RECOMMENDED READING: AI in Data Analytics: The Future of Business Intelligence 💥 Data analytics using artificial intelligence (AI) and machine learning (ML) has become mainstream in a wide range of industries, including retail, financial services, healthcare, manufacturing, and many others. 💥 With AI and ML, it’s now possible to efficiently analyze extremely large data sets and deliver a more sophisticated level of business intelligence. 💥 AI in data analytics is the future, but to take full advantage of it, organizations must up their commitment to data organization and the development of internal data analytics expertise. READ MORE: https://bit.ly/4bMmDQY #Prime8Consulting #AIConsulting #AIBestPractices #AIAndSales
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How AI is Revolutionizing Business Intelligence
How AI is Revolutionizing Business Intelligence
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Statistical analysis of rounded or binned data https://lnkd.in/dUKWTAVF AI News, AI, AI tools, Innovation, itinai.com, LLM, Matthias Plaue, t.me/itinai, Towards Data Science - Medium 🚀 **Practical AI Solutions for Middle Managers** Are you looking to leverage the statistical analysis of rounded or binned data to drive your company's success with AI? Discover how AI can transform your operations, enhance decision-making, and drive growth. 🔍 **Understanding the Impact** The article "On the Statistical Analysis of Rounded or Binned Data" sheds light on the challenges of rounding or binning in statistical analyses. It delves into Sheppard's corrections and total variation bounds, offering insights into addressing errors when computing statistical values from rounded or binned data. 📊 **Practical Insights** Sheppard's corrections provide approximations to estimate original data from rounded values, offering valuable insights when the probability density function is smooth and the sample size is moderate. Total variation bounds and Fisher information-based bounds help constrain the error in computing the mean based on rounded or binned data. 🤖 **AI Solutions for Your Business** Looking to harness the power of AI for your company? Connect with us at [email protected] to explore how AI can redefine your operations, identify automation opportunities, and drive performance through strategic KPI management. 🌟 **Spotlight on AI Sales Bot** Discover our AI Sales Bot at itinai.com, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Explore how AI can revolutionize your sales processes and customer engagement. 🔗 **Useful Links** - AI Lab in Telegram @aiscrumbot – free consultation - [Statistical analysis of rounded or binned data](link to the article) - Towards Data Science – Medium - Twitter – @itinaicom Let's unlock the potential of AI for your business together! #AISolutions #AIforMiddleManagers #DataAnalysis #AIInnovation
Statistical analysis of rounded or binned data https://itinai.com/statistical-analysis-of-rounded-or-binned-data/ AI News, AI, AI tools, Innovation, itinai.com, LLM, Matthias Plaue, t.me/itinai, Towards Data Science - Medium 🚀 **Practical AI Solutions for Middle Managers** Are you looking to leverage the statistical analysis of rounded or binned data to drive your company's success with AI?...
