Data is the backbone of most businesses, as they need it to analyze competitors, monitor prices, and aggregate prices from different sources. However, most business owners view web scraping as a hard nut to crack, especially if were talking about collecting data from social media platforms. Luckily, the solution lies with probably the most revered network: Telegram.

When it comes to scraping data from social media, Telegram is not held in the same regard as other platforms. This is because many business owners think scraping chats and group information from Telegram is hard, but the truth is far from that. In fact, it is easier with Telegram because it supports automation.

In this article, we are going to provide you with a gentle guide on how to get the most out of Telegram for the benefit of your business. But first, let us look at why you need telegram automation.

Telegram is one of the most popular messaging platforms. It is also secure due to encryption, which makes it ideal for chatting, sending photos and videos, and sharing files in almost all formats you can think of. Moreover, it supports mega groups of up to 200,000 people and themed channels, which makes it a holy grail for business processes such as marketing and industry data collection and analysis.

Telegram bots are configured to send automated messages and support automated video downloads, file conversions, and reminders. Automation is also ideal for data collection, and this is where datacenter proxies come in. Using proxies for scraping enables you to automatically generate, filter, and collect the data that you need.

Datacenter proxies smooth the process of data collection from Telegram. Even scraping large amounts of data becomes easier as the platform supports various proxies for automation.

Telegram offers two platforms, that is, groups and channels, for users to interact and generate or share data. The best place to start would be to differentiate the two, as the data generated from each is different. Groups are open platforms that are meant to be like chats where every member can share their views and opinions, while channels are like broadcasts, where only admins can send messages and other members can only view. Now lets see how we can extract different types of data from these platforms.

For a business to thrive, they need to identify their audience, what the audience needs, and how to bring them close. Telegram channels are among the best places on the internet to get this data, especially due to their large number of members. They can also be a good place to source the contact information of prospective audiences for the purpose of reaching out to them. Unfortunately, the option of scraping this data is not available, as only administrators have access to contact information.

Extracting group members on Telegram is more than possible, as opposed to scraping channel subscribers. This is because Telegram does not have many restrictions on scraping its content. As a business owner, you may need group members information to get attention from the groups, add them to your group, or engage them without spamming. Here is a short tutorial to get you going.

Before scraping telegram group members, you need to have your credentials. To do this,

Scraping data on Telegram is easier, and datacenter proxies alone are enough to accomplish the task. Get your proxy, authenticate it, and change the address, port, username, and password.

Telethon is an MTProto API Telegram client library. You can install it using Pip as follows:

python pip install telethon

However, if you are using Linux or Mac, you may need to use sudo before pip to avoid permissions issues.

The latest version of Telethon has two sync and async models. Here, we will focus on the sync module. Import it from your preferred library, then change the api_id, api_hash, and phone to insatiate your client object.

If you are good to go, a session file that makes your session persistent will be created.

Create an empty list of chats that you would like to scrape from and populate it with the results you get from GetDialogsRequest. You also need to add the InputPeerEmpty to have your code look as follows;

Here, we are sending empty values to the parameters offset_date and offset_peer so that the API can return all chats. We also assume that we are only interested in mega groups, so we have to check if the mega group attribute of the chat is True and add it to your list.

After listing the groups, its time to select the group that you would like to scrape members from. When the code is executed, it loops through the groups that you stored in the previous step, printing every groups name starting with a number, which is the index of the group list. Enter the number associated with your target group.

After identifying the group you need data from, the last step is to export its participants. Telethon makes this easy with a function that lets us create an empty list of users, get members using the get_participants function, and populate the list.

Open a CSV file in the write mode with UTF-8 encoding. This is crucial, as it is common for Telegram group members to have non-ASCII names. Create a CSV writer object and write the first row in the CSV file, then loop through every item in the all_participants list and write them to the CSV file.

Datacenter proxies are ideal for scraping Telegram for various reasons, including providing an extra layer of security between your computer and the internet. It also protects your privacy as you collect data for your business needs.

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Scraping Telegram with Datacenter Proxies: An In-Depth Guide for ... - TechiExpert.com

Andrs Izquierdo

On April 28th, 2023, the Committee on Development and Intellectual Property (CDIP 30th Session) at the World Intellectual Property Organization (WIPO), approved a pilot project CDIP/30/9/REV on Text and Data Mining (TDM) to support research and innovation in universities and other research-oriented institutions in Africa. PIJIP has been participating as an observer on this Committee since 2022.

The pilot project aims to increase awareness and enhance the capacity of universities and other research-oriented institutions in Africa to use TDM, enabling the utilization of AI tools, generating and sharing knowledge on TDM by documenting best practices of universities and research institutions in the region, and strengthening the skills of staff from African universities and research institutions.

