🤔Data tagging: advantages and downsides
Data tagging is the process of assigning labels or annotations to specific elements in a data set to train machines to understand and extract information from that data. Data labeling plays an important role in machine learning, deep learning, and data mining because it allows algorithms to understand which objects or factors in the data are important and which are unimportant.
Benefits of data tagging:
1. Improve model accuracy: Data labeling helps create more accurate and reliable models because algorithms can learn from the correct labels and avoid errors.
2. Training algorithms: Labeled data allows machine learning algorithms to be trained more efficiently, making them capable of solving complex problems such as pattern recognition, text classification, forecasting and others.
3. Expanding the functionality of applications: Labeled data allows you to develop more intelligent applications and services, such as virtual assistants, automated systems and much more.
Disadvantages of data tagging:
1. Resource-intensive: Data tagging requires significant effort and resources, especially when it comes to large data sets or complex tasks.
2. Subjectivity: Data labeling may depend on the subjective judgments of the labelers, which can lead to errors and inaccuracies.
3. Task limitation: Data labeling is limited to a specific training task, and changing this task may require re-labeling the data.
4. Updating Data: Labeled data can become outdated over time, and the labeling needs to be updated periodically to keep models up to date.

Overall, data labeling is an integral part of many machine learning projects, and its benefits often outweigh its disadvantages, especially when the labeling process is properly organized and managed.

🌎TOP DS-events all over the world in November
Nov 9 - Big Data Analytics & AI - London, UK - https://whitehallmedia.co.uk/bdanov2023/
Nov 14-15 - SLAS-FHNW 2023 Data Sciences and AI Symposium - Basel, Switzerland - https://www.slas.org/events-calendar/slas-2023-data-sciences-and-ai-symposium/
Nov 20-21 - Gartner IT Infrastructure, Operations & Cloud Strategies Conference - London, UK - https://www.gartner.com/en/conferences/emea/infrastructure-operations-cloud-uk
Nov 20-24 - THE BIGGEST AI EVENT WORLDWIDE - Belgrade, Serbia - https://datasciconference.com/
Nov 21-24 - BIG DATA CONFERENCE EUROPE - Vilnius, Lithuania - https://bigdataconference.eu/
Nov 23-24 - Data Science Summit - Warsaw, Poland - https://dssconf.pl/en/
Nov 25-26 - 4th International Conference on Data Science and Applications - London, UK - https://www.cndc2023.org/dsa/index
Nov 27-29 - THE GLOBAL BIG DATA ANALYTICS IN POWER & UTILITIES INDUSTRY FORUM - Berlin, Germany - https://berlin-energy-summit.com/etn/the-global-big-data-analytics-in-power-utilities-industry-forum-27-28-29-november-2023/
Nov 30 - Dec 1 - AI & Big Data Expo Global - London, UK - https://www.ai-expo.net/global/speakers/

📝🔎💥Data temperature management is now even easier
Great Expectations (GX) is an open source Python-based tool for data quality control. It provides a team of data scientists with the ability to analyze and validate data, as well as create reports using it. This tool has a user-friendly command line interface (CLI) that allows you to create new tests and edit existing sources. It's important to note that Great Expectations can be integrated with a variety of data extraction, transformation, and loading (ETL) tools such as Airflow and various database management systems. A complete list of related integrations and official documentation can be found on the Great Expectations website

📝📚Selection of books on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques - The book provides an introduction to the fundamentals of Data Mining and uses the popular Weka tool to train machine learning algorithms
Introduction to Data Mining - a classic book that covers the basic concepts and techniques of Data Mining
Principles of Data Mining - this book provides an extensive discussion of the principles and methods of Data Mining
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management - the book is focused on the use of Data Mining in marketing and customer relationship management
Data Science for Dummies - a good option for beginners, the book covers many topics including Data Mining, machine learning and data analysis

