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Michael Conterio,
Martin O'Hanlon,
Ross Exton,
and
Eirini K
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for those who have a little Python experience and are comfortable using functions with parameters–as covered in our Programming 102 course. Please note: screengrabs and recorded screencasts are key elements of this course. You can use the hashtag #RPiLearn to talk about this course on social media.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for those who have a little Python experience and are comfortable using functions with parameters–as covered in our Programming 102 course. Please note: screengrabs and recorded screencasts are key elements of this course. You can use the hashtag #RPiLearn to talk about this course on social media.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for those who have a little Python experience and are comfortable using functions with parameters–as covered in our Programming 102 course. Please note: screengrabs and recorded screencasts are key elements of this course. You can use the hashtag #RPiLearn to talk about this course on social media.
Topics Covered
- Creating, reading from, and writing to files using Python
- The importance of data persistence
- Structuring data using CSV files, Python dictionaries, and JSON files
- How data structures aid compatibility between systems
- Interacting with databases using SQL and Python
Register for this course and see more details by visiting:
OpenCourser.com/course/4ntjth/programming
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Reviews summary
Practical python data handling fundamentals
According to students, Programming 103: Saving and Structuring Data provides a solid and practical foundation in handling data with Python. Learners frequently praise its clear explanations, especially regarding file I/O, Python dictionaries, and JSON file structures. The course's hands-on activities and recorded screencasts are highlighted as particularly effective for reinforcing concepts. While many find the introduction to SQL and database interaction useful, a few note it can be too basic or feels rushed, sometimes requiring additional self-study for deeper understanding. Overall, it's considered highly valuable for skill development, building well on prerequisite Python knowledge.
Ideal for foundational learning, but may be slow for experienced coders.
"Some parts felt a little slow, but overall it provided a solid foundation."
"The course is okay for an absolute beginner, but for someone with some experience, it's very slow; the lectures felt drawn out."
"I found some of the earlier examples a bit too simplistic, making the course feel like it was moving slowly at times, but it picks up later."
"This course provides a comprehensive introduction to data handling, accessible even for those with just a little prior Python knowledge."
The database section has improved but remains introductory.
"I struggled with the database section. It felt rushed and assumed more SQL knowledge than I had."
"The SQL part felt a bit rushed and I had to supplement with other resources to gain depth."
"They seem to have improved the database section somewhat; it's clearer now, but still very much an introduction."
"Learning about SQL with Python gave me a vital skill, even if it was just an initial exposure, not a deep dive."
Strong practical exercises and clear demonstrations enhance understanding.
"The practical exercises cemented my understanding. Highly recommend for anyone looking to handle data!"
"The hands-on activities for reading and writing data were particularly useful for applying concepts."
"The recorded screencasts are helpful, making complex topics accessible and easy to follow along with the code."
"I greatly benefited from the practical nature, as it moved beyond theory to real coding scenarios that I could replicate."
Provides practical, clear lessons on file I/O, CSV, and JSON in Python.
"The lessons on file I/O and using JSON for data serialization were incredibly clear and immediately applicable."
"I found the sections on CSV and dictionaries very helpful for structuring simple data."
"The emphasis on data persistence is crucial, and the examples with Python dictionaries and JSON files were perfectly explained."
"I truly appreciated the clarity of the instructor; the file I/O lessons are excellent and form a strong core."
Activities
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Programming 103: Saving and Structuring Data.
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Learners who complete Programming 103: Saving and Structuring Data will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/4ntjth/programming
Reading list
We haven't picked any books for this reading list yet.
Covers designing and building data-intensive applications. It discusses various types of data persistence, including file-based, database, and cloud-based, and how to choose the right type for an application.
Focuses on using Hibernate, an object-relational mapping (ORM) framework, for data persistence. It teaches the basics of Hibernate and how to map objects to a relational database.
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Explores the inner workings of various data systems, including databases and distributed systems. It's excellent for deepening one's understanding of how data persistence is implemented at a lower level and in distributed environments. It valuable resource for those interested in the mechanics of data storage and retrieval.
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Covers using Spring Data, a framework for data persistence in Java, for data persistence. It teaches the basics of Spring Data and how to use it in application development.
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Covers using JPA, a standard for object-relational mapping (ORM) in Java, for data persistence. It teaches the basics of JPA and how to use it in application development.
Covers data persistence in the cloud using various cloud computing platforms. It teaches the basics of cloud computing and how to use it for data persistence.
Covers best practices for data persistence, including how to choose the right data persistence method and how to design and implement a data persistence layer. It also covers common pitfalls and how to avoid them.
Covers using NHibernate, an ORM framework for .NET, for data persistence. It teaches the basics of NHibernate and how to use it in application development.
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Covers using Redis, an in-memory data structure store, for data persistence. It teaches the basics of Redis and how to use it in application development.
Provides a comprehensive overview of the fundamental concepts and challenges in designing data systems. It covers various data storage and processing technologies, making it excellent for gaining a broad understanding of data persistence in modern applications. It is highly regarded in the industry and is valuable as both a learning resource and a reference.
Introduces the fundamental concepts for designing, using, and implementing database systems. It covers database modeling, design, and implementation techniques, providing a solid foundation for understanding data persistence. It is suitable as a textbook for introductory to advanced database courses.
Delves into the theoretical underpinnings of the relational model and SQL. It's excellent for deepening one's understanding of relational databases, which are central to much of data persistence. It is more theoretical and best suited for those who want a rigorous understanding.
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Provides a concise introduction to the concepts behind NoSQL databases and when to use them. It's valuable for understanding contemporary approaches to data persistence beyond traditional relational databases. It serves as a good starting point for exploring the diverse landscape of NoSQL options.
While not solely focused on data persistence, this classic book covers architectural patterns for enterprise applications, including patterns for data access and persistence. It provides valuable context for how data persistence fits into larger application designs. It foundational text for software architecture.
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Discusses fundamental principles of software architecture and design, which are relevant to designing maintainable and testable data persistence layers. It provides valuable insights into structuring code that interacts with databases and other persistence mechanisms. It widely recommended book for software developers.
Definitive guide to dimensional modeling, a technique widely used in data warehousing and business intelligence. It provides a deep dive into designing databases for analytical purposes, offering a different perspective on data persistence focused on reporting and analysis.
Focuses specifically on Entity Framework Core, a popular ORM for .NET development. It's highly relevant for developers working with ASP.NET Core and C#, covering practical aspects of data persistence in that ecosystem. It useful reference for developers using EF Core.
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This book, while a general Android programming guide, includes sections on data persistence using SQLite and other Android-specific mechanisms. It's valuable for those focusing on mobile data persistence on the Android platform. It serves as a practical guide with hands-on examples.
Covers data warehousing, a specific type of data persistence used for business intelligence and data analysis. It teaches the basics of data warehousing and how to design and implement a data warehouse.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4ntjth/programming
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