The main stakeholder groups identified as relevant to this project are public and private research institutions; universities; text and data mining researchers; communities of creators and innovators; and publishers.

The pilot project will begin by mapping the current treaty implementation, legal framework, and licensing schemes, as well as existing materials such as studies and toolkits in the region, to assess the use of TDM in research, particularly by universities and research-oriented institutions. In the second step, the project will collaborate with three pilot universities in Africa, along with input from other regional stakeholders, to develop case studies on the application of TDM in research, using the information and experiences gathered during the mapping process.

As a last step, the project will use the insights gained from the case studies to develop training materials on TDMs effective use by universities and research-oriented institutions in Africa, which will undergo peer review by experts in the field, followed by two regional training seminars for a wider range of stakeholders as the final step.

The project addresses Development Agenda (DA) Recommendations 4, 10, 16, 25, and 27. Moreover, this proposal is in line with WIPOs Medium-Term Strategic Plan (MTSP) for 2022-2026, including its vision, which is to help create a world where innovation and creativity from anywhere is supported by intellectual property (IP) for the good of everyone, and its mission to lead the development of a balanced and effective global intellectual property ecosystem to promote innovation and creativity for a better and more sustainable future.

The pilot project is also in line with WIPOs proposed future direction to achieve the Program of Work and Budget for 2022/23 Expected Result 2.2: Bring the international community together to proactively address, at the global level, emerging issues and policy challenges relating to IP, innovation and creativity, and in particular, to create spaces for information sharing, the exchange of best practices, and other important reflections, to enrich understanding of emerging IP issues among stakeholders and the broader user community.

The proposal put forth by the African Group for a pilot project on Text and Data Mining CDIP/30/9 REV presents a promising chance to survey the legal terrain and implement TDM initiatives across three diverse countries, with the aim of enhancing research and innovation in African universities and research oriented institutions. The project also seeks to produce educational resources for TDM use in scientific research that can be replicated in other developing countries and regions in the world.

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The First WIPO Project on Text and Data Mining | infojustice - International IP and the Public Interest

This year could arguably be dubbed "the year of artificial intelligence (AI)." Since entering the spotlight in early 2023, the public and investors alike have become enamored with generative AI. This recent technological breakthrough in AI was years in the making and is the foundation of ChatGPT and other high-profile chatbots.

Yet, this is just the tip of the iceberg of what AI can accomplish. The process that enabled these advancements has been on the drawing board for years and one of the pioneers behind this technology is data mining specialist Palantir Technologies (PLTR 1.05%). The company has been developing AI tools for the U.S. government and its allies for more than two decades and its enterprise business is finally beginning to gathering steam.

Image source: Getty Images.

For the first quarter, Palantir reported results that caught investors off guard and they were pleasantly surprised. Revenue grew 18% year over year to $525 million, fueled by government revenue that grew 20%, while commercial revenue climbed 15%. The overall results far exceeded analysts' consensus estimates of $506 million.

Perhaps most surprising, however, was the rapid improvement on the bottom line. Palantir generated GAAP profits for the second consecutive quarter, and GAAP operating profitability for the first time in the company's history. The company delivered income from operations of $4 million, up 1,000 basis point year over year. The resulted in earnings per share (EPS) of $0.01. On an adjusted basis, EPS of $0.05 sailed past exceeded expectations of $0.04. The company also generated operating cash flow and free cash flow of $187 million and $189 million, respectively.

Perhaps most importantly for investors, CEO Alex Karp said, "We now anticipate that we will remain profitable each quarter through the end of the year."

In the company's letter to shareholders, Karp was even more bullish, particularly related to the accelerating adoption and strong demand for AI. "The arrival of the latest large language models (LLM), which have provided the world with the first real hints of more generalizable forms of artificial intelligence, will transform enterprise software," Karp posited.

He went on note that the technology that Palantir has been building for years provides the foundation to leverage the capability of these new AI models.

Palantir also introduced its Artificial Intelligence Platform, a set of proprietary tools for unleashing the power of the LLMs that underpin generative AI.

The customers' first-party data, combined with Palantir's AI and data analysis ecosystem and these foundational LLM's, will help businesses leverage AI while maintaining full control over their data.

While it's still too early to gauge the future success of these more recent efforts, the initial signs are encouraging. "The depth of engagement with and demand for our new Artificial Intelligence Platform is without precedent," he wrote.

For the upcoming second quarter, Palantir is guiding for revenue of roughly $530 million, up 12% year over year at the midpoint of its guidance. Furthermore, as a result of the current AI goldrush, Palantir has boosted its full-year outlook. Management is now expecting revenue of $2.21 billion, an increase of about 16% at the midpoint of its guidance, up from its previous forecast of 15% growth.