🤖⚡️🔎Selection of AI-based services for Big Data analysis
AskEdith - Simplifies data analysis by allowing users to ask questions and get instant information. Expands the capabilities of “self-service analytics” by providing secure and reliable access to data. Compatible with all databases and CRMs (Google Sheets, Airtable, PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery and Redshift, etc.)
Tomat.AI - An artificial intelligence-powered tool that allows data scientists to easily explore and analyze large CSV files without the need for coding or writing formulas. You can open and view huge CSV files with just a few clicks
Coginiti - Allows users to generate SQL queries using natural language hints, optimize existing SQL queries, explain common SQL in an integrated catalog, provide detailed explanations and solutions to errors, and explain plans query execution for better optimization. The AI assistant continually evolves based on every interaction, tailoring recommendations and suggestions to suit individual needs
Speak Ai - A language data analysis and research platform that offers trannoscription, data mining, and sentiment analysis capabilities for various media types. It allows automatic trannoscription, bulk analysis, visualization and data collection for use in research, market analysis and competitive analysis. The tool also offers a shared media repository, an AI-powered text hint system, and a SWOT analysis solution, among other features
Formula God - An artificial intelligence tool built into Google Sheets. It uses artificial intelligence to help users manipulate and calculate data across a full range of cells
Simple ML for Sheets - useful for machine learning experts who want to quickly iterate or prototype on small (e.g. <1 million examples) tabular data sets. Simple ML for Sheets is a Google Sheets add-on from the TensorFlow Decision Forests team.

📝🔎Apache Flink: advantages and disadvantages
Apache Flink is a distributed real-time data processing system that provides capabilities for streaming data processing and real-time analysis.
Benefits of Apache Flink:
1. Stream Data Processing: Flink is designed to efficiently process data in real time, allowing you to quickly respond to changes and events.
2. High Performance: Flink provides high performance through optimized query execution and efficient task distribution across the cluster.
3. Flexibility and Scalability: Flink provides flexibility in defining and modifying stream computing. Also worth noting is the increase in performance as the volume of processed data increases.
Disadvantages of Apache Flink:
1. Complexity of Setup: Setting up and managing an Apache Flink cluster can require significant effort and experience.
2. Lack of widespread popularity: Compared to some other real-time data processing systems, Apache Flink is not as widely used, which may affect the availability of resources and the support community.
3. Integration Challenges: Integrating Apache Flink with existing systems and tools can be challenging, requiring data reworking to be compatible with other systems' formats and structures.
Overall, Apache Flink provides powerful real-time data processing capabilities, but requires careful implementation and management to achieve maximum performance and reliability.

💥😎Selection of open datasets for various areas
This collection is a list of high-quality open datasets for machine learning, time series, NLP, image processing, etc., focused on specific topics.
Datasets are available at this link

📝A little about ClickHouse: advantages and disadvantages
ClickHouse is an open source columnar database designed for processing analytical queries with large volumes of data.
Advantages of ClickHouse:
1. High performance: ClickHouse is optimized for running analytical queries on large volumes of data. It provides high query speed due to its columnar data structure and other optimizations.
2. Scalability: ClickHouse easily scales horizontally, allowing you to add new cluster nodes to process a growing volume of data.
3. Efficient use of resources: Thanks to columnar layout and data compression, ClickHouse can efficiently use storage resources, which reduces disk space consumption.
4. Low read overhead: Thanks to its data structure and optimizations, ClickHouse provides high read performance.
Disadvantages of ClickHouse:
1. Limited transaction support: ClickHouse is focused on analytical queries and does not have full transaction support, which can be a disadvantage for applications that require strong data consistency.
2. Limited write support: ClickHouse is designed primarily for reading data, and write operations may be less efficient than other database management systems for large change volumes.
3. Insufficient indexing support: ClickHouse has limited indexing support compared to some other DBMSs, which can affect the performance of search operations.
4. Difficult to maintain and set up: Setting up ClickHouse may require some skill and understanding of its architecture, which may make it less attractive to less experienced administrators.
Overall, the choice of ClickHouse depends on the specific needs of the project. If your tasks involve analytics and processing large volumes of data, ClickHouse may be an excellent option. However, if highly consistent transactions and writes are required, other solutions may be worth considering.

😎🔎Selection of useful OLAP services for processing Big Data
Apache Druid is a real-time OLAP engine. It is focused on time series data, but can be used for any data. It uses its own columnar format that can highly compress data, and it has many built-in optimizations such as inverted indexes, text encoding, automatic data folding, and more.
Apache Pinot - Offers lower latency thanks to the Startree index, which does partial precomputation, so it can be used for user-facing applications (it was used to fetch LinkedIn feeds). This uses a sorted index instead of an inverted one, which is faster.
Apache Tajo - Designed to perform ad hoc queries with low latency and scalability, online aggregation and ETL for large data sets stored in HDFS and other data sources. It supports integration with Hive Metastore to access shared schemas.
Solr is a very fast open source enterprise search platform built on Apache Lucene. Solr is robust, scalable, and fault-tolerant, providing distributed indexing, replication and load-balanced queries, automatic failover and recovery, centralized configuration, and more.
Presto is an open source platform from Facebook. It is a distributed SQL query engine for running interactive analytical queries against data sources of any size. Presto lets you query data where it lives, including Hive, Cassandra, relational databases, and file systems. It can query large data sets in seconds. Presto is independent of Hadoop, but integrates with most of its tools, especially Hive, to run SQL queries.

