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Featured:
- ======== Linked lists ======= Why do we define a NODE CLASS (Python)? ====== https://github.com/ormigi/ormigi/blob/main/Linked%20list
When we talk about linked lists, think of them as a chain of items, like a string of beads. Each bead is a piece of data, like a number or a word. Now, imagine you want to write a program to manage these beads, to add new ones, remove them, or find specific ones. To do this, you need a way to represent each bead and how they connect to each other. That's where the NODE CLASS comes in. It's like a blueprint for each bead. It tells the program what each bead looks like and how it's connected to the next one in the chain.
Here's why we need it:
Keeps things organized: The node class helps keep track of each bead's data (like a number or word) and how they're linked together.
Makes things easier to work with: By defining a node class, we create a standard way to handle each bead. It's like having a specific tool for each job - it makes things easier to understand and manage.
Lets us add extra features: With a node class, we can add extra information or functions to each bead if we need to. This gives us more flexibility in how we use our linked list.
Helps avoid mistakes: Using a node class helps prevent errors because it keeps everything organized and consistent. It's like having a checklist to make sure each bead is in the right place. So, in simple terms, we define a node class in a linked list to make it easier to work with and manage the individual items in the list. It's like creating a blueprint for each item so we can keep track of them and do different things with them in our program.
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Pandas Pivot Table with Visualization Tutorial.ipynb from https://github.com/AbhisheakSaraswat/RawData
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👀 Focus on the 2 major ways to access DATA in databases:
- OLTP -transactional access, data is modified often, in small amounts/ CRUDE
- vs OLAP -analytical access, large amounts of data + complex is queried
Question answered: Why do we need to duplicate our data /remodel it for analytics? Answered by https://shorturl.at/egC12 , picture courtesy https://shorturl.at/egC12
NB: Newer technologies and optimization techniques have allowed OLTP systems to handle a broader range of analytical queries efficiently. In some cases, with proper indexing and query optimization, OLTP databases can provide satisfactory performance for certain analytics use cases without the need for a separate analytical system. However, for complex analytical workloads involving extensive data aggregation and summarization, OLAP systems still offer distinct advantages. It's essential to evaluate the specific requirements of your analytics workload and choose the appropriate data storage system accordingly.
- Data tells me how to approach it, https://www.youtube.com/clip/Ugkx5T1yLWn0EHynoW3_HDVliZZqCHowo21g, "Why Do Businesses Fail At Machine Learning?" by Cassie Kozyrkov from Google
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