Palantir's guidance could well end up being conservative. During the quarter, the company's total contract value surged 60% year over year to $397 million while billings of $614 million climbed 25%. Since these metrics are both forward-looking indicators, the robust increases suggest Palantir's growth spurt could just be getting started.

While most experts agree that the market opportunity for AI will be vast, estimates vary wildly regarding the potential size of the market opportunity.

One of the more optimistic takes comes courtesy of Cathie Wood's Ark Investment Management and its Big Ideas 2023 report. According to the firm's calculations, AI could "increase the productivity of knowledge workers more than fourfold by 2030 ... if vendors were to capture just 10% of [the] value created by their products, AI software could generate $14 trillion in revenue and $90 trillion in enterprise value in 2030."

While no one can know for sure what the future will bring, the current secular tailwinds resulting from the boom in AI bodes well for Palantir. The stock remains a buy.

Danny Vena has positions in Palantir Technologies. The Motley Fool has positions in and recommends Palantir Technologies. The Motley Fool has a disclosure policy.

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Palantir's AI Expertise Is Driving Unprecedented Demand. That ... - The Motley Fool

By Ravikaran Peesapati, Executive Director Employability Business Imarticus LearningThe economy today is primarily supported by data-driven insights to make major decisions. It would hardly be an exaggeration to say that data is the basis for a sustainable and profitable business ethos. Unsurprisingly, data scientists and analysts are in demand, and companies are willing to pay substantial amounts to onboard skilled professionals in this domain.

However, with the sophistication of technology, these professionals must continuously upskill and remain abreast with the latest developments to remain relevant. They must choose from a myriad of platforms offering upskilling courses to find the right fit in accordance with their job role and industry readiness. Additionally, those who want to make a career in analytics must decide if they want to opt for a full-time course or pursue a degree online along with their regular jobs. Here are seven tips that may help you make a better decision regarding course selection.

Determine your goalsThe first step for course selection is to have a comprehensive estimate of your needs and aspirations. You must ascertain if you want to upskill, reskill or opt for an entirely new career trajectory. These goals would determine the kind, of course, most suited for you.

Look for a course with a practical approachThe field of data science entails entangling real-world problems. Therefore, it is imperative that the course you choose should give due importance to practical skills acquisition and provide hands-on training using industry-standard tools and techniques. Students should opt for courses that offer them opportunities to work on data sets, practice data cleaning and preparation along with visualisation and statistical analysis. These will help the learners to solve real-world problems and make them industry as well as job ready.

Consider the course contentThe field of data analytics is evolving continuously, with new technologies coming to the forefront every now and then. The course curriculum is thus a central tenant of any upskilling program in the field. One must choose a platform that offers a syllabus designed by experts in accordance with industry standards. Learners must carefully study all aspects of the course material and ensure it is not a cheap copy of content already available online. The important topics a course should cover include data mining, data visualization, statistical analysis, and machine learning.

Additionally, you must take the time to familiarise yourself with the faculty members, their qualifications, and their professional achievements. This information will help you gauge the quality of the content and the pedagogy.

Check the course instructors credentialsThe hallmark of a good course is the credibility and repute commanded by its instructors. Further elaborating on the aforementioned point, it is of utmost importance to look into faculty members academic qualifications and industry experience before opting for a course. A good teacher will be able to deliver course content in an easily comprehensible manner and cater to the learning requirements of a wide range of learners.

Check for reviews and recommendationsA good way of determining course efficiency and quality is to read the reviews and recommendations of those already enrolled in such courses or those working successfully in the field. Such assessments will help you gain insight into the specificities of the course and evaluate its underwhelming and compelling aspects.

Consider the course duration and formatA noteworthy quality of a course in data analytics is that it can be customized to suit ones needs and time requirements. Since these programs can be of varying lengths and formats, learners can choose to attend an online self-paced course or a full-time university curriculum. The choice should be guided by considerations like the time available and the style of teaching preferred.

Look for additional resources and supportPossessing knowledge of the specificities of data analytics is essential. However, this knowledge is useless if one cannot market oneself adequately. Simply put, one should know of the opportunities in the field, be able to contact people of interest, and showcase their skills in a way that impresses the potential employer. Therefore, a good course should offer additional resources and support, such as online forums, mentorship, and career services.Moreover, in cognizance of the fact that building a career in this field requires excellent communication skills, the course should offer training in resume building and conducting oneself in an interview. It is also essential to check the hiring partners of the platform to ascertain the career-building opportunities accorded post-course completion.

