International Research Data Analysis Excellence Awards: January 2025

31 January, 2025

Big Data Analytics Market to Reach $1.1 Trillion, Globally, by 2032 at 14.5% CAGR: Allied Market Research !

NEW CASTLE, Delaware, Sept. 11, 2024 (GLOBE NEWSWIRE) -- Allied Market Research published a report, titled, "Big Data Analytics Market by Component (Solution and Services), Application (Data Discovery, Data Visualization, Advanced Analytics and Other), and End User (BFSI, Healthcare, Automotive, IT and Telecom, Retail, Energy and Utilities, Government and Others): Global Opportunity Analysis and Industry Forecast, 2024-2032". According to the report, the big data analytics market was valued at $0.3 trillion in 2023, and is estimated to reach $1.1 trillion by 2032, growing at a CAGR of 14.5% from 2024 to 2032.

Prime Determinants of Growth

Rise in need for cost effective and flexibility, community support, and innovation and rise in concern regarding security features and stability of software positively are factors expected to propel the growth of the global big data analytics market. However, lack of availability of popular mainstream software is anticipated to hamper the growth of global market.

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Report Coverage & Details:
Report Coverage Details
Forecast Period 2024–2032
Base Year 2023
Market Size in 2023 $0.3 trillion
Market Size in 2032 $1.1 trillion
CAGR 14.5%
Segments covered Component, Application, End User and Region.
Drivers Increase in demand for data-driven decision-making
Technological advancements in data processing and storage
Opportunities Expansion into new markets and industries
Restraints Data privacy and security concerns

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The service segment is expected to dominate the market during the forecast period

By component, the services segment is expected to attain the largest CAGR from 2024 to 2032 and is projected to maintain its lead position during the forecast period, as organizations increasingly seek specialized expertise and support in implementing and optimizing big data analytics solutions. Service providers offer consulting, implementation, and support services to help businesses leverage the full potential of big data analytics, which drives the segment growth in the big data analytics market.

The advance analytics segment to maintain its lead position during the forecast period

By application, the advance analytics segment is expected to attain the largest CAGR from 2024 to 2032 and is projected to maintain its lead position during the forecast period, as businesses seek to extract deeper insights and predictive capabilities from their data. Advanced analytics techniques, such as machine learning and predictive modeling, enable organizations to uncover hidden patterns and trends in data, leading to more accurate forecasting and strategic decision-making. Thereby, driving the growth of this segment in the global big data analytics market.

The retail segment to maintain its lead position during the forecast period

By end user, the retail segment is expected to attain the largest CAGR from 2024 to 2032 and is projected to maintain its lead position during the forecast period, owing to enhanced customer insights, optimized inventory management, personalized marketing strategies, and improved overall operational efficiency. Thereby, driving the growth of this segment in the global big data analytics market.

Asia-Pacific region to maintain its dominance by 2032

By region, the North America segment held the highest market share in terms of revenue in 2023, owing to the early adoption of advanced technologies, a strong presence of key market players, and a high level of awareness about the benefits of big data analytics among businesses in the region. Big data analytics has been quickly embraced by North American businesses to obtain a competitive edge, streamline decision-making procedures, and improve customer experiences—all of which are expected to fuel the market's expansion in this region. However, the Asia-Pacific segment is projected to attain the highest CAGR from 2024 to 2032, owing to rapid digital transformation and increasing investments in technology across various industries in countries like China, India, Japan, and South Korea. The growing adoption of cloud computing, IoT, and AI technologies in the Asia-Pacific region, is further expected to contribute to the growth of the market in this region.

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Leading Market Players: - SAP SE IBM Oracle MapR Technologies Inc. Google LLC Hewlett Packard Enterprise Amazon Datameer Sage Clarity Systems Kinaxis Inc. Microsoft Corporation

The report provides a detailed analysis of these key players in the big data analytics market. These players have adopted different strategies such as new product launches, collaborations, expansion, joint ventures, agreements, and others to increase their market share and maintain dominant shares in different countries. The report is valuable in highlighting business performance, operating segments, product portfolio, and strategic moves of market players to showcase the competitive scenario.

Recent Development: In July2020, Microsoft partnered with SAS and announced an extensive technology and go-to-market strategic partnership. The two companies are expected to enable customers to easily run their SAS workloads in the cloud, expanding their business solutions and unlocking critical value from their digital transformation initiatives. The companies migrate SAS’ analytical products and industry solutions onto Microsoft Azure as the preferred cloud provider for the SAS Cloud. SAS’ industry solutions and expertise are expected to bring added value to Microsoft’s customers across healthcare, financial services, and other industries. In March 2022, IBM launched new software designed to help enterprises break down data and analytics silos so they can make data-driven decisions quickly and navigate unpredictable disruptions. IBM Business Analytics Enterprise is a suite of business intelligence planning, budgeting, reporting, forecasting, and dashboard capabilities that provides users with a robust view of data sources across their entire business. A This suite also includes a new IBM Analytics Content Hub that helps streamline how users discover and access analytics and planning tools from multiple vendors in a single, personalized dashboard view.

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About Us:

Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Wilmington, Delaware. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports Insights" and "Business Intelligence Solutions." AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

We are in professional corporate relations with various companies, and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Allied Market Research CEO Pawan Kumar is instrumental in inspiring and encouraging everyone associated with the company to maintain high quality of data and help clients in every way possible to achieve success. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.

