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Data structures are fundamental concepts in computer science that allow for the organization, storage, and manipulation of data in a systematic and efficient manner. They provide a way to represent and manage collections of data, as well as perform operations on that data. There are various types of data structures, each with its own characteristics and purposes. Some common data structures include: 1. Arrays: A collection of elements stored at contiguous memory locations, indexed by integers. Arrays offer constant-time access to elements but may have fixed size limitations. 2. Linked Lists: A data structure consisting of a sequence of elements, where each element points to the next one in the sequence. Linked lists allow for dynamic memory allocation and efficient insertion and deletion operations. 3. Stacks: A last-in, first-out (LIFO) data structure where elements are added and removed from the same end, often referred to as the "top" of the stack. Stacks are used in various applications such as expression evaluation and function call management. 4. Queues: A first-in, first-out (FIFO) data structure where elements are added to the rear and removed from the front. Queues are commonly used in scenarios like task scheduling and breadth-first search algorithms. 5. Trees: Hierarchical data structures consisting of nodes connected by edges, where each node has zero or more child nodes. Trees are used for organizing hierarchical data and performing hierarchical operations. 6. Graphs: A collection of nodes (vertices) and edges that connect pairs of nodes. Graphs can be directed or undirected and are used to represent various relationships and networks. These are just a few examples, and there are many other data structures available, each suited for different tasks and scenarios. Choosing the appropriate data structure is crucial for designing efficient algorithms and solving computational problems effectively. #data #datastructure #graphs #arrays #queues #stacks #linkedlists

𝗡𝗼𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 The two main categories of nonlinear data structures are trees and graphs 𝗧𝗿𝗲𝗲𝘀 - Trees are hierarchical data structures consisting of nodes connected by edges, with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗿𝗼𝗼𝘁 𝗻𝗼𝗱𝗲 𝗮𝘁 𝘁𝗵𝗲 𝘁𝗼𝗽. - Each node can have 𝘇𝗲𝗿𝗼 𝗼𝗿 𝗺𝗼𝗿𝗲 𝗰𝗵𝗶𝗹𝗱 𝗻𝗼𝗱𝗲𝘀, and there are 𝗻𝗼 𝗰𝘆𝗰𝗹𝗲𝘀 𝗼𝗿 𝗹𝗼𝗼𝗽𝘀 allowed within the structure. - Trees are commonly used to represent 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀, such as organizational charts, file systems, and family trees. - Examples of trees include binary trees, AVL trees, Red-Black trees, B-trees, and trie structures. 𝗚𝗿𝗮𝗽𝗵𝘀 - Graphs are collections of nodes (vertices) connected by edges (links), where each edge represents a relationship between two nodes. - Graphs can be directed or undirected, depending on whether the edges have a direction or not. - Graphs are used to model 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 between entities, such as social networks, transportation networks, and communication networks. - Examples of graph types include directed graphs, undirected graphs, and weighted graphs. 𝗡𝗼𝗱𝗲 𝗮𝗻𝗱 𝗘𝗱𝗴𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 - Both trees and graphs are composed of nodes (vertices) and edges. - Nodes represent individual elements or entities, while edges represent connections or relationships between nodes. Both trees and graphs can be traversed using similar algorithms, such as depth-first search (DFS) and breadth-first search (BFS).

Being able to visualize your data and communicate your findings to stakeholders is of utmost importance. Here are 5 exploratory data analysis visualizations that can help with your overall model performance! #machinelearning #datascience #exploratorydataanalysis

𝗗𝗮𝘁𝗮 𝘃𝗮𝘂𝗹𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 aims to be the most flexible modeling technique, adapting to changes and new datasets easily while storing all historical data by default. There are 3 core types of tables in data vault: 𝗵𝘂𝗯𝘀, 𝗹𝗶𝗻𝗸𝘀, and 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀. 📃 𝗛𝘂𝗯𝘀: tables that contain a list of unique business keys (natural keys), surrogate keys, and metadata describing the data source for each hub item. 🔗 𝗟𝗶𝗻𝗸𝘀: tables that associate hubs and satellites via the business key. 🛰️ 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀: tables that hold the descriptive data about the entities being modeled as well as start and end date columns to track historical changes. Read more about this data engineering concept: https://lnkd.in/g2R_spjz #DataEngineering #DataModeling

3 Data structures & 3 Essential Algorithms to get a competitive edge in technical interviews- These go beyond the standard data structures like LinkedLists, BSTs, HashTables etc. Everybody knows these standard ones- Hence, you need a competitive edge! Data structures: 1. Trie    - Efficient for prefix-based searches and autocomplete.    - Supports operations like insert, search, and delete.    - Uses nodes to represent characters and edges for word paths. 2. Bloom Filters    - Space-efficient probabilistic data structure for set membership.    - Allows false positives but no false negatives.    - Useful for large-scale data and caching applications. 3. Quadtree    - Partition data in a 2D space into four quadrants recursively.    - Useful for spatial indexing and image processing.    - Handles dynamic datasets with varying density. Algorithms: 1. HyperLogLog    - Approximate counting algorithm for unique elements in a dataset.    - Provides memory-efficient estimation with probabilistic accuracy.    - Suitable for large-scale analytics and stream processing. 2. Consistent Hashing    - Distributes data across a set of nodes to minimize reorganization.    - Useful for distributed systems and load balancing.    - Ensures minimal disruption when nodes are added or removed. 3. Leaky/Token Buckets     - Controls the rate of requests or operations to prevent overload.     - Used in traffic shaping applications. Follow Arvind Telharkar for more such insights!