Bottom lineThe hiring ecosystem has changed substantially over the years. Employers now prioritise skill possession in addition to the educational qualifications of the applicants. This is especially true for those who want to pursue a career in data science or analytics. Therefore, there is an upsurge in the number of ed-tech platforms offering various courses in this segment. However, students must be meticulous in choosing the course as this may determine their career trajectory. The platform of choice must offer a holistic curriculum that provides an engaging learning experience.

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Looking to upskill in data analytics? Here are 7 tips for selecting the right course - Express Computer

Business Intelligence (BI) utilizes cutting-edge technology to analyze data and manage business information. This can encompass data mining, visualization, tools and infrastructure. Ultimately, BI involves adopting best practices within organizations to make data-driven decisions across multiple business aspects. This can be particularly effective within the financial sector, where data analytics are vital and require efficiency and accuracy to inform strategic decision-making. Ergo, this article presents an overview of the benefits of BI to optimize operations within financial organizations.

The finance sector inherently requires discerning risk management across multiple aspects of business preventative decisions can be significantly improved by implementing BI by accurately analyzing financial data and market trends. For example, BI can be effectively utilized to accurately assess the risk of customer loans based on pertinently circumstantial factors, such as the potential assets of a borrower, their earning capacity, and any extraneous economic influences that could impact repayments. For those who may find themselves in a high-risk category, BI can additionally detail appropriate alternatives, such as small loans for poor credit that are more suitable to those with a history of unstable finances. Ultimately, BI can facilitate organizations to minimize potential losses and bolster more shrewd risk management decisions.

BI is increasingly being adopted by businesses within the financial sector to significantly improve incisive decision making. BI tools can rapidly collect and organize data from myriad sources and compile the result into insightful reports that can assist financial analysts to make data-driven decisions. BI software can comprehensively evaluate the market and credit risks for companies and how particular strategies can affect profits. The increased accuracy that data-driven software affords is pertinently useful for sectors such as banking, which must be transparent and circumspect due to stringent regulatory monitoring. Moreover, large amounts of data can be overwhelming for employees to deal with BI circumvents human error that could detrimentally affect exactitude with regard to figures, customers, financial indicators, banking data and market conditions.

Fraud is a critical issue that affects all facets of the finance sector. Financial businesses that successfully safeguard their clients can build trust and confidence with fact-based security information derived from the expanding datasets that BI provides. Moreover, to mitigate fraud, BI can accurately and efficiently identify fraudulent activity that might affect their business by analyzing transactional data and detecting exceptionally specific or minute anomalies. Ultimately, BI can assist organizations to prevent potential financial losses and take appropriate corrective action against fraud by ensuring that potential risks are promptly detected and resolved.

Businesses within the finance sector can mitigate costs by utilizing BI to identify areas where expenses can be optimized, such as inefficient processes, redundant tasks and superfluous spending. Moreover, BI can assemble a budget analysis by analyzing raw data within the company, such as reducing inventory and increasing efficiency. Another benefit of BI application within finance is its ability to optimize human resources (HR). BI can accurately analyze countless skillsets of employees and identify where reductions or increases in personnel are required. BI software assists HR managers to make discerning decisions about teams and staff members, which can reduce costs overall.

In the finance industry, efficiency is essential to provide streamlined services to clients. In the past, manual data collection and reporting was exceptionally time-consuming for financiers. BI tools can increase efficiency by automating routine tasks and processes, mitigating the requirement for human intervention and freeing employees to focus on more critical tasks, such as data analysis and decision-making. Moreover, BI can rapidly organize and extract critical information from vast amounts of unorganized data, underpinning more precise and incisive business decisions with accurate data statistics.

BI software can assist companies in the finance sector to more accurately understand customer behavior and preferences to provide tailored services and enhance customer experiences. This can enable businesses to optimize their services by understanding needs, ultimately increasing sales and encouraging loyalty and trust. Furthermore, BI can provide collated datasets that anticipate new markets and lucrative opportunities for expanding a customer base. Lastly, BI can underpin more cost-effective means of interaction with clients by automatically suggesting services to those who may not be utilizing them, such as potential client upgrades to digital services or smartphone apps.

In summary, BI is radically transforming the finance sector by improving decision-making, managing risks, detecting fraud, reducing costs, increasing efficiency, and enhancing customer experiences. By leveraging BI tools, the finance sector can gain a competitive advantage and improve its overall performance. Ultimately, BI can enhance the finance sector to become a more streamlined and efficient industry it is of great anticipation for many how this paradigm shift will develop.

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How Business Intelligence Can Optimize Operations Within the ... - Analytics Insight