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30 January, 2025

Essential Statistical Tools for Data-Driven Research

Numerous fields rely heavily on research and data analysis. From the scientific community to business decision-makers, statistical science has long impacted people's lives in many ways. Statistical analysis, which employs technological methods to condense and depict the 'facts and figures' of diverse data, may appear to be a very complex and challenging science.This article briefly discusses research and data analysis statistical tools.

Best Statistical Tools

Statistical analysis is a crucial part of research, and statistical tools can streamline the process by helping researchers interpret the data in a simpler format. Here's the list of best statistical tools:
1. R

In data analytics, R R stands out among the top open-source statistical tools. Researchers in statistics use it. It provides high-quality toolboxes that can be used for many different things. The learning curve for the open-source programming language R is steep. R programming provides an efficient data handling and storage facility. R also has the best set available for array calculations. Thanks to its graphical tools for analysis, data visualization is another area where R shines. It is an all-inclusive high-level programming language with various functions, conditional loops, and decision expressions.
2. Python

Python is a versatile language with statistics modules. Its versatility and depth make it an excellent choice for creating analysis pipelines that combine statistics with other fields, such as text mining, physical experiment control, picture analysis, and more. NumPy and Pandas are two widely used Python libraries offering extensive statistical modeling support.
3. GraphPad Prism

With GraphPad Prism, you can do scientific charting, thorough curve fitting (nonlinear regression), and make statistics accessible to grasp and organize your data. In addition to t-tests and non-parametric comparisons, Prism also includes analysis of contingency tables, survival analysis, and one-, two-, and three-way ANOVA. Analytical decisions are laid down, free of extra statistical lingo.
4. Statistical Package for the Social Sciences (SPSS)

Among the many statistical packages used in the study of human behavior, SPSS is among the most popular. SPSS's graphical user interface (GUI) makes it easy to create descriptive statistics, parametric and non-parametric analyses, and visual representations of results. Additionally, it offers the ability to automate analysis through scripting or advanced statistical processing.
5. SAS (Statistical Analysis System)

Advanced statistical analysis can be done utilizing the graphical user interface or SAS scripts. This innovative solution is employed in healthcare, business, and human behavior research. GraphPad Prism can perform extensive analysis and create publication-quality graphs and charts, even though coding may be challenging for beginners.
6. Stata

Stata is robust statistical software for data analysis, management, and visualization. Scholars who study economics, biology, and political science primarily use it to analyze data. Its command line and graphical user interface make it easier to use.
7. Minitab

Minitab has both basic and advanced statistical features. Users can execute commands using the GUI and written instructions, making it accessible to beginners and advanced analysts. Minitab can perform measurement systems, capacity, graphical, hypothesis, regression, non-regression, and other analyses. It lets you produce the best scatterplots, box plots, dot plots, histograms, time series graphs, etc. Minitab supports one-sample Z-tests, two-sample t-tests, paired t-tests, and more.
8. Excel

Microsoft Excel has many data visualization and elementary statistics capabilities but is not a statistical analysis solution. Summary metrics, customizable images, and statistics make it a valuable tool for data beginners. Statistics are simple to master because so many people and businesses use Excel.
9. MATLAB

At its core, MATLAB is a programming language and an analytical platform. The tool allows scientists and engineers to write their code, which in turn helps them solve their research problems. It also gives researchers great flexibility to meet their specific demands.
10. JMP

Engineers and scientists depend on JMP for its robust analytic skills and ability to facilitate dynamic statistical discovery while working with data. Suppose you must understand complicated relationships, dig deeper, or find the unexpected. In that case, JMP is your data analysis tool, thanks to its linked analyses and visualizations. Gain the most out of your data in any situation with JMP. Effortlessly access data from several sources, use trustworthy data preparation tools, and conduct selected statistical analyses.
11. Tableau

When it comes to data visualization, Tableau is one of the most popular tools out there. The data visualization method is found to be extensively valuable for data analytics. Tableau makes it easy to quickly and easily generate a top-notch data representation from large datasets.

Consequently, it facilitates the data analyst's ability to make snap judgments. Excel spreadsheets, cloud databases, and massive online analytical processing cubes are just a few of its numerous features. Therefore, users must adjust the filters according to their needs and drag and drop the data set sheet into Tableau.

Conclusion

A researcher's familiarity with essential statistical tools is crucial for conducting a well-planned study that yields reliable results. Using the wrong statistical methods might result in misleading conclusions and unethical behavior. You can use statistics in research by familiarizing yourself with the research issue, utilizing your knowledge of statistics, and drawing on your personal experience in coding. Enroll in Simplilearn’s Post Graduate Program In Data Analytics to ace the top programming language and take your career to the next level.

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Website: International Research Data Analysis Excellence Awards

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29 January, 2025

NATIVE PROTEINS, FLEXIBLE FRAMEWORKS AND CYTOPLASMIC ORGANIZATION !!

Leon Wang, President of Huawei's Data Communication Product Line, shares Huawei's collaboration with world-leading carriers, leveraging its Xinghe Intelligent Network Solution.

At the Ultra-Broadband Forum (UBBF) 2024, Leon Wang, President of Huawei's Data Communication Product Line, delivered a keynote speech entitled Xinghe Intelligent Network: Accelerating New Growth for Carriers in Net5.5G Era. In his speech, Wang shared Huawei's collaboration with world-leading carriers, leveraging its Xinghe Intelligent Network Solution to drive business success for customers. He also looked ahead to the development directions of new services in intelligent computing scenarios.

