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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-analysis-a-z-become-data-analyst-in-30-days/
课程评论：没有评论
## Coursera 课程总结：30天掌握数据分析师技能 本课程“Data Analysis A-Z: Become Data Analyst in 30 Days”是一项为期30天的密集训练营，旨在帮助学员掌握成为数据分析师所需的关键技能。课程内容涵盖了从基础的Python编程到使用pandas、numpy和Excel等行业标准工具进行高级统计分析。 **第一周（Day 1 - 7）：Excel数据分析** 此阶段专注于利用Microsoft Excel进行数据分析。学员将学习如何： * **清洁和准备原始数据**：处理缺失值、异常值和不一致性。 * **进行描述性统计和推断性统计**。 * **创建动态仪表板和可视化**：运用Excel函数、PivotTables、PivotCharts和切片器等工具。 * **数据可视化技术**：掌握图表、图形等多种可视化方法。 **第二周（Day 9 - 17）：Python基础** 本周将深入学习Python编程语言的基础知识，包括： * **Python编程语言入门**。 * **数据类型、变量和运算符**：深入理解整数、浮点数、字符串、布尔值等。 * **编写简单程序**：使用控制结构（如循环和条件语句if, elif, else）有效管理程序流程。 **第三周（Day 18 - 21）：Python数据结构** 学员将学习Python的核心数据结构，并掌握对其进行操作的方法： * **Python数据结构介绍**：包括列表（Lists）、字典（Dictionaries）、元组（Tuples）和集合（Sets）。 * **访问和修改数据结构中的元素**。 * **将数据结构应用于实际编程问题**。 **第四周（Day 22 - 30）：Python数据分析** 最后阶段将聚焦于如何使用Python及其强大的库（如pandas, numpy, scipy）进行数据分析： * **Python数据分析入门**。 * **操作pandas DataFrame**：掌握数据框架的创建、读取和基本操作。 * **数据处理和清洗**：进行数据的转换、过滤和清洗。 * **探索性数据分析（EDA）技术**。 * **学习统计推断技术**：如ANOVA、相关性分析和回归分析。 贯穿整个训练营，学员将通过大量**动手练习和真实世界的数据分析项目**来巩固所学知识，并将新技能应用于实际场景。完成此课程后，学员将具备作为数据分析师的信心和能力，能够有效地进行**数据驱动的决策**。
Data Analysis A-Z: Become Data Analyst in 30 Days is an intensive training program designed to equip participants with the essential skills and knowledge required to excel as a data analyst. This comprehensive course covers a wide range of topics, from basic Python programming to advanced statistical analysis techniques using industry-standard tools such as pandas, numpy, and Excel.Day 1 - 7: Data Analysis with ExcelThe week of the bootcamp focuses on data analysis using Microsoft Excel. Participants will learn how to clean and prepare raw data, perform descriptive and inferential statistics, and create dynamic dashboards and visualizations using Excel functions and tools. Topics covered include:- Cleaning and preparing raw data in Excel- Handling missing data, outliers, and inconsistencies- Descriptive and inferential statistics in Excel- Creating dynamic dashboards with PivotTables and PivotCharts- Data visualization techniques in Excel (charts, graphs, slicers)Day 9 - 17: Python FundamentalsIn this week, participants will gain a solid understanding of Python's basic syntax, data types, variables, and operators. They will learn how to write simple programs and perform basic operations using Python. Topics covered include:- Introduction to Python programming language- Understanding data types (integers, floats, strings, booleans)- Working with variables and operators- Utilizing control structures like loops and conditional statements (if, elif, else)- Managing program flow effectively with control structuresDay 18 - 21: Working with Data StructuresDuring this week, participants will delve into fundamental data structures in Python, including lists, dictionaries, tuples, and sets. They will learn how to manipulate, access, and modify these structures for diverse programming needs. Topics covered include:- Introduction to data structures in Python- Working with lists, dictionaries, tuples, and sets- Accessing and modifying elements in data structures- Applying data structures to solve practical programming problemsDay 22 - 30: Data Analysis with PythonIn this week, participants will learn how to perform data analysis tasks using Python and industry-standard libraries such as pandas, numpy, and scipy. They will acquire skills in working with dataframes, performing data manipulation, and employing metrics such as counts, percentages, group by, pivot tables, correlation, and regression. Topics covered include:- Introduction to data analysis with Python- Working with pandas dataframes- Data manipulation and cleaning- Exploratory data analysis techniques- Statistical inference techniques (ANOVA, correlation, regression)Throughout the bootcamp, participants will engage in hands-on exercises and real-world data analysis projects to reinforce their learning and apply their newfound skills in practical scenarios. By the end of the program, participants will have the confidence and proficiency to work as data analysts and make data-driven decisions effectively.
