#Big Data Market

[seoexpertswizard]

Big Data Market Size, Share, Price, Trends, Report and Forecast 2023-2028

 Big data refers to large, diverse sets of data that are growing at an exponential rate. The volume of data, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered are all factors to consider.

[wisteriagoesvroom]

got jumpscared by these ‘cus it looks like he is a junior analyst at an investment bank

from mclaren’s ig

[catie-does-things]

Just saw an ad for an "AI powered" fertility hormone tracker and I'm about to go full Butlerian jihad.

[skillacademybytestbookpune]

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[hydralisk98]

Czarina-VM, study of Microsoft tech stack history. Preview 1

Write down study notes about the evolution of MS-DOS, QuickBASIC (from IBM Cassette BASIC to the last officially Microsoft QBasic or some early Visual Basic), "Batch" Command-Prompt, PowerShell, Windows editions pathing from "2.11 for 386" to Windows "ME" (upgraded from a "98 SE" build though) with Windows "3.11 for Workgroups" and the other 9X ones in-between, Xenix, Microsoft Bob with Great Greetings expansion, a personalized mockup Win8 TUI animated flex box panel board and other historical (or relatively historical, with a few ground-realism & critical takes along the way) Microsoft matters here and a couple development demos + big tech opinions about Microsoft too along that studious pathway.

( Also, don't forget to link down the interactive-use sessions with 86box, DOSbox X & VirtualBox/VMware as video when it is indeed ready )

Yay for the four large tags below, and farewell.

[ofcoursethevibes]

Week 5 blog post "Saga of Big Data 🙃"

After watching "The Legal Side of Big Data", Maciej Ceglowski's talk and reading "The Internet's Original Sin" .  I was intrigued by the complexities surrounding the use of big data in today's business landscape. As a business owner myself, I realize that harnessing the power of big data can unlock numerous opportunities for growth and innovation. However, there are crucial aspects that businesses must be acutely aware of when using big data.

First and foremost, data privacy and security must be at the forefront of any big data strategy. As businesses collect and analyze vast amounts of consumer data, they must ensure strict adherence to applicable laws and regulations. Compliance with data protection laws such as GDPR, CCPA, or other relevant regional laws is not just an ethical responsibility but also vital for avoiding potential legal repercussions and preserving consumer trust.

Transparency is another critical aspect that businesses must prioritize. Consumers have the right to know how their data is being used, stored, and shared. Clear and concise privacy policies and terms of use should be provided, ensuring that consumers can make informed decisions about their data's usage.

Furthermore, businesses should guard against using big data to engage in discriminatory practices. The insights derived from big data must be utilized responsibly and without any bias that could harm certain demographic groups or individuals. It's essential to continuously monitor data usage and algorithmic decisions to avoid reinforcing harmful stereotypes.

On the consumers' side, awareness of the implications of sharing their data is paramount. They should be mindful of what data they provide to businesses and exercise caution when consenting to data usage. Staying informed about privacy settings and exercising their rights to access, rectify, or delete personal data empowers consumers to have control over their information.

As for balancing the opportunities and threats of big data, a multi-faceted approach is necessary. Collaboration between businesses, policymakers, and consumer advocacy groups is key to developing comprehensive regulations that foster innovation while safeguarding privacy. Encouraging ethical data practices and responsible use of big data should be incentivized, and non-compliance should be met with appropriate consequences.

Additionally, promoting data literacy among the general public can foster a better understanding of the potential benefits and risks associated with big data. By educating consumers about data collection practices, they can make more informed decisions about sharing their information and demand greater accountability from businesses.

In conclusion, the world of big data offers immense potential for businesses, but it also poses significant challenges in terms of privacy, security, and ethics. By being aware of these considerations, businesses can navigate the legal complexities and build trust with their customers. Simultaneously, consumers must stay vigilant about their data and support initiatives that strike a balance between seizing the opportunities and mitigating the threats of big data. Only through collective efforts and responsible practices can we harness the full potential of big data while safeguarding individual rights and societal welfare.

[uniquesdataoutsourcing]

Ecommerce Product Listing service - Uniquesdata

Amazon is one of the best product-selling websites, offering many products to ensure consumer happiness. Amazon creates a fresh identity and awareness for your product. Everyone wants their business to be on eCommerce, and Amazon is one method everyone can use to expand their buyer base.