🌎TOP DS-events all over the world in December
Dec 4-5 - ICDSTA 2023: 17 - Tokyo, Japan - https://waset.org/data-science-technologies-and-applications-conference-in-december-2023-in-tokyo
Dec 6-7 - The AI Summit New York - New York, USA - https://newyork.theaisummit.com/
Dec 6 - DSS NYC: Applying AI & ML to Finance & Technology - New York, USA - https://www.datascience.salon/newyork/
Dec 7-8 - ADSN 2023 Conference - University of Adelaide, Australia - https://www.australiandatascience.net/event/2023-adsn-conference/
Dec 8-10 - CDICS 2023 - Online - https://www.cdics.org/
Dec 11-15 - DSWS-2023 - Tokyo, Japan - https://ds.rois.ac.jp/article/dsws_2023
Dec 25-26 - ICVDA 2023: 17. International Conference on Vehicle Data Analytics - France, Paris - https://waset.org/vehicle-data-analytics-conference-in-december-2023-in-paris

🤔Grouparoo Review: Advantages and Disadvantages
Grouparoo is a data management tool that provides an automated process for collecting, processing and synchronizing data across different applications and data sources.
Benefits of Grouparoo:
1. Automate data synchronization processes: Grouparoo provides the ability to create rules for automatic data synchronization between different sources. This reduces manual labor and keeps data up to date in real time.
2. Flexibility and Customizability: The tool allows the user to customize synchronization rules to suit an organization's unique needs and data structure. Flexible customization makes Grouparoo a powerful tool for various business scenarios.
3. Improved data accuracy: An automated data synchronization process helps prevent errors associated with manual data entry and ensures greater data accuracy across multiple systems.
4. Integration with various data sources: Grouparoo provides support for integration with various applications and data sources, which allows you to manage data from various sources in a single format.
Disadvantages of Grouparoo:
1. Setup Difficulty: Grouparoo's setup process can sometimes be difficult, especially for users without technical experience. This may require time and effort to fully implement the tool.
2. Technical understanding required: Full use of Grouparoo requires an understanding of the technical aspects of data synchronization and rules configuration, which can be a challenge for users without relevant experience.
3. Dependency on Third Party Data Sources: Grouparoo depends on the availability and structure of data in third party applications. Problems with these sources can affect the performance of the tool.
Overall, Grouparoo is a powerful data management tool that can greatly simplify your data synchronization and processing processes. However, before use, it is important to carefully weigh the advantages and disadvantages, taking into account the specifics and needs of a particular organization.

💥💯💡A new open source library for working with data has appeared on the Internet
Cleanlab is a library that helps clean data and labels by automatically detecting problems in a machine learning dataset. To make machine learning easier on messy data, this data-centric II package uses additional models to evaluate problems in data sets that can be corrected to train even better models.
As a result, the AI library performs the following functions:
1. Detection of data problems (mislabeling, omissions, duplicates, drift)
2. Setting up and testing the training model.
3. Conduct active training of models

💥📝📊An archive of 32 datasets that you can use to practice your skills
Data Science Dojo has created an archive of 32 data sets that you can use to practice and improve your data science skills.
The repository provides a wide range of topics, complexity levels, dimensions, and attributes. The datasets are categorized according to different difficulty levels to suit different skill levels.
Datasets offer the opportunity to gain practical knowledge to improve your skills in areas such as exploratory data analysis, data visualization, data science, deep learning, and more.