All industries are rapidly moving toward the AI era, driving the evolution of data communication networks to its next generation — Net5.5G. Huawei first unveiled its interpretation of the Net5.5G target network at UBBF 2023, and later at MWC Shanghai 2024 launched the Xinghe Intelligent Network Solution tailored for the Net5.5G target network. Since then, the Xinghe Intelligent Network Solution has been put into commercial use by world-leading carriers, helping them achieve revenue growth.

Wang noted that Huawei's Net5.5G-oriented Xinghe Intelligent Network Solution boasts four capabilities:

Premium experience assurance: The solution provides precise experience assurance to meet differentiated demands of different users and accelerates experience monetisation of 2H/2C services. It also improves service experience through managed services, facilitating leapfrog growth of 2B services.

Ultra-reliable converged transport: E2E 400GE routers and Network Digital Map, which supports network configuration simulation, help build ultra-broadband and reliable transport networks. And one network for multiple services reduces the TCO, effectively addressing carriers' burgeoning annual traffic growth of over 20%.

High efficiency for intelligent computing: Elastic lossless WAN facilitates efficient transmission of computing power, helping carriers seize the opportunities brought by enterprises' access to intelligent computing centers. The solution also helps build a reliable and efficient computing foundation, enabling carriers to monetise computing services more quickly.

Ubiquitous intelligent security protection: To address network threats fueled by AI's exponential growth, this solution leverages AI-powered network security detection, rapidly and accurately pinpointing threats to safeguard service development.

In his speech, Wang discussed a variety of carriers' cutting-edge use cases. In terms of how to accelerate service monetisation through premium experience assurance, Huawei's Xinghe Intelligent Network Solution harnesses automatic optimisation capabilities of SRv6+Network Digital Map to optimise network paths and relieve traffic congestion caused by hundreds of fiber cuts every month. This shortens optimisation time from 5 days to a matter of minutes, improves service experience, and releases suppressed traffic. This also reduces the complaint rate by 80% and increases the DOU by 7%, boosting revenue by more than an estimated US$4 million per month.

In terms of how to reduce the TCO through ultra-reliable converged transport, a customer was able to slash the CAPEX of the entire network by 50% after introducing Huawei's 400GE routers. These routers, which provide the industry's highest density, enabled the customer to build a converged transport network that supports smooth evolution over the next 10 years, addressing the projected 50% CAGR in traffic growth over the next three years driven by the explosive expansion of 5G, FTTH, and video services. Wang also shared carriers' exploration and use cases in seizing new opportunities brought by AI to drive growth. These include Data Express services that enable enterprises' massive data samples to enter carriers' intelligent computing data centers, long-distance lossless transmission services that facilitate cross-DC collaborative training, intelligent computing cloud services, and the pioneering use of AI to defend against AI for reduced network security risks.

Xinghe Intelligent Network's end-to-end products have been expedited for commercial use, with 40 carriers from over 20 countries already using the Xinghe Intelligent Network to boost growth and expand their business scope. Huawei will remain at the forefront of network technology innovation, developing industry-leading products and solutions in collaboration with top-tier carriers from around the world to unlock new opportunities in the intelligent era and drive their business forward.

28 January, 2025

ConocoPhillips Takes Over Operatorship of Malaysian Oil and Gas Cluster !

ConocoPhillips Sabah Gas, a subsidiary of the global energy company ConocoPhillips, has taken over the operatorship of Kebabangan Cluster Production Sharing Contract (KBBC PSC) in Malaysia.

The transfer took effect on 21 January 2025, making ConocoPhillips Sabah Gas A the sole operator of the KBBC PSC.

Previously, the KBBC PSC was operated by Kebabangan Petroleum Operating Company (KPOC), a joint operating company comprising Petronas Carigali, Shell Energy Asia, and ConocoPhillips Sabah Gas.

With an export capacity up to 750 million standard cubic feet of gas per day (MMscfd), the continuation of the KBBC PSC until the end of 2050 and other commercial agreements have been structured to ensure future gas field developments remain economically attractive in supporting Sabah's energy security requirements.

"Sabah is rapidly emerging as a preferred destination for oil and gas investments, thanks to the strategic developments and partnerships championed by Petronas and its industry collaborators.

“These initiatives underscore the vast potential of Sabah’s resources and reinforce our commitment to driving economic growth and creating opportunities for our people. We look forward to further collaboration with PETRONAS in the best interest of Sabah,” said Datuk Seri Panglima Haji Hajiji said.

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27 January, 2025

Essential Statistical Tools for Data-Driven Research !!!!

Numerous fields rely heavily on research and data analysis. From the scientific community to business decision-makers, statistical science has long impacted people's lives in many ways. Statistical analysis, which employs technological methods to condense and depict the 'facts and figures' of diverse data, may appear to be a very complex and challenging science.This article briefly discusses research and data analysis statistical tools.
Best Statistical Tools

Statistical analysis is a crucial part of research, and statistical tools can streamline the process by helping researchers interpret the data in a simpler format. Here's the list of best statistical tools:
1. R

In data analytics, R R stands out among the top open-source statistical tools. Researchers in statistics use it. It provides high-quality toolboxes that can be used for many different things. The learning curve for the open-source programming language R is steep. R programming provides an efficient data handling and storage facility. R also has the best set available for array calculations. Thanks to its graphical tools for analysis, data visualization is another area where R shines. It is an all-inclusive high-level programming language with various functions, conditional loops, and decision expressions.
2. Python

Python is a versatile language with statistics modules. Its versatility and depth make it an excellent choice for creating analysis pipelines that combine statistics with other fields, such as text mining, physical experiment control, picture analysis, and more. NumPy and Pandas are two widely used Python libraries offering extensive statistical modeling support.

