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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-engineer-developer/
课程评论：没有评论
课程名称：数据工程认证：掌握数据管理 概述：在当今数据驱动的世界中，理解如何管理和分析数据对于各种领域的成功至关重要。本课程“掌握数据管理与分析”专为希望深入了解数据管理、关系数据库、数据分析和云计算概念的初学者设计。在课程中，您将学习如何使用各种工具（如MySQL、Excel、Python和Power BI）创建、操作和分析数据。此外，您还将探索云计算基础知识以及Azure如何增强您的数据管理能力。 课程内容涵盖：数据基础知识、数据库设计、MySQL掌握、Excel中的数据分析、Python数据科学入门、Power BI可视化、云计算基本概念、动手项目、数据仓库解决方案和ETL工具。课程共包括超过15个部分和86节讲座，总时长超过8小时，帮助您获得实践经验并建立应对真实数据工程挑战的信心。 数据工程师的技术技能包括精通SQL、编程语言（用于统计建模和数据分析）、数据仓库解决方案的设计、数据管道的构建以及ETL过程的理解。机器学习（ML）作为人工智能的一部分，关注通过经验和数据来自动改进的计算机算法。 通过本课程，您将全面掌握理论与实践技能，为成为一名优秀的数据工程师打下坚实基础。
In today's data-driven world, understanding how to manage and analyze data is essential for success in various fields. This course, "Mastering Data Management and Analytics," is designed for beginners who want to dive deep into the concepts of data management, relational databases, data analytics, and cloud computing.Throughout this course, you'll learn how to create, manipulate, and analyze data using various tools, including MySQL, Excel, Python, and Power BI. You'll also explore cloud computing fundamentals and how Azure can enhance your data management capabilities.This comprehensive course is designed to equip you with the technical skills needed to excel as a data engineer. From foundational SQL knowledge to advanced cloud data solutions, you'll learn to design robust data pipelines, build efficient data warehousing solutions, and leverage ETL tools. Additionally, you'll explore machine learning fundamentals and their application in real-world scenarios.Key Topics Covered:Data Fundamentals: Understand the core concepts of data, databases, and Database Management Systems (DBMS).Database Design: Learn the essential steps for designing effective databases and explore single-table and multi-table database structures.MySQL Mastery: Gain hands-on experience in installing MySQL, creating databases and tables, inserting data, and using SQL commands to fetch and manipulate data.Data Analysis in Excel: Discover Excel's powerful features, including formulas, sorting, filtering, and data visualization techniques.Introduction to Python for Data Science: Get started with Python programming, focusing on data manipulation using NumPy and data visualization.Power BI for Visualization: Learn how to create interactive dashboards and visualizations, import data, and derive insights from your datasets.Cloud Computing Essentials: Understand cloud service models (IaaS, PaaS, SaaS) and their applications in data management, focusing on Azure.Hands-On Projects: Engage in practical projects that will help solidify your understanding and application of the concepts learned throughout the course.Data Warehousing Solutions: Learn to design and implement data warehousing architectures.ETL Tools: Understand the process of extracting, transforming, and loading data from multiple sources into a unified system.Course Content:With over 15 sections and 86 lectures, totaling more than 8 hours of content, you'll gain hands-on experience and the confidence to tackle real-world data engineering challenges.Data Engineer Technical SkillsTo become a data engineer, you should be very good at SQL, and you should know those programming languages used for statistical modeling and data analysis. Also you should know, how to design a data warehousing solutions, and how to build data pipelines.Database design:You should know SQL. SQL is the standard programming language for building and managing relational database systems. Data warehousing solutions:ETL tools.:In computing, extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s). The ETL process became a popular concept in the 1970s and is often used in data warehousing. Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming them into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational data store, a data mart, data lake or a data warehouseMachine learning:Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks
