😎💡🔥A selection of unpopular but very useful Python libraries for working with data

Bottleneck is a library that speeds up NumPy methods up to 25 times, especially when processing arrays containing NaN values. It optimizes calculations such as finding minima, maxima, medians, and other aggregate functions. By using specialized algorithms and handling missing data, Bottleneck significantly speeds up work with large data sets, making it more efficient than standard NumPy methods.

Nbcommands is a tool that simplifies code search in Jupyter notebooks, eliminating the need for users to search manually. It allows you to find and manage code by keywords, functions, or other elements, which significantly speeds up working with large projects in Jupyter and helps users navigate their notes and code blocks more efficiently.

SciencePlots is a style library for matplotlib that allows you to create professional graphs for presentations, research papers, and other scientific publications. It offers a set of predefined styles that meet the requirements for data visualization in scientific papers, making graphs more readable and aesthetically pleasing. SciencePlots makes it easy to create high-quality graphs that meet the standards of academic publications and presentations.

Aquarel is a library that adds additional styles to visualizations in matplotlib. It allows you to improve the appearance of graphs, making them more attractive and professional. Aquarel simplifies the creation of custom styles, helping users create graphs with more interesting designs without having to manually configure all the visualization parameters.

Modelstore is a library for managing and tracking machine learning models. It helps organize, save, and version models, as well as track their lifecycle. With Modelstore, users can easily save models to various storages (S3, GCP, Azure, and others), manage their updates and restore. This makes it easier to deploy and monitor models in production environments, making working with models more convenient and controllable.

CleverCSV is a library that improves the process of parsing CSV files and helps avoid errors when reading them with Pandas. It automatically detects the correct delimiters and format of CSV files, which is especially useful when working with files that have non-standard or heterogeneous structures. CleverCSV simplifies working with data by eliminating errors associated with incorrect recognition of delimiters and other file format parameters.

🌎TOP DS-events all over the world in October
Oct 1-2 - AI and Big Data Expo Europe - Amsterdam, Netherlands - https://www.ai-expo.net/europe/
Oct 7-10 - Coalesce - Las Vegas, USA - https://coalesce.getdbt.com/
Oct 9-10 - World Summit AI - Amsterdam, Netherlands - https://worldsummit.ai/
Oct 9-10 - Big Data & AI World - Singapore, Singapore - https://www.bigdataworldasia.com/
Oct 10-11 - COLLIDE 2024: The South's largest data & AI conference - Atlanta, USA - https://datasciconnect.com/events/collide/
Oct 14-17 - Data, AI & Analytics Conference Europe 2024 - London, UK - https://irmuk.co.uk/data-ai-conference-europe-2024/
Oct 16-17 - Spatial Data Science Conference 2024 - New York, USA - https://spatial-data-science-conference.com/2024/newyork
Oct 19 - Oktoberfest - London, UK - https://datasciencefestival.com/event/oktoberfest-2024/
Oct 19 - INFORMS Workshop on Data Science 2024 - Seattle, Washington, USA - https://sites.google.com/view/data-science-2024
Oct 20-25 - TDWI Transform - Orlando, USA - https://tdwi.org/events/conferences/orlando/information/sell-your-boss.aspx
Oct 21-25 - SIAM Conference on Mathematics of Data Science (MDS24) - Atlanta, USA - https://www.siam.org/conferences-events/siam-conferences/mds24/
Oct 23-24 - NDSML Summit 2024 + AI2R Expo - Stockholm, Sweden - https://ndsmlsummit.com/
Oct 28-29 - Cyber Security Summit - San Paulo, Brazil - https://www.cybersecuritysummit.com.br/index.php
Oct 29-31 - ODSC West - California, United States - https://odsc.com/

💡😎3 unpopular but very necessary visualization libraries

Supertree is a Python library designed for interactive and convenient visualization of decision trees in Jupyter Notebooks, Jupyter Lab, Google Colab and other notebooks that support HTML rendering. With this tool, you can not only visualize decision trees, but also interact with them directly in the notebook.

Mycelium is a library for creating graphical visualizations of machine learning models or any other directed acyclic graphs. It also provides the ability to use the Talaria graph viewer to visualize and optimize models

TensorHue is a Python library designed to visualize tensors directly in the console, making it easier to analyze and debug them, making the process of working with tensors more visual and understandable.

😎⚡️A powerful dataset generated using Claude Opus.

Synthia-v1.5-I is a dataset of over 20,000 technical questions and answers designed to train large language models (LLM). It includes system prompts styled like Orca to encourage the generation of diverse answers. This dataset can be used to train models to answer technical questions more accurately and comprehensively, improving their performance on a variety of technical and engineering problems.

✅To load the dataset using Python:

from datasets import load_dataset
ds = load_dataset("migtissera/Synthia-v1.5-I")

⚡️HTTP SQLite StarbaseDB

StarbaseDB is a powerful and scalable open source database that is based on SQLite and runs over the HTTP protocol. This database is built to run in a cloud environment (e.g. on Cloudflare), allowing it to scale efficiently down to zero based on load. Key benefits of StarbaseDB include:

✅Ease of use: Provides the ability to work through HTTP requests, making it easy to integrate with various systems and services.
✅Scalability: Automatically adjusts to load volume with the ability to scale both ways.
✅Support for SQLite: Utilize the time-tested and lightweight SQLite database for data storage.
✅Open Source: Open source, allowing developers to customize and improve the system to suit their needs.

It is suitable for developers who are looking for a simple and reliable way to organize databases with minimal customization and high availability in cloud platforms such as Cloudflare.

💡 News of the day: MongoDB creates AI partner ecosystem

MongoDB is actively adapting to the challenges of artificial intelligence development by introducing an improved version of its database (8.0) and launching the MongoDB AI Application Program (MAAP). This program aims to create a global partner ecosystem aimed at standardizing AI solutions. Key partners include major cloud and consulting players such as Microsoft Azure, Google Cloud Platform, Amazon Web Services, Accenture, and AI companies Anthropic and Fireworks AI.

Updates to MongoDB 8.0 promise notable performance improvements:

✅ A 32% increase in throughput.
✅Acceleration of batch writes by 56%.
✅ Increase parallel write speed by 20%.

This gives MongoDB the ability to better handle the high loads often encountered with big data and AI. Solutions have already been deployed for large companies, including one of France's leading automakers and a global home appliance manufacturer.

In this way, MongoDB, by building MAAP and improving its technology, aims to become a key player in the AI industry, supporting developers and companies in their quest for innovation.

🔎Read more here

😎 Optimizing Analytics with Oracle

Oracle posted an article on their blog where they talk about how to connect to a BDS cluster using Hive and Spark connections from Oracle Analytics Cloud (OAC).

Oracle Big Data Service clusters contain a Hadoop Distributed File System (HDFS) and a Hive database that load and transform data from different sources and in different formats (structured, semi-structured, and unstructured).

Learn how to connect Oracle Analytics Cloud to Oracle Big Data Service using Hive and Spark to improve data analytics. Combining powerful tools can help you efficiently process and visualize large amounts of data.

😎Top Python libraries for optimizing work with data

✅Pony ORM is a convenient and powerful library for working with object-relational databases, which allows you to write SQL queries using Python syntax. It automatically converts Python code into SQL queries, which simplifies interaction with databases, making it more intuitive and concise. Pony ORM supports major DBMSs such as PostgreSQL, MySQL, SQLite and others, providing flexibility and convenience when creating queries and working with data models.

✅Pypika is a library for creating SQL queries programmatically in Python, which allows you to avoid errors in hand-writing SQL code and protects against SQL injections. It is especially useful for building dynamic and parameterized queries, making it an ideal tool for database applications. Pypika allows you to build queries with a high degree of detail and complexity, while maintaining the readability and security of your code.

✅EdgeDB is a modern database and client library for Python that simplifies managing data schemas and writing queries. It offers a more intuitive and convenient approach compared to traditional SQL databases, providing advanced capabilities for working with data. Key features of EdgeDB include automatic schema generation, working with relational data without the need to write complex SQL queries, as well as support for type safety and a more expressive syntax for manipulating data.

✅Tortoise ORM is a modern asynchronous ORM (Object-Relational Mapping) designed for working with databases in asynchronous Python applications. It supports various relational databases such as PostgreSQL, MySQL, SQLite, and is written with an emphasis on simplicity and ease of use. Tortoise ORM allows you to build complex SQL queries using Python code, automatically synchronizing data models with the database. Support for asynchrony makes it especially useful in high-load or web applications where it is important to efficiently manage resources and database queries.

✅Polars is a high-performance data processing and analysis library in Python and Rust, focused on working with large volumes of data. Thanks to multithreading and an optimized architecture, Polars provides significantly higher execution speeds compared to traditional tools such as Pandas. The library supports a wide range of operations on tabular data (dataframes), offering an intuitive interface for filtering, aggregating and transforming data. It is ideal for tasks that require high performance, especially when working with large data sets.

🔥A small selection of data annotation tools with all the details

CVAT (Computer Vision Annotation Tool) is one of the most popular and sought-after image annotation tools used to create datasets in the field of computer vision.

Advantages of CVAT:
✅Customization: CVAT, as an open-source solution, gives users complete freedom to customize the platform to their needs. This makes the tool flexible and adaptable, allowing it to be integrated into various workflows. The CVAT documentation provides detailed instructions on customization, making the setup process more accessible even for beginners.

✅Detailed documentation: CVAT documentation includes detailed denoscriptions of functionality, use cases, life hacks, and images. Regular documentation updates ensure that users are always aware of the latest changes and improvements.

Disadvantages of CVAT:
✅High resource requirements: One of the main disadvantages of CVAT is its high server resource requirements, which can be a problem for some teams.

Supervisely is a multi-functional platform for working with computer vision projects, offering solutions for the entire lifecycle of AI projects, from data labeling to model training and deployment.

Advantages:
✅A rich ecosystem of applications: Supervisely Apps already offers many ready-made widgets that allow you to extend the functionality of any part of the platform. Each of them is open source and available on GitHub, which makes it possible not only to modify existing applications but also to create new ones.

Disadvantages:
✅High cost: Despite its extensive capabilities, Supervisely may be a less profitable choice financially compared to other tools.

Label Studio is a powerful and flexible open-source tool for data annotation in various machine learning tasks, including computer vision, text, and audio processing. It is used to label data for subsequent training of models.

Advantages:
✅Flexibility: Users can create labels themselves using code, which opens up new possibilities for customization.
✅Extensibility: The modular structure allows for easy addition of new features and integration of additional label types.

Disadvantages:
✅High resource requirements: Label Studio may require a significant amount of resources to fully use, which makes it less convenient for users with disabilities.
✅Limitations in Bounding Boxes labeling: While, for example, CVAT offers a more convenient and fast tool for Bounding Boxes labeling, Label Studio is better suited for labeling audio data.

💡🔥Working with geographic data efficiently

GeoPy is a Python library that allows you to work with geographic data and provides tools for performing tasks such as geocoding (converting addresses to coordinates), reverse geocoding (converting coordinates to addresses), and calculating distances between geographic points.

😎Main features of working with geodata via GeoPy:

✅Geocoding: Converts addresses or places into geographic coordinates (latitude and longitude). This is useful when you need to, for example, visualize data on a map.

✅Reverse geocoding: Converts coordinates into a human-readable address. This can be useful for creating more understandable data or interfaces.

✅Reverse geocoding: Converts coordinates into a human-readable address. This can be useful for creating more understandable data or interfaces.

🖥You can learn more about geographic data analysis from this article

😎Nvidia have published a new dataset for training faintune models

HelpSteer2 is an English-language dataset developed by NVIDIA and hosted on the Hugging Face platform. It includes 21,362 rows and is designed to train reward models that help improve the utility, factual accuracy, and coherence of answers generated by large language models (LLMs).

Each row in the dataset contains a query, a response, and five human annotated response attributes:
✅Utility (usefulness)
✅ Correctness
✅ Coherence
✅ Complexity
✅ Verbosity

The dataset can be used to fine-tune LLMs to generate more relevant and better responses to user queries.

🌎TOP DS-events all over the world in November
Nov 4-8 - PASS Data Community Summit 2024 - Seattle, USA - https://passdatacommunitysummit.com/
Nov 6 - Enterprise AI & Big Data - London, UK - https://whitehallmedia.co.uk/bdanov2024/
Nov 6-8 - PyData NYC, New York, USA - https://pydata.org/nyc2024
Nov 7 - Data Science Day 2024 - https://events.altair.com/data-science-day-2024/
Nov 7 - Data & Analytics Congres 2024 - Liemes, Utrecht - https://datainsightsnetwork.nl/events/dac-2024/
Nov 14 - IMPACT: The Data Observability Summit - Online - https://impactdatasummit.com/
Nov 18-19 - Machine Learning Week Europe - Munich, Germany - https://machinelearningweek.eu/
Nov 18-22 - LEADING GLOBAL AI EVENT - Belgrade, Serbia - https://datasciconference.com/
Nov 18-22 - QCon - San Francisco, USA - https://qconsf.com/
Nov 20 - Tech & AI LIVE 2024 - New York, USA - https://live.technologymagazine.com/tech-ai-newyork-2024/
Nov 20-23 - FMLDS - Sydney, Australia - https://www.fmlds.org/
Nov 20-21 - Data & Analytics Insight Summit - San Diego, USA - https://gdsgroup.com/events/physical-summit/data-analytics-na-nov-24/
Nov 21 - Data Science Summit - Warsaw, Polland - https://dssconf.pl/
Nov 28-29 - AI ML, Data Science & Robotics Conferences 2024 - Porto, Portugal - https://aiml.events/events/ai-ml-data-science-robotics-conferences-2024