Unveiling the Secret of Spectral Clustering: 7 Fascinating Facts You Must Know In the realm of data analysis, clustering algorithms serve as the cornerstone for unveiling patterns, relationships, and structures within vast datasets. These algorithms, by aggregating similar data points, provide a prism through which researchers, analysts, and businesses can interpret complex data landscapes. Among the plethora of clustering techniques, spectral clustering stands out for its unique approach and effectiveness. Here are seven fascinating facts about spectral clustering that underscore its importance and innovative edge https://lnkd.in/dWhCnz65 #DataScience #MachineLearning #SpectralClustering #Analytics #TechnologyTrends

Great article Mary K. Pratt on the importance of Data Observability and the 12 most prominent use cases. I think "Run simulations to plan capacity" and "Data Drift" are two use cases that companies should seriously start to consider as top points to address. Planning for more data is essential and as is understanding you algorithms. https://lnkd.in/gp3wXmmi

The Power of Parallel Processing: Accelerating Data Analysis https://lnkd.in/d-_UuXMZ

𝐐𝐝𝐫𝐚𝐧𝐭 1.11 is all about making a statement. This release focuses on features that improve memory usage and optimize segments. - 𝐃𝐞𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Storage for multitenant workloads is more optimized and scales better. - 𝐎𝐧-𝐃𝐢𝐬𝐤 𝐏𝐚𝐲𝐥𝐨𝐚𝐝 𝐈𝐧𝐝𝐞𝐱 Store less frequently used data on disk, rather than in RAM. - 𝐔𝐔𝐈𝐃 𝐟𝐨𝐫 𝐏𝐚𝐲𝐥𝐨𝐚𝐝 𝐈𝐧𝐝𝐞𝐱: Additional data types for payload can result in big memory savings. 𝘛𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘢𝘭𝘴𝘰 𝘢 𝘧𝘦𝘸 𝘮𝘰𝘳𝘦 𝘢𝘥𝘥𝘪𝘵𝘪𝘰𝘯𝘴 𝘵𝘰 𝘵𝘩𝘦 𝘳𝘦𝘤𝘦𝘯𝘵𝘭𝘺 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥 𝘘𝘶𝘦𝘳𝘺 𝘈𝘗𝘐: - 𝐆𝐫𝐨𝐮𝐩𝐁𝐲 𝐄𝐧𝐝𝐩𝐨𝐢𝐧𝐭: Use this query method to group results by a certain payload field. - 𝐑𝐚𝐧𝐝𝐨𝐦 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠: Select a subset of data points from a larger dataset randomly. - 𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐞𝐚𝐫𝐜𝐡 𝐅𝐮𝐬𝐢𝐨𝐧: We are adding the Distribution-Based Score Fusion (DBSF) method. 𝘐𝘯 𝘤𝘢𝘴𝘦 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦𝘯'𝘵 𝘤𝘩𝘦𝘤𝘬𝘦𝘥 𝘰𝘶𝘵 𝘵𝘩𝘦 𝘞𝘦𝘣 𝘜𝘐, 𝘧𝘦𝘦𝘭 𝘧𝘳𝘦𝘦 𝘵𝘰 𝘵𝘳𝘺 𝘰𝘶𝘳 𝘯𝘦𝘸𝘦𝘴𝘵 𝘵𝘰𝘰𝘭𝘴 𝘵𝘰 𝘦𝘹𝘱𝘭𝘰𝘳𝘦 𝘺𝘰𝘶𝘳 𝘥𝘢𝘵𝘢: 𝐒𝐞𝐚𝐫𝐜𝐡 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐓𝐨𝐨𝐥: Test the precision of your semantic search requests in real-time. 𝐆𝐫𝐚𝐩𝐡 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥: Visualize vector search in context-based exploratory scenarios. 𝐁𝐥𝐨𝐠: https://lnkd.in/dnDM_6yh

We just added Domain Classifier as a multivariate drift detection method in NannyML 0.10.3! Here’s how it works: 1. We take model inputs (no targets) from the reference data and the chunk in the monitored that we want to examine for covariate drift. 2. We create the target: ▪ 1 for data coming from the current chunk ▪ 0 for the data coming from the reference dataset 3. We train models in a cross-validated fashion, predicting the binary target from (2) and using the monitored model inputs as features. 4. We measure the cross-validated model AUROC (or ROC AUC, if preferred). 5. We plot AUROC as a time-series for each chunk. You can see the result in the plot below. Now, why does it work? AUROC measures how easy it is to discriminate between the positive and negative classes. For us, this means how different the reference and current chunk are. This is exactly what we want to measure conceptually when trying to detect data drift. Is it better than the Reconstruction Error approach for data drift detection? It’s slightly different. We’re still doing more in-depth analysis, but preliminary results indicate that: 1. Domain Classifier is more sensitive to changes in the data structure and can detect more subtle changes. 2. Domain Classifier is better at detecting non-linear changes in relationships between features. 3. Domain Classifier is significantly more expensive computationally, as we need to train a new GradientBoosting model for every chunk. Domain classifier tutorial: https://lnkd.in/d-rSPRbF More about Reconstruction Error Approach for Detecting Data Drift: https://lnkd.in/d6akmKJW