3. GraphPad Prism

With GraphPad Prism, you can do scientific charting, thorough curve fitting (nonlinear regression), and make statistics accessible to grasp and organize your data. In addition to t-tests and non-parametric comparisons, Prism also includes analysis of contingency tables, survival analysis, and one-, two-, and three-way ANOVA. Analytical decisions are laid down, free of extra statistical lingo.
4. Statistical Package for the Social Sciences (SPSS)

Among the many statistical packages used in the study of human behavior, SPSS is among the most popular. SPSS's graphical user interface (GUI) makes it easy to create descriptive statistics, parametric and non-parametric analyses, and visual representations of results. Additionally, it offers the ability to automate analysis through scripting or advanced statistical processing.
5. SAS (Statistical Analysis System)

Advanced statistical analysis can be done utilizing the graphical user interface or SAS scripts. This innovative solution is employed in healthcare, business, and human behavior research. GraphPad Prism can perform extensive analysis and create publication-quality graphs and charts, even though coding may be challenging for beginners.
6. Stata

Stata is robust statistical software for data analysis, management, and visualization. Scholars who study economics, biology, and political science primarily use it to analyze data. Its command line and graphical user interface make it easier to use.
7. Minitab

Minitab has both basic and advanced statistical features. Users can execute commands using the GUI and written instructions, making it accessible to beginners and advanced analysts. Minitab can perform measurement systems, capacity, graphical, hypothesis, regression, non-regression, and other analyses. It lets you produce the best scatterplots, box plots, dot plots, histograms, time series graphs, etc. Minitab supports one-sample Z-tests, two-sample t-tests, paired t-tests, and more.
8. Excel

Microsoft Excel has many data visualization and elementary statistics capabilities but is not a statistical analysis solution. Summary metrics, customizable images, and statistics make it a valuable tool for data beginners. Statistics are simple to master because so many people and businesses use Excel.
9. MATLAB

At its core, MATLAB is a programming language and an analytical platform. The tool allows scientists and engineers to write their code, which in turn helps them solve their research problems. It also gives researchers great flexibility to meet their specific demands.
10. JMP

Engineers and scientists depend on JMP for its robust analytic skills and ability to facilitate dynamic statistical discovery while working with data. Suppose you must understand complicated relationships, dig deeper, or find the unexpected. In that case, JMP is your data analysis tool, thanks to its linked analyses and visualizations. Gain the most out of your data in any situation with JMP. Effortlessly access data from several sources, use trustworthy data preparation tools, and conduct selected statistical analyses.
11. Tableau

When it comes to data visualization, Tableau is one of the most popular tools out there. The data visualization method is found to be extensively valuable for data analytics. Tableau makes it easy to quickly and easily generate a top-notch data representation from large datasets.

Consequently, it facilitates the data analyst's ability to make snap judgments. Excel spreadsheets, cloud databases, and massive online analytical processing cubes are just a few of its numerous features. Therefore, users must adjust the filters according to their needs and drag and drop the data set sheet into Tableau.

Conclusion

A researcher's familiarity with essential statistical tools is crucial for conducting a well-planned study that yields reliable results. Using the wrong statistical methods might result in misleading conclusions and unethical behavior. You can use statistics in research by familiarizing yourself with the research issue, utilizing your knowledge of statistics, and drawing on your personal experience in coding. Enroll in Simplilearn’s Post Graduate Program In Data Analytics to ace the top programming language and take your career to the next level.

25 January, 2025

How LLMs can improve data analysis !!

Large language models can augment data analysis, crunching more information and identifying deeper insights than data professionals can on their own. But LLMs are unreliable tools for non-analysts, and even trained analysts should use them cautiously.

Off-the-shelf LLMs are not ready to perform the role of a data analytics tool. They can't accurately or consistently answer detailed questions about the meanings of data sets. Automated LLM functions require training on the correct data sets to generate the most accurate results; it's up to analysts to ensure that outputs are secure, accurate and ethically sound.
How LLMs improve data analysis

LLMs can analyze structured numerical data. They can calculate statistics, look for trends and identify anomalies. But data analysts should only use LLMs for this type of data if they limit the tool to look only at data in specific files and confine answers to material within those files.
Text analysis

An even better use of LLMs is to take advantage of their facility with language. Data analysts can use an LLM to accelerate text analysis and -- if it is multimodal and can interpret spoken language -- oral inputs. LLMs can transcribe spoken word inputs, translate languages and analyze the results by doing the following:Highlighting categories of words.
Looking for commonalities among comments such as similar language, or references to the same people or things.
Providing semantic scoring of inputs based on defined categories of words. For example, being angry, despairing, engaged or credulous.
Pointing out contextual information associated with use of specific words or images.
Visual media analysis

If an LLM is trained with the ability to parse visual media, it can analyze the content of pictures, charts and videos. LLMs can follow straightforward prompts, such as looking for a specific kind of object -- say, how many hats are in a picture. They can also identify subtle elements, such as the prevalence of different color palettes across TikTok videos with a given hashtag.

LLMs can help analysts combine inherently unstructured data with structured data sets by converting free text, audio or video media into specific numerical data. Trained multimedia LLMs can also generate visualizations of data sets, ranging from traditional line or bar charts to word clouds and heat maps.