💡A small selection of useful things for working with Big Data

postgres-backup-local is a Docker tool for creating backups of PostgreSQL databases, storing them in the local file system with the ability to flexibly manage copies. With its help, you can back up multiple databases from one server by specifying their names through the POSTGRES_DB environment variable (separated by a comma or space).
The tool supports webhooks before and after backup, automatically manages the rotation and deletion of old copies, and is also available for Linux architectures, including amd64, arm64, arm/v7, s390x, and ppc64le.

EfCore.SchemaCompare is a tool for comparing database schemas in Entity Framework Core (EF Core), allowing you to find and analyze differences between the current database and migrations. It provides a convenient way to track changes in data structures, which helps prevent errors caused by schema mismatches during application development.
Suitable for database versioning, especially useful when developing and upgrading EF Core-based applications.

Greenmask is an open-source tool for PostgreSQL designed for masking, obfuscation, and logical backup of data. It allows you to anonymize sensitive information in database dumps, making it useful for preparing data for use in non-production environments such as development and testing. Greenmask support helps protect data by meeting privacy requirements and reducing the risk of leaks during development.

😎How Spotify accelerated data markup for ML by 10x

Spotify shared how it accelerated data markup for machine learning models using large language models (LLMs) in conjunction with the work of annotators. Automated initial LLM partitioning significantly reduced processing time by allowing annotators to focus on complex or ambiguous cases. This combined solution tripled process throughput and reduced costs. This scalable solution is especially relevant for a rapidly growing platform and is used to monitor compliance with service rules and policies.

💡 Spotify's data partitioning strategy is based on three core principles:

✅Scaling human expertise: annotators validate and refine results to improve data accuracy.

✅Annotation tools: creating efficient tools that simplify the work of annotators and allow models to be integrated more quickly into the process.

✅Fundamental infrastructure and integration: the platform is designed to handle large amounts of data in parallel and run dozens of projects simultaneously.

This approach has allowed Spotify to run multiple projects simultaneously, reduce costs, and maintain high accuracy.
More information about Spotify's solution can be found in their whitepaper.

😂A Radical Solution from AI

Every day, thousands of programmers can breathe a sigh of relief when AI performs tasks for them like queries, data formatting, or other routine tasks😁

🖥ChatGPT was asked to write SQL queries for a store database. The answer just killed

😎Sometimes AI's views on solving a particular problem are slightly different from human ones

😎The Power of Data: Analyzing Quarterly Revenue Growth for Business Success

💡I recently came across an article in which the author talks about analyzing quarterly revenue growth. He argues that focusing only on annual data can hide trends and slow down decision making. Quarterly analysis allows you to better understand the current performance of the business and identify potential problems, such as a decrease in revenue in a certain period. This granularity helps you identify causes (such as seasonal fluctuations or marketing shortcomings) faster and take action faster than when analyzing only annual data. Quarterly data creates a foundation for optimizing growth strategies, moving from reactive to more effective data-driven management.

The author also highlights key metrics for analyzing quarterly revenue growth:

✅Customer Acquisition Cost (CAC): It is important to understand the cost of acquiring new customers to optimize marketing and sales efforts, which helps increase ROI and revenue growth.
✅Customer Lifetime Value (CLTV): This metric shows the total revenue a customer brings in over their entire relationship with the company, helping to identify high-yield segments for targeting and retention.
✅Sales Conversion: Analyzing conversion at each stage of the funnel helps identify bottlenecks and improve overall sales efficiency, which contributes to revenue growth.

🖥Link to the article

🧐Anthropic CEO Dario Amodei interviews Lex Fridman

😎Highlights:

✅Dario expressed optimism about the imminent emergence of AI capable of reaching human levels. He noted that development and training costs will increase in the coming years, and by 2027, clusters will likely be built worth around $100 billion - significantly larger than the current largest supercomputers, which cost around $1 billion.

✅Amodei believes that models will continue to scale, despite the lack of a theoretical explanation for this process - there is, according to him, some "magic" in it.

✅AI models are currently improving at an astonishing rate, especially in areas such as programming, physics, and mathematics. On the SWE-bench test, their success at the beginning of the year was only 2-3%, and now reaches about 50%. The main concern in these conditions is the possible monopoly on AI, when control over it ends up in a small number of large companies, which could threaten

🖥You can watch the interview here