📝🔎Kappa Big Data architecture: advantages and disadvantages
Kappa architecture is a coherent data processing model where all data is considered as a sequential stream of events.
K-architecture finds its application in scenarios where:
1. It is necessary to manage the queue of events and requests in a distributed file system
2. High availability and resilience are critical, since data processing occurs on every node in the system.
For example, Apache Kafka, as an efficient message broker, meets these requirements by providing a high-performance, reliable and scalable platform for data collection and aggregation. Thus, Kappa architecture built on top of Kafka is ideal for projects like LinkedIn, where large amounts of information need to be efficiently processed and stored to serve many simultaneous requests.
Advantages of Kappa architecture in Big Data:
1. Scalability: the architecture is easily scaled horizontally, which allows you to process large volumes of data. This is especially important with the increasing volume of information that many businesses face.
2. Low latency: Systems built on the Kappa architecture are capable of low latency in data processing. This is important for tasks that require a quick response to changes in data.
3. Easy updates: Since the data is processed in real time, making changes to the data processing becomes easier. This makes it easier to deploy new versions and system updates.
4. Support complex analytical tasks: Kappa architecture is suitable for complex analytical tasks such as real-time machine learning, anomaly analysis and others. It provides the ability to quickly respond to changes in data.
Disadvantages of Kappa architecture in Big Data:
1. Data duplication: One of the major disadvantages is data duplication. Because data first enters raw data storage and then goes through processing, this can lead to storage overuse.
2. Difficulty in managing data schemas: Since data enters the system in a raw format and is then transformed, managing data schemas can be a challenge, especially when there are changes in the data structure.
3. Resource Requirements: Real-time data processing can require significant computing resources. This can be a challenge for organizations with limited budgets.
Thus, the Kappa architecture makes a significant contribution to the development of the Big Data field by providing efficient data processing in real time. However, like any architecture, it has its advantages and disadvantages, which should be taken into account when choosing the appropriate solution for a particular project.

⚡️💡Free tool for visualizing user journey data
MyTracker is a multi-platform analytics and attribution system for mobile applications and websites. This service is also a tool for collecting and processing data on marketing activity and user actions in the application and on the website. MyTracker works for free, without restrictions on the volume and period of data storage. Main components of MyTracker:
1. SDK - software library for tracking mobile applications.
2. Web counter for tracking data on websites.
3. Web interface for creating a working environment, viewing and downloading analytical reports.

😎⚡️💥Top little-known but quite useful Python libraries for Big Data analysis
Pattern - designed for data extraction on the Internet, natural language processing, machine learning and social network analysis. Tools include a search engine, APIs for Google, Twitter and Wikipedia, and text analysis algorithms that can be executed in a few lines of code.
SciencePlots is a library that provides styles for the Matplotlib library to produce professional plots for presentations, research papers, etc.
Pgeocode is a Python geocoding module that is designed to process geographic data and helps to combine and correlate different data. Using the pgeocode module, you can obtain and provide information related to a region or area using postal code information. Distances between two postal codes are also supported.
pynimate - module for animating line graphs of statistical data

⚡️📝💡Platforms for marking data for computer vision tasks
VoTT is a free, open-source image annotation tool developed by Microsoft. It provides comprehensive support for creating datasets and validating video and image-based object detection models.
LabeIimg is a graphical image annotation tool for labeling objects using Bounding Boxes. It is written in Python. Labeled data is exported as XML files in PASCAL VOC format.
Labelme is an online data annotation tool created by MIT's Computer Science and Artificial Intelligence Laboratory. Labelme supports six different types of annotations: polygons, rectangles, circles, lines, dots and linear stripes.
DataLoop is a universal cloud-based annotation platform with built-in tools and automation for creating high-quality training datasets.
Supervise.ly is a web platform for annotating images and videos with your community. Researchers and large groups can annotate and experiment with datasets and neural networks.

📝💡🔎Selection of datasets for autopilots
Berkeley DeepDrive BDD100k - One of the largest datasets for autopilots. Includes more than 100 thousand videos with more than a thousand hours of driving recordings at different times of day and in different weather conditions
Baidu Apolloscapes - a dataset for recognizing 26 semantically different objects such as cars, buildings, pedestrians, bicycles, street lights, etc.
Comma.ai. - more than 7 hours of driving on the highway. The dataset contains information about car speed, GPS coordinates, acceleration, steering angle
Oxford’s Robotic Car - more than a hundred repetitions of one route around Oxford, filmed over the course of a year. The dataset contains different combinations of traffic, pedestrians, weather conditions, as well as road works
Cityscape Dataset - recordings of one hundred street scenes in fifty cities