Predictive analytics. LLMs can allow analysts to analyze non-textual data, and, critically, to integrate those results with the analysis of standard numerical data. The combination broadens the reach of predictive analytics by enabling it to spot more trends. For example, an LLM would be able to identify patterns across media platforms more easily than human analysts could.

LLMs can tease out words in the data, understand the context of words and assign sets of words to themes. Using that information, analysts can then adjust the LLM model training for subsequent predictive analytics operations.
Examples of data analysis with LLMs

Analysts can use LLMs to provide insights and improve business operations in several ways.
Identify actions based on customer feedback

LLMs can help analysts understand patterns and trends in customer sentiment about specific products, services, store locations or staff. Analysts can use LLMs to analyze data from fixed-choice surveys, emails to sales teams and support desks, support and sales chat logs, social media postings and content from podcasts or product review videos. Companies can use the results to improve their operations and address shortcomings in their service or product portfolios.

Identify business development opportunities

Analysts using LLMs can harvest data from sources including competitor websites, relevant social media channels and their own support channels. The information can help identify new product or service opportunities, open new stores or websites, move into new locations or form new partnerships with other companies.
Identify potential threats

Governments and corporations can analyze the content of material posted in public. The information can help augment data from other sources to spot potential security threats.
Will LLMs replace data analysts?

Data analysts will not see LLMs replace their jobs anytime soon.

Data professionals must learn more about what LLMs can do and how to keep the models honest. In the meantime, LLMs will be able to assist analysts, but not replace them. Organizations need analysts to craft prompts carefully and verify the accuracy of all outputs -- that is, until developers can consistently produce LLMs that don't make up data, present made-up data as real or make up conclusions not driven by the data.

John Burke is CTO and principal research analyst with Nemertes Research. With nearly two decades of technology experience, he has worked at all levels of IT, including end-user support specialist, programmer, system administrator, database specialist, network administrator, network architect and systems architect. His focus areas include AI, cloud, networking, infrastructure, automation and cybersecurity.

24 January, 2025

OpenAI unveils tool to automate web tasks as AI agents take center-stage !

OpenAI has recently introduced "Operator," an AI agent designed to automate web tasks by interacting with on-screen elements such as buttons, menus, and text fields. Announced on January 23, 2025, Operator represents a significant advancement in AI development, enabling models to perform tasks typically handled by humans and expanding the potential for various new applications. It can execute functions like creating to-do lists and assisting with vacation planning, requiring user input for certain actions, such as logging into websites. Initially available to Pro users in the U.S. as a research preview, this tool exemplifies the growing focus on AI agents that execute actions autonomously.

On the same day, OpenAI's rival Perplexity launched its own agent-based assistant for Android devices, capable of functions such as booking reservations and setting reminders. This announcement follows the trend of integrating advanced AI in consumer technology, as seen with Apple's incorporation of Apple Intelligence and ChatGPT into Siri. The development signals a shift towards practical, autonomous AI applications driven by improved reasoning approaches.

23 January, 2025

Jio Adds 12.1 Lakh Users in November: TRAI Data

New Delhi, Jan 22 (PTI) Reliance Jio added 12.1 lakh wireless subscribers in November, pushing its mobile user tally to 46.12 crore, while rivals Bharti Airtel, Vodafone Idea and state-owned BSNL suffered subscriber losses, according to TRAI's latest data.

Jio snapped decline of past four months, and spruced up its mobile user tally to 46.12 crore in November.

Bharti Airtel lost 11.4 lakh wireless subscribers in November as its mobile user base slid to 38.4 crore.

Vodafone Idea too suffered subscriber losses, with its mobile user count shrinking by 15 lakh users to 20.8 crore customers.

Bharat Sanchar Nigam Ltd which had enjoyed subscriber gains over past few months, benefitting from tariff hikes effected by private telcos and SIM consolidation, however, logged subscriber losses this time around.

BSNL's wireless subscriber numbers fell by 3.4 lakh during the month to end at 9.20 crore users, data showed.

#ResearchDataExcellence #DataAnalysisAwards #InternationalDataAwards #ResearchDataAwards #DataExcellence #ResearchData #DataAnalysis #DataAwards #GlobalDataExcellence #DataInnovationAwards #DataResearch #ExcellenceInData #DataAwardWinners#DataAnalysisExcellence #ResearchDataInsights #GlobalResearchAwards #DataExcellenceAwards #ExcellenceInResearchData #ResearchDataLeadership #DataResearchExcellence #AwardWinningData #InternationalResearchAwards #DataAnalysisInnovation #ResearchDataAchievement #ExcellenceInDataAnalysis #GlobalDataInsights #ResearchDataSuccess #DataAwards2024

Website: International Research Data Analysis Excellence Awards

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22 January, 2025

A beginner’s guide to learn Python programming: What you need to know !

Python, a versatile and widely-used programming language, is emerging as one of the most sought-after skills according to the technological industrial landscape in 2025. With its emphasis on simplicity, readability, and adaptability, Python has become an essential tool for tasks ranging from web development to artificial intelligence (AI).

What is Python?

Python is a high-level, interpreted programming language created in 1991 by a Dutch programmer, Guido van Rossum. Its syntax, similar to English, allows programmers to construct clear, shorter codes. Python supports various paradigms, including procedural, object-oriented, and functional, making it adaptable to a wide range of programming demands.

The language is named after the British comedy troupe Monty Python.

Why is Python so popular?

