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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-analytics-introduction
课程评论：没有评论
课程名称：数据分析入门 概述：本课程旨在为您提供实际理解和框架，以指导基本分析任务的执行，例如获取、清理、处理和分析数据。课程介绍了数据分析项目的OSEMN周期，您将学习如何使用电子表格和SQL查询执行数据分析任务。课程还将介绍如何使用Python编程语言处理数据集，作为电子表格的替代方案。您将学习基础编程概念及其在营销中的应用。此外，还将学习如何使用Tableau创建数据可视化和仪表板。 完成本课程后，您将能够： - 明确商业目标、关键绩效指标（KPI）及相关指标 - 应用数据分析流程：OSEMN - 确定和定义与营销相关的待收集数据 - 比较和对比不同数据格式及其使用案例 - 识别收集数据中的差距，并描述其优缺点 - 展示Python的使用能力，包括变量、控制流、循环和基本数据结构 - 在电子表格中以及使用Python库对数据进行排序、查询和结构化 - 编写基本SQL语句以选择、分组和过滤数据 - 使用电子表格可视化数据模式和趋势 - 利用Tableau可视化数据模式和趋势 本课程适合希望学习数据分析基础，包括使用电子表格和Python进行数据排序和结构化，以及使用Tableau进行数据模式可视化的人士。学习者无需具备营销或数据分析经验，但应具备基本的互联网导航技能，并愿意积极参与。学习者还需要访问拥有良好网络连接的计算机。理想情况下，学习者已完成该项目的第一门课程（营销分析基础）。 大纲： 第一部分：数据处理 本周您将了解数据分析入门课程的概述，并学习为营销活动设置目标、目标和关键绩效指标。解释数据科学项目的五个步骤，并介绍OSEMN周期框架。最后，您将看到每个OSEMN周期步骤的实际应用。 第二部分：Python数据分析 本周您将接触Python编程。学习基础编程概念，例如变量、数据类型和函数。 第三部分：数据清理和处理 在第3周，您将深入了解如何使用电子表格、SQL和Python数据分析堆栈（Pandas）清理和处理收集的数据。 第四部分：数据可视化导论 本周您将接触Tableau平台，学习创建数据可视化和仪表板。了解不同类型的可视化及其使用案例。 第五部分：结构化真实世界的分析项目 本周，您将结合在整个课程中学到的所有信息，应用于您的第一个数据分析项目。
Part: 1
Title:Working with Data
Description:This week you’ll get an overview of the Introduction to Data Analytics Course and then you’ll be introduced to setting Goals, Objectives and Key Performance Indicators for marketing campaigns. The 5 steps of a Data Science Project will be explained with the introduction of the OSEMN cycle framework. You’ll finish out the week seeing a real-life application of each step of the OSEMN cycle.
Part: 2
Title:Python for Data Analysis
Description:This week you will be introduced to programming in Python. You will learn foundational programming concepts such as variables, data types, and functions.
Part: 3
Title:Data Cleaning and Processing
Description:In week three, you’ll dig into how to clean and process data you’ve gathered using spreadsheets, SQL, and the Python Data Analytics Stack (Pandas).
Part: 4
Title:Introduction to Data Visualization
Description:This week you’ll be introduced to the Tableau platform which you will use to create data visualizations and dashboards. You’ll learn different types of visualization and their use cases.
Part: 5
Title:Structuring Real-World Analytics Projects
Description:This week you will combine all the information you have learned throughout the course and apply it in your first data analytics project.
This course equips you with a practical understanding and a framework to guide the execution of basic analytics tasks such as pulling, cleaning, manipulating and analyzing data by introducing you to the OSEMN cycle for analytics projects. You’ll learn to perform data analytics tasks using spreadsheet and SQL queries. You will also be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will learn foundational programming concepts and how they apply to marketing. You will also learn how to use Tableau to create data visualizations and dashboards. By the end of this course, you will be able to: • State business goals, KPIs and associated metrics • Apply a Data Analysis Process: OSEMN • Identify and define the relevant data to be collected for marketing • Compare and contrast the different formats and use cases of different kinds of data • Identify gaps in data collected and describe the strengths and weaknesses • Demonstrate proficiency in Python with variables, control flow, loops, and basic data structures • Sort, query and structure data in spreadsheets and with Python libraries • Write basic SQL statements to select, group and filter data • Visualize data patterns and trends with spreadsheets • Utilize Tableau to visualize data patterns and trends This course is designed for people who want to learn the basics of data analytics including using spreadsheets and Python to sort and structure data and using Tableau to visualize data patterns. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Learners also need access to a computer with strong internet connection. Ideally learners have already completed course 1 (Marketing Analytics Foundation) in this program.
