.g., dplyr, ggplot2)Learning Outcomes:-Set up and use R for Data Science tasks.Write basic R code and use RStudio effectively.