🌎TOP DS-events all over the world in2024
Jan 9-12 - CES 2024 - LAS VEGAS, USA - https://www.ces.tech/
Jan 11-12 - ICSDS 2024: 18. International Conference on Statistics and Data Science - Zurich, Switzerland - https://waset.org/statistics-and-data-science-conference-in-january-2024-in-zurich?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 15-16 - ICCDS 2024: 18. International Conference on Computational and Data Sciences - Montevideo, Uruguay - https://waset.org/computational-and-data-sciences-conference-in-january-2024-in-montevideo?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 15-16 - ICCIDS 2024: 18. International Conference on Communication Informatics and Data Science - Rome, Italy - https://waset.org/communication-informatics-and-data-science-conference-in-january-2024-in-rome?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 24 - Data Science Salon Seattle: Retail & ecommerce - Seattle, USA - https://www.datascience.salon/seattle/
Jan 25 - AI, Machine Learning & Data Science Meetup - Online - https://www.meetup.com/london-ai-machine-learning-data-science/events/297485409/
Jan 24-25 - The Festival of Genomics & Biodata - London, UK - https://festivalofgenomics.com/
Jan 29-Feb 2 - SUPERWEEK 2024 - https://superweek.hu/
Jan 31 - National Data Science PhD Meetup - Nyborg, Denmark - https://ddsa.dk/phd-meetup-2-0/
Feb 2-5 - ICBDM 2024 - Shenzhen, China - https://www.icbdm.org/
Feb 8-10 - World Artificial Intelligence Cannes Festival - Cannes, France - https://www.worldaicannes.com/en
April 24-25 - Data Innovation Summit - Stockholm, Sweden -https://datainnovationsummit.com/
May 23-24 - The Data Science Conference - Chicago, USA - https://www.thedatascienceconference.com/
June 17-19 - World Conference on Data Science & Statistics - Amsterdam, Netherlands - https://datascience.thepeopleevents.com/
July 9-11 - DATA 2024 – Conference - Dijon, France - https://data.scitevents.org/
31 July-1 Aug - Gartner Data Analytics Summit - Sydney, Australia - https://www.gartner.com/en/conferences/apac/data-analytics-australia

📉📊The world of data with Tableau: advantages and disadvantages
Tableau is an innovative data visualization software that has become an integral part of modern data analysis.
Advantages of Tableau:
Intuitive Interface: One of the key benefits of Tableau is its intuitive and easy to understand interface. Users can create complex visualizations without extensive programming knowledge.
Rich Visualization Options: Tableau provides a variety of options for data visualization, ranging from standard graphs to complex dashboards. This allows users to present data in the most visual form.
Integration with various data sources: Tableau supports a wide range of data sources, including databases, Excel files, cloud and many more. This provides convenience in working with data from various sources.
Dynamic Dashboards and Reports: With Tableau, users can create dynamic dashboards and reports that allow them to instantly track changes and analyze data in real time.
Extensive Community and Support: Tableau has an active user community, providing access to extensive resources, training, and forums for problem solving and sharing experiences.
Disadvantages of Tableau:
Need for Data Preparation: In some cases, pre-processing of data is required before it can be visualized in Tableau. This may require time and additional effort.
Limited analytics capabilities: Compared to some other data analytics tools, Tableau may be less capable of complex analytical calculations.
Limited real-time capabilities: In some scenarios, Tableau may face limitations in processing data in real-time, which may be an issue for certain business scenarios.
Overall, Tableau remains a powerful and popular data visualization tool, providing rich functionality for analyzing data and making informed decisions. The decision to use it depends on the specific needs and capabilities of the business.

📚A selection of books for immersion in the world of time series analysis
Time series analysis and forecasting - considered time series indicators, main types of trends and methods for their recognition, methods for estimating fluctuation parameters, measuring the stability of series levels and dynamic trends, modeling and time series forecasting. Designed for persons with knowledge of the general theory of statistics.
Practical analysis of time series: forecasting with statistics and machine learning - modern technologies for analyzing time series data are described here and examples of their practical use in a variety of subject areas are given. It is designed to help solve the most common problems in the study and processing of time series using traditional statistical methods and the most popular machine learning models.
Elementary theory of analysis and statistical modeling of time series - the book contains the theoretical and probabilistic foundations of the analysis of the simplest time series, as well as methods and techniques for their statistical modeling (simulation ). The material on elementary probability theory and mathematical statistics is presented briefly using the analogy of probabilistic schemes and supplemented with results on the theory of series and criteria of randomness.
Statistical analysis of time series - monograph by a famous American specialist in mathematical statistics contains a detailed presentation of the theory of statistical inference for various probabilistic models. Methods for representing time series, estimating the parameters of corresponding probabilistic models, and testing hypotheses regarding their structure are outlined. The extensive material collected by the author, previously scattered across various sources, makes the book a valuable guide and reference book.
Time series. Data processing and theory () - the monograph is devoted to the study of times series found in various fields of physics, mechanics, astronomy, technology, economics, biology, medicine. The main orientation of the book is practical: methods of theoretical analysis are illustrated with detailed examples, and the results are clearly presented in numerous graphs.