Amazon Product Listing Services can offer a variety of features and services to meet your product listing requirements. Each product is submitted to Amazon, and the appropriate product tags are used to assist shoppers. When you sell, your inventory is changed, and your products are refilled. The Amazon Bulk Upload Service allows you to upload multiple products at once, allowing you to focus on other duties, such as image editing.

[tomwambsmilk]

Don’t know how I feel about Spotify Wrapped becoming A Phenomenon

[bommagoni]

Big Data in Agriculture Market Size, Share, Analysis, Growth

[sangeersoft]

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[cacmsinsitute]

Data Analytics Toolbox: Essential Skills to Master by 2025

As data continues to drive decision-making in every business, mastering data analytics becomes more important than ever for ambitious professionals. Students preparing to enter this dynamic sector must have a firm foundation in the necessary tools and abilities. Here, we describe the most important data analytics skills to learn in 2025, explain their significance, and provide a road map for building a versatile and relevant analytics toolkit.

1. Programming languages: Python and R

Python and R are the two most popular programming languages in data analytics, with each having distinct strengths and capabilities.

Python: The preferred language for data analysis, data manipulation, and machine learning, Python is well-known for its readability, adaptability, and extensive library. Libraries like Scikit-Learn for machine learning, NumPy for numerical calculations, and Pandas for data manipulation give analysts the strong tools they need to work effectively with big datasets.

R: Widely used in research and academia, R is used for data visualisation and statistical analysis. It is a strong choice for statistical analysis and for producing detailed, publication-ready visualizations thanks to its packages, like ggplot2 for visualization and dplyr for data processing.

Why It Matters: Students who are proficient in Python and R are able to manage a variety of analytical activities. While R's statistical capabilities can improve analysis, especially in professions that focus on research, Python is particularly useful for general-purpose data analytics.

2. Structured Query Language, or SQL

Data analysts can efficiently retrieve and manage data by using SQL, a fundamental ability for querying and maintaining relational databases.

SQL Fundamentals: Data analysts can manipulate data directly within databases by mastering the core SQL commands (SELECT, INSERT, UPDATE, and DELETE), which are necessary for retrieving and analyzing data contained in relational databases.

Advanced SQL Techniques: When working with structured data, SQL is a tremendous help. Proficiency in JOIN operations (for merging tables), window functions, and subqueries is essential for more complicated data chores.

Why It Matters: The main tool for retrieving and examining data kept in relational databases is SQL. Since almost all organizations store their data in SQL-based systems, analysts in nearly every data-focused position must be proficient in SQL.

3. Data Preparation and Cleaning

Cleaning, converting, and organizing data for analysis is known as "data wrangling," or data preparation, and it is an essential first step in the analytics process.

Managing Outliers and Missing Values: Accurate analysis relies on knowing how to handle outliers and missing values.

Data Transformation Techniques: By ensuring that data is in a format that machine learning algorithms can understand, abilities like normalization, standardization, and feature engineering serve to improve model accuracy.

Why It Matters: Analysts invest a lot of effort on cleaning and preparing data for any data analytics project. An accurate, reliable, and error-free analysis is guaranteed by efficient data preparation.

4. Visualization of Data

Complex datasets are transformed into understandable, relevant pictures through data visualization, which facilitates narrative and decision-making.

Visualization Libraries: Analysts may produce educational, expert-caliber charts, graphs, and interactive dashboards by learning to use tools like Matplotlib, Seaborn, Plotly (Python), and ggplot2 (R).

Data Storytelling: To effectively communicate findings, data analysts need to hone their storytelling abilities in addition to producing images. An effective analyst is able to create narratives from data that help decision-makers make decisions.

Why It Matters: Insights can be effectively communicated through visualizations. By becoming proficient in data visualization, analysts may communicate findings to stakeholders in a way that is compelling, accessible, and actionable.

5. Fundamentals of Machine Learning

Data analysts are finding that machine learning (ML) abilities are becoming more and more useful, especially as companies seek for predictive insights to gain a competitive edge.

Supervised and Unsupervised Learning: To examine and decipher patterns in data, analysts need to be familiar with the fundamentals of both supervised (such as regression and classification) and unsupervised (such as clustering and association) learning.

Well-known Machine Learning Libraries: Scikit-Learn (Python) and other libraries make basic ML models easily accessible, enabling analysts to create predictive models with ease.

Why It Matters: By offering deeper insights and predictive skills, machine learning may improve data analysis. This is especially important in industries where predicting trends is critical, such as marketing, e-commerce, finance, and healthcare.

6. Technologies for Big Data

As big data grows, businesses want analytics tools that can effectively manage enormous datasets. Big data tool knowledge has grown in popularity as a highly sought-after ability.

Hadoop and Spark: Working with big data at scale is made easier for analysts who are familiar with frameworks like Apache Hadoop and Apache Spark.

NoSQL databases: An analyst's capacity to handle unstructured and semi-structured data is enhanced by knowledge of NoSQL databases such as MongoDB and Cassandra.

Why It Matters: Data volumes in many businesses beyond the capacity of conventional processing. In order to meet industrial expectations, big data technologies give analysts the means to handle and examine enormous datasets.

7. Probability and Statistics

Accurately evaluating the findings of data analysis and drawing reliable conclusions require a solid foundation in probability and statistics.

Important Ideas: By understanding probability distributions, confidence intervals, and hypothesis testing, analysts can apply statistical concepts to actual data.

Useful Applications: Variance analysis, statistical significance, and sampling techniques are essential for data-driven decision-making.

Why It Is Important: Analysts can assess the reliability of their data, recognise trends, and formulate well-informed predictions with the use of statistical skills. Accurate and significant analysis is based on this knowledge.

8. Communication and Critical Thinking Soft Skills

Technical proficiency alone is insufficient. Proficient critical thinking and communication capabilities distinguish outstanding analysts.

Communication Skills: To ensure that their insights are understood and useful, analysts must effectively communicate their findings to both technical and non-technical audiences.

Problem-Solving: Critical thinking allows analysts to approach problems methodically, assessing data objectively and providing insightful solutions.

Why It Matters: In the end, data analytics is about making smarter decisions possible. Effective data interpreters and communicators close the gap between data and action, greatly enhancing an organization's value.

Conclusion: Developing a Diverse Skill Set for Success in Data Analytics

Both technical and soft skills must be dedicated in order to master data analytics. Students that master these skills will be at the forefront of the field, from core tools like SQL and visualization libraries to programming languages like Python and R. With data-driven professions becoming more prevalent across industries, these abilities make up a potent toolkit that can lead to fulfilling jobs and worthwhile projects.

These fundamental domains provide a solid basis for students who want to succeed in data analytics in 2025. Although mastery may be a difficult journey, every new skill you acquire will help you become a more proficient, adaptable, and effective data analyst.

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[serpentinegraphite]

Okay, I'm having trouble matching sources for all of OP's claims.

I'm certainly not calling OP a liar or claiming that American Education Is Good, Actually, because I'm pretty familiar with the commonly cited statistic that American adults can't read above a 5th grade level (here's a snopes article sourcing some Gallup data and the linked PIAAC data that's a little more readable especially if you're on mobile), but it's worth emphasizing here that:

The PIAAC skills results (i.e., proficiency levels) do not specifically correspond to measures such as grade levels at school. The PIAAC proficiency levels have a use-oriented conception of competency and focus on describing what types of tasks adults at each level can typically do and their ability to apply information from the task to accomplish goals they may encounter in everyday life; for example, identifying a job search result that meets certain criteria.

The PIAAC does test comprehension and proficiency for interpreting data (not just vocab, as many of the replies and reblogs first expected), and while the US is decently behind the top two countries measured this way (Japan and Finland), it's ahead of the international average for this metric.

The second source link also suggests some heavy deficits in how US education teaches children to read (and makes what seem to me compelling arguments for improvements, though I don't specialize in early childhood education and am not familiar enough to judge their relevance), but does not contextualize this or compare it to any kind of international average.

I understand the initial distress of OP's claim that Americans can't read above an elementary school level, but journalists and publications are fully aware of this, and many have guidelines and standards for writing that take that into account (e.g. in my journalism classes, I was told to aim for a 6th grade reading level or lower and shown specific guidelines for how to make information accessible and minimize jargon. The US government and CDC aim specifically for 3rd–5th grade reading levels. The NYT aims higher, and your local publications may vary, but news is meant to be accessible, so the range could be closer to a 5th—9th grade reading level on average). Therefore, it's not at all accurate that people below a 6th grade reading level only have access to TV and video.

More accurately to the PIAAC data, 18% or so might have trouble with being able to read simple articles or web pages, but once again the US meets the PIAAC international average here (23% at literacy level 1 or below).

I've done my best to review the PIAAC data, but I'm simply not finding any backing for the claim that 55% of US adults cannot read long texts at all.

This appears to be a pretty clear misinterpretation of the data.

Quick question, genuine question:

Why on earth does "more than half of US adults under 30 cannot read above an elementary school level" not strike horror into the heart of everyone who hears it?

Are the implications of it unclear????

I'm serious, people keep reacting with a sort of vague dismissal when I point this out, and I want to know why!

If adults in the US cannot read, then the only information they have access to is TV and video, the spaces with the most egregious and horrific misinformation!

If they cannot read, they cannot escape that misinformation.

This obscene lack of literacy should strike fear into every heart! US TV is notoriously horrific propaganda!

Is that???? Not??? Obvious???????

I know this sounds sarcastic, I know it does, but I'm completely serious here. I do not understand where the disconnect is.

[jamaicahomescom]

The Future of Real Estate in Jamaica: AI, Big Data, and Cybersecurity Shaping Tomorrow’s Market

[blogpopular]

Análise de Dados de Marketing: A Chave para Decisões Estratégicas Eficazes

A análise de dados de marketing se tornou uma ferramenta essencial no ambiente empresarial moderno. Com o crescimento das plataformas digitais e o aumento da concorrência em todos os setores, as empresas precisam de insights precisos para se destacar. Através da coleta, interpretação e aplicação de dados, a análise de dados de marketing permite que empresas tomem decisões estratégicas baseadas em…

[jcmarchi]

China Telecom trains AI model with 1 trillion parameters on domestic chips

New Post has been published on https://thedigitalinsider.com/china-telecom-trains-ai-model-with-1-trillion-parameters-on-domestic-chips/

China Telecom trains AI model with 1 trillion parameters on domestic chips

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China Telecom, one of the country’s state-owned telecom giants, has created two LLMs that were trained solely on domestically-produced chips.

This breakthrough represents a significant step in China’s ongoing efforts to become self-reliant in AI technology, especially in light of escalating US limitations on access to advanced semiconductors for its competitors.

According to the company’s Institute of AI, one of the models, TeleChat2-115B and another unnamed model were trained on tens of thousands of Chinese-made chips. This achievement is especially noteworthy given the tighter US export rules that have limited China’s ability to purchase high-end processors from Nvidia and other foreign companies. In a statement shared on WeChat, the AI institute claimed that this accomplishment demonstrated China’s capability to independently train LLMs and signals a new era of innovation and self-reliance in AI technology.

The scale of these models is remarkable. China Telecom stated that the unnamed LLM has one trillion parameters. In AI terminology, parameters are the variables that help the model in learning during training. The more parameters there are, the more complicated and powerful the AI becomes.

Chinese companies are striving to keep pace with global leaders in AI based outside the country. Washington’s export restrictions on Nvidia’s latest AI chips such as the A100 and H100, have compelled China to seek alternatives. As a result, Chinese companies have developed their own processors to reduce reliance on Western technologies. For instance, the TeleChat2-115B model has approximately 100 billion parameters, and therefore can perform as well as mainstream platforms.

China Telecom did not specify which company supplied the domestically-designed chips used to train its models. However, as previously discussed on these pages, Huawei’s Ascend chips play a key part in the country’s AI plans.

Huawei, which has faced US penalties in recent years, is also increasing its efforts in the artificial intelligence field. The company has recently started testing its latest AI processor, the Ascend 910C, with potential clients waiting in the domestic market. Large Chinese server companies, as well as internet giants that have previously used Nvidia chips, are apparently testing the new chip’s performance. Huawei’s Ascend processors, as one of the few viable alternatives to Nvidia hardware, are viewed as a key component of China’s strategy that will lessen its reliance on foreign technology.

In addition to Huawei, China Telecom is collaborating with other domestic chipmakers such as Cambricon, a Chinese start-up specialising in AI processors. The partnerships reflect a broader tendency in China’s tech industry to build a homegrown ecosystem of AI solutions, further shielding the country from the effects of US export controls.

By developing its own AI chips and technology, China is gradually reducing its dependence on foreign-made hardware, especially Nvidia’s highly sought-after and therefore expensive GPUs. While US sanctions make it difficult for Chinese companies to obtain the latest Nvidia hardware, a black market for foreign chips has emerged. Rather than risk operating in the grey market, many Chinese companies prefer to purchase lower-powered alternatives such as previous-gen models to maintain access to Nvidia’s official support and services.

China’s achievement reflects a broader shift in its approach to AI and semiconductor technology, emphasising self-sufficiency and resilience in an increasingly competitive global economy and in face of American protectionist trade policies.

(Photo by Mark Kuiper)

See also: Has Huawei outsmarted Apple in the AI race?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Tags: artificial intelligence, chip, huawei, llm, Nvidia