Python’s popularity stems from its usability and versatility. According to the TIOBE Index, which is a monthly ranking of the popularity of programming languages, Python is still the most prevalent programming language as of November 2025.

Key elements driving Python's widespread adoption

Python's readability and its simple syntax are suitable for both novices and professionals. Python is widely utilised in various fields, including banking, healthcare, and education, for web development and artificial intelligence.

Extensive built-in libraries and third-party packages facilitate data analysis and visualisation.

Python code is platform-independent, allowing it to execute across multiple operating systems.

Dynamic Typing in Python allows for speedier development by eliminating the need for explicit type definitions for variables.

Python in action: Applications across industries

Python is used extensively in:Data Science: Pandas, NumPy, and Matplotlib are essential libraries for manipulating and visualising data.
AI and Machine Learning: TensorFlow and PyTorch are two frameworks that enable developers to create complex models.
Web development: Flask and Django make backend development easier.
Game Development: The creation of games is made possible by Python modules like PyGame.
Automation: Python scripts increase productivity by automating tedious processes.

Learning Python: A beginner’s pathway

For aspiring programmers, learning Python can be both manageable and rewarding. While beginners can grasp the basics within weeks, proficiency requires consistent practice and application.

Top resources to get started:

1. Google's Python Class: A free introductory course that includes readings and activities and covers strings, lists, and regular expressions.
2. Microsoft's Introduction to Python is a brief 16-minute lesson that covers variables, fundamental data types, and console operations.
3. Learn Python 2 by Codecademy: This 17-hour course covers everything from game development to DNA analysis and includes nine projects and tests.
4. Programming for Everyone on Coursera: This 18-hour course from the University of Michigan, which is offered in 27 languages, covers fundamental concepts including loops and computations.

Should you get a certification?

There is no clear-cut generic answer to this question, as their necessity depends on individual goals. However, given its dominance in the tech landscape presently, a Python certification will bolster one's resume in today's world.

Mastering Python for beginners

1. Focus on Fundamentals: Establish a strong foundation in syntax and basic concepts.
2. Consistent practice helps reinforce learning.
3. Engage in projects, and apply information to real-world issues to improve understanding.
4. Join a Community and Work with peers to exchange insights and solve problems.
5. Revisit previous projects to monitor progress.

Python’s future: Why learn it now?

Python skills are becoming increasingly valuable as the demand for AI, machine learning, and data-driven solutions grows. The growing necessity of implementing machine learning, automation and AI-powered solutions, in key industries makes Python a must-have talent for professionals seeking to prosper in a tech-driven environment, also preparing students for future technological breakthroughs.

21 January, 2025

Confidence Intervals !!

What Is a Confidence Interval?

A confidence interval (CI) is a range of values that estimates a parameter, like a population mean or proportion, with a specified level of confidence. For example, a 95% confidence interval suggests that if we repeated the same study 100 times, about 95 of those intervals would contain the true population parameter.

Key Terms:

Point Estimate: The single best guess for a parameter (e.g., a sample mean).

Margin of Error: The range above and below the estimate where the true value is likely to fall.

Why Are Confidence Intervals Important?

Uncertainty Management: They provide a clearer picture of uncertainty than single point estimates.

Data Interpretation: Help avoid overconfidence in results.

Decision-Making: Used in medicine, business, and policymaking to assess risks and outcomes.

For instance, instead of saying "The average test score is 75," you might say, "The average test score is between 72 and 78 with 95% confidence." The latter acknowledges variability.

20 January, 2025

Top 7 Challenges PI-NETWORK Faces in Its Journey to Blockchain Success !!

I-NETWORK, a blockchain-based decentralized platform that aims to democratize cryptocurrency mining, has been growing rapidly since its inception.

With millions of users worldwide, the network has garnered significant attention. However, like any emerging technology, PI-NETWORK faces its share of challenges that could hinder its long-term success.

Below are the top seven challenges PI-NETWORK must navigate to realize its vision.
1. Scalability Issues

One of the biggest challenges for any blockchain network is scalability, and PI-NETWORK is no exception. As the user base grows, the platform needs to handle a massive volume of transactions efficiently.

Blockchain networks often struggle with scalability, especially when they need to process transactions quickly while maintaining decentralization.

In PI-NETWORK’s case, the system has been designed to function on mobile devices, making it easier for users to mine tokens without heavy computational power.

However, this mobile-centric approach may present limitations when scaling the network to millions of active users.

If the network can’t scale effectively, it could result in slower transaction speeds, higher fees, and user dissatisfaction.
2. Adoption and User Growth

While PI-NETWORK has attracted millions of users by offering free mining on mobile phones, the challenge lies in sustaining this growth and fostering real utility for its tokens.

It is essential for PI-NETWORK to convert its large user base into active participants who use the platform beyond simply earning tokens.

To achieve this, the network needs to introduce use cases for its native token, the PI, in real-world applications. Without clear incentives or functionalities, users may lose interest over time, and the platform’s growth could plateau.
3. Regulatory Uncertainty

Cryptocurrency projects often face significant regulatory challenges, and PI-NETWORK is no different. As cryptocurrency regulations are constantly evolving across the globe, PI-NETWORK must navigate complex legal environments.

Governments may impose strict regulations regarding the issuance, distribution, and trading of cryptocurrencies, which could limit the growth of PI-NETWORK.