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#AI can help prevent human mistakes in data analytics! AI’s impact in data analytics is undeniable. It can eliminate and prevent human error working with data and do it much faster than anyone could hope to accomplish. As we enter a new era of data analytics, AI will continue to play a significant role. ExperienceFlow.AI can identify and implement AI’s role for your enterprise. Click here to know more! https://lnkd.in/d5J9r6jY Giri Srinivas ATG A Anand R. Arjun I. Srinivas Koppolu Rama Mohan Venkata Kadayinti #decisionintelligence #artificialgeneralintelligence #autonomousenterprise #digitalnervoussystem
AI to Reduce Human Mistakes in Data Analysis
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🚀 Embracing the Future: AI's Impact on Data Analysts 🚀 The rapid rise of Artificial Intelligence (AI) is transforming various aspects of our professional landscape, and data analysts find themselves at the forefront of this exciting revolution. While some may fear that AI will replace data analysts entirely, I firmly believe that it will revolutionize the role rather than replace it. In the past, data analysts spent considerable time on manual tasks like data cleaning and visualization. However, AI has now empowered us to automate these processes, allowing data analysts to shift their focus towards more strategic endeavors. Imagine utilizing AI to develop predictive models, uncover fraud, and optimize marketing campaigns - these opportunities are now within our grasp! To thrive in this evolving landscape, data analysts must embrace AI and continually expand their skill set. Those who adeptly leverage AI's potential will be able to automate more tasks and delve deeper into complex strategic analysis. From generating invaluable insights from massive datasets to crafting interactive visualizations, the possibilities are limitless! As businesses increasingly rely on data-driven decisions, the value of data analysts equipped with AI skills will soar. Our ability to translate data into actionable recommendations will be a critical asset in shaping the future success of companies across industries. Conversely, those who resist integrating AI into their skill set risk falling behind. Embracing AI is not just a choice but a necessity to remain competitive in the data analytics field. By adapting and mastering AI-powered tools, we can stay ahead of the curve and unlock the full potential of data-driven possibilities. In conclusion, let's welcome AI as an empowering ally rather than a formidable foe. As data analysts, we have an unprecedented opportunity to reshape our role, making it more impactful and rewarding than ever before. Together, we can embrace the potential of AI and revolutionize the world of data analytics! #AI #DataAnalytics #FutureReady
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Electrical Engineer | Upto learning Data science, Machine Learning, Deep learning | #MachineLearning #DataScience #YouTuber
𝗠𝗮𝗸𝗶𝗻𝗴 𝗦𝗲𝗻𝘀𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮: 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗔𝗜 Imagine you're organizing a photo album. You have tons of pictures, but not all are equally important. Some might be blurry or irrelevant. That's where sorting comes in. In the world of data science and AI, feature selection and pattern extraction are like your sorting tools! 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻:: Choosing the Right Photos What it is : Selecting the most useful information (features) from your data. Think of it like: Picking the best pictures for your album based on relevance and clarity. Why it's important: Reduces complexity, improves model performance, and helps us understand which features matter most. Example: In a spam email classifier, features might be words or phrases. Feature selection helps identify the most indicative words for spam, like "free" or "urgent." 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Finding Hidden Themes What it is : Identifying new patterns or relationships within your data by creating entirely new features. Think of it like : Grouping related pictures in your album to tell a story. Why it's important : Uncovers hidden insights, simplifies complex data, and creates more powerful features for models. Example: Analyzing customer purchase history. Pattern extraction might create a new feature combining items frequently bought together, helping recommend similar products. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Feature selection chooses from existing data. Pattern extraction creates entirely new features. So, which one to use? It depends! Sometimes, both are helpful. Feature selection is good for interpretable models where you want to understand why something works. Pattern extraction is great for complex problems where hidden patterns might hold the key. Remember: Both techniques help us make sense of data and build better AI models! Muhammad Irfan Dr. Sheraz Naseer - (PhD Artificial Intelligence, Data Science) Muhammad Haris Tariq Mehran Ali Shaheryar Yousaf
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Best-selling Author | Digital Innovation Leader | C-Level Advisor and Strategist | Venture Capital | Author, Speaker, Publisher | Mentor, Coach, Team Builder
A few years ago, I defined the Seven Links in the Data Value Chain – a framework for understanding the unique skills required to get Information and Insights out of the Data your company is aggregating. The key message was that it takes a diverse set of skills to go from intriguing ideas to actionable insights – and it takes a well-rounded team to make that journey. Fast-forward to today, and there is a new tool in the toolbox – Gen AI. Yes, statistical forecasting ("Predictive AI") has been part of the data analyst's arsenal for many years. But we are now in the era of Generative AI, where original (as it were) content is created from existing data sets. Will Gen AI change the Data Value Chain? It won't magically eliminate any of the Seven Links – but it may be able to accelerate and automate the work required to get things done. Click on the link to read the entire article, including examples of GenAI for data analytics “in the wild”. = = = = = = Follow #MakerTurtle to stay current on #CorporateInnovation and other important topics. #Innovation, #digitalvalue, #DigitalTransformation, #CorporateInnovation, #ArtificialIntelligence, #AI, #StrategicThinking, #databricks
How Gen AI Transforