This course explores the fundamentals of relational databases and how to seamlessly map Python data structures to robust database tables using object-relational mappers (ORMs). You'll gain practical experience in building efficient ETL (Extract, Transform, Load) pipelines, ensuring your data is not only accessible but also reliable and persistent. You'll learn about data validation and quality control, leveraging powerful tools like Pandas to explore, clean, and analyze your datasets. By the end of the course, you’ll be equipped to uncover insights, identify biases, and apply best practices in data management.
Data Science Fundamentals Part 1: Unit 3
Data Science Fundamentals Part 1: Unit 3
本课程是 Data Science Fundamentals, Part 1 专项课程 的一部分
位教师：Pearson
访问权限由 New York State Department of Labor 提供
您将学到什么
Master the fundamentals of relational databases and persistent data storage.
Build and optimize ETL pipelines using Python and object-relational mappers.
Apply data validation techniques to ensure data quality and integrity.
Utilize Pandas for effective data exploration, transformation, and statistical analysis.
您将获得的技能
- Data Processing
- Extract, Transform, Load
- Exploratory Data Analysis
- Database Management
- Data Persistence
- Object-Relational Mapping
- Data Cleansing
- Data Transformation
- Data Analysis
- Databases
- Relational Databases
- Data Validation
- Data Manipulation
- Data Integrity
- SQL
- Data Quality
- Pandas (Python Package)
- Descriptive Statistics
- Data Pipelines
- 技能部分已折叠。显示 10 项技能，共 19 项。
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作业
3 项作业
授课语言：英语（English）
最近已更新！
August 2025
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本课程是 Data Science Fundamentals, Part 1 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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该课程共有1个模块
This module guides learners through essential data handling skills, from storing and persisting data using relational databases and object-relational mappers, to validating, exploring, and transforming data for analysis. Emphasizing practical techniques with tools like Pandas, the lessons cover best practices for querying, managing missing values, and using descriptive statistics and visualizations to understand data quality and distribution. The module provides a systematic approach to the ETL process, equipping students to efficiently prepare data for deeper analytical modeling.
涵盖的内容
28个视频3个作业
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