PI-NETWORK must ensure it complies with local regulations to avoid any legal issues that could affect its users or its ability to operate in certain markets. Additionally, the potential for a regulatory crackdown could impact the platform’s credibility and the value of its tokens.
4. Security Concerns

Blockchain technology is renowned for its security, but no system is immune to attacks. As a decentralized platform, PI-NETWORK faces the constant threat of hacking and cyberattacks.

Ensuring that user data remains secure, and that tokens are not stolen, is essential to the platform’s reputation and trustworthiness.

PI-NETWORK’s mobile mining feature has been praised for making cryptocurrency mining more accessible, but it also introduces potential vulnerabilities.

If hackers were to exploit the mobile app or network, it could lead to significant losses for users, damage the network’s integrity, and erode public confidence.
5. Centralization Risks

PI-NETWORK markets itself as a decentralized platform, but there are concerns regarding the level of centralization in its governance model.

Currently, a small group of validators and trusted nodes have significant control over the network’s operations. This could result in centralized decision-making, which goes against the very ethos of decentralization.

As the network grows, it is crucial that PI-NETWORK adopts a transparent and decentralized governance structure. Otherwise, it risks alienating its user base and losing its position as a truly decentralized platform.
6. Market Volatility

Cryptocurrencies are notoriously volatile, and PI-NETWORK’s token, PI, is no exception. As with other cryptocurrencies, its value is susceptible to market fluctuations, making it difficult for users to predict its worth in the future.

If the value of the PI token does not stabilize or increase, users may become disillusioned with the platform.

Additionally, if the token fails to achieve tangible value, it may lose its appeal as a method of payment or investment. Maintaining a stable value for the token and creating real-world use cases is crucial for PI-NETWORK’s continued growth.
7. Lack of Transparency

Transparency is a fundamental principle for any decentralized network, yet PI-NETWORK has been criticized for its lack of openness regarding its development, decision-making processes, and governance structure.

Many users have expressed concerns over the platform’s centralized leadership and the absence of clear information on how the network is evolving.

For PI-NETWORK to gain long-term trust from its users and the wider blockchain community, it must become more transparent in its operations.

Open communication about key decisions, development milestones, and updates is essential for fostering trust and ensuring a decentralized, community-driven platform.
Conclusion

PI-NETWORK has made impressive strides in making cryptocurrency mining accessible to the masses.

However, the platform must overcome several significant challenges to ensure its continued growth and adoption.

From scalability issues and regulatory concerns to security risks and the need for decentralization, PI-NETWORK must address these hurdles to fulfill its vision of a global decentralized network.

The future success of PI-NETWORK will depend on how effectively it navigates these challenges while providing real utility for its users.

18 January, 2025

Otelier data breach exposes info, hotel reservations of millions !

Hotel management platform Otelier suffered a data breach after threat actors breached its Amazon S3 cloud storage to steal millions of guests' personal information and reservations for well-known hotel brands like Marriott, Hilton, and Hyatt.

The breach first allegedly occurred in July 2024, with continued access through October, with the threat actors claiming to have stolen amost eight terabytes of data from Otelier's Amazon AWS S3 buckets.

In a statement to BleepingComputer, Otelier confirmed the compromise and said it is communicating with impacted customers.

"Our top priority is to safeguard our customers while enhancing the security of our systems to prevent future issues," Otelier told BleepingComputer.

"Otelier has been in communications with its customers whose information was potentially involved. In response to this incident, we hired a team of leading cybersecurity experts to perform a comprehensive forensic analysis and validate our systems."

"The investigation determined that the unauthorized access was terminated. In order to help prevent a similar incident from occurring in the future, Otelier disabled the involved accounts and continues to work to enhance its cybersecurity protocols."

Otelier, previously known as MyDigitalOffice, is a cloud-based hotel management solution used by over 10,000 hotels worldwide to manage reservations, transactions, nightly reports, and invoicing.

17 January, 2025

How Statistics Are Used in Supply Chain Management !

In 2021, the waves of the pandemic started to quickly unravel supply chains across the world. Manufacturing plants slowed or even closed, ports experienced unprecedented back-ups, and transportation costs and inflation raised prices dramatically. This situation was exacerbated by prevailing manufacturing practices. That is, before the pandemic, many large organizations were using “lean manufacturing," which means they had just enough staff, materials, and vehicles to fulfill average orders.

And at the same time that people became isolated in their homes, orders went up, but the ability to adequately fill those orders went down. And supply chain managers were tasked to solve these problems.

Supply chain managers are crucial to improving the efficiency and speed of supply-chain processes. Equipping them with new skills in data, analytics, and statistics can support digital acceleration efforts that may help mend the broken supply chain.

Professionals with a Master’s in Applied Statistics are able to leverage supply chain management statistics to automate outdated processes, track the supply chain, and provide data-driven business insights that improve operational efficiencies. Learn more about how statistics are used to optimize supply chain management and discover if a supply chain career could be right for you.
What is the Current State of Supply Chain Management?

In response to the pressures of the pandemic, burnout, and the collective “Great Resignation” sweeping across the United States, supply chain managers began leaving their jobs. As a result, the number of job openings for supply chain managers more than doubled between January 2020 and March 2022. Supply chain managers directly attributed their burn-out and stress to the use of outdated systems and processes in the supply chain.

Although data is essential to streamline processes, sources say that 60 percent to 70 percent of an analytics employee’s time is spent gathering data whereas only 30 percent to 40 percent is dedicated to analyzing the figures and providing insights.

In other words, the current state of supply chain management relies too heavily on manual, labor-intensive operations while lacking the data, talent, and skills needed to modernize platforms and processes. Therefore, the global supply chain still needs experts in supply chain statistics who can lead in data-driven decision-making.
Supply Chain Statistics and Trends

The following supply chain statistics illustrate the recent state of data and technology used in the supply chain and how supply chain managers must reconfigure priorities to meet evolving customer expectations in the coming years.63% of companies do not use any technology to monitor their supply chain performance.
81% of supply chain professionals say analytics will be important in reducing costs.
79% of companies with high-performing supply chains achieve greater revenue growth than the average for their industry.
73% of supply chains experienced pressure to improve and expand their delivery capabilities.
Businesses with optimal supply chains have 15% lower supply chain costs.
How Can Supply Chain Management Statistics Help Resolve Problems?

Statistics is a field of applied mathematics that involves collecting, describing, analyzing, and applying insights from quantitative data. Statistical analysts leverage statistics from smaller sample sizes to draw accurate conclusions about large groups, trends, business processes, and more.

For example, supply chain statisticians may study data related to one chain of manufacturing, distribution, and transportation to draw insights into other supply chains using the same third-party partners. They use the two major branches of statistical analysis:Descriptive statistics: Explains the properties of sample and population data
Inferential statistics: Uses properties drawn from descriptive statistics to test hypotheses and make conclusions

Supply chain statistics can be used to improve technologies, operational processes, and business outcomes for organizations. As a result, organizations and third-party partners can expect better outcomes, including reduced costs, faster delivery, higher employee satisfaction, enhanced customer experiences, and more. Supply chain statistics and supply chain analytics allow supply chain managers to accomplish the following outcomes.
Reduce Costs and Improve Margins

Supply chain statistics allow analysts to access vast data sets and create proactive strategies using real-time insights. This data can then be used to enhance operational efficiency and therefore, reduce overall costs.
Improve Risk Management and Compliance

Leaders can use supply chain analytics to mitigate risks, detect threats, enhance cybersecurity, and prepare for future risks before they disrupt the supply chain. Data can also be used to monitor third parties across the supply chain to ensure they are complying with regulations.
Enable a Data-Driven Strategy

Supply chain analytics can use customer data, including online behavior, purchases, and preferences, to help businesses better predict future demand for their products and services. As a result, supply chain managers with analytical skills can help build strategies to optimize profitability by discontinuing unpopular products and increasing the supply and distribution of others.
Create a Leaner Supply Chain

Before the pandemic, many companies were already moving towards creating leaner supply chains, which reduce waste and cuts costs. However, businesses need data to accurately monitor warehouse and customer demands, so that they make better-informed decisions. Supply chain statistics allow managers to identify what is necessary and what products or processes can be cut from the supply chain.
Prepare for the Future

The future of supply chain management will rely on improvements in data and technology. Analysts are well-positioned to occupy leadership roles in supply chain management because they can use advanced analytics to minimize risks, reduce costs, lower environmental impact, improve employee satisfaction, and more. With supply chain statistics, the future of the supply chain will be faster, more efficient, and more environmentally conscious.
Why Should I Pursue a Supply Chain Career?

The demand for supply chain managers and procurement specialists to remedy existing challenges has given professionals with supply chain skills the upper hand in the job market. Supply chain professionals want to work for data-driven organizations with modern systems, earn high salaries that match their output, and ensure job security and stability as the workforce continues to evolve.

Here are the typical roles, salary, and job outlook for supply chain managers in the United States.
Supply Chain Manager Roles and Responsibilities

Supply chain managers, also known as logisticians, are responsible for coordinating an organization’s supply chain and efficiently moving products from the supplier to consumer. They manage the entire life cycle of a product, which includes how a product is received, distributed, and delivered. The typical roles and responsibilities for supply chain managers or logisticians include the following:Overseeing a product’s life cycle from initial design to removal
Leading the distribution of materials, supplies, and final products
Communicating with suppliers and clients regularly
Understanding their client’s needs and present business plans
Reviewing logistical functions and identifying areas that can be made more efficient
Applying supply chain statistics to create data-driven business strategies
Supply Chain Manager Salary

According to the U.S. Bureau of Labor Statistics (BLS), the 2023 median annual salary of supply chain managers is $79,400. The highest 10 percent, though, earned more than $128,550 and the highest-paid jobs include roles with the federal government and managers of large enterprises.
Supply Chain Manager Job Outlook

In addition, the BLS, which validates positive job outlook for supply chain managers, forecasts a 18% growth between 2022 through 2032 (much faster than the average for all occupations). With increasing rates of globalization, advancing transportation technology, and people’s reliance on direct-to-consumer shipments, the fast-paced growth in this industry will likely continue.
Start Your Future in Supply Chain Management at Michigan Tech!

Are you an analytical and mathematically inclined professional with a passion for leading people and improving business processes? Starting a career in supply chain management is an opportunity to harness the power of data and technology to streamline the world around you. In addition, you can gain access to job opportunities across all sectors, including automotive, healthcare, retail, Big Tech, and more.

The Online Master of Science in Applied Statistics at Michigan Technological University can equip you with the skills for a career path in supply chain management.

In addition, Michigan Tech prepares you to use data and statistics in real-world settings by teaching you how to do the following:Develop specialized quantitative skills to meet the rising demand for data experts
Explore the application of advanced statistical methods like predictive modeling, statistical data mining, model diagnostics, and forecasting
Gain confidence and familiarity with industry-standard software including R, SAS, S-Plus, and Python Enter leadership roles with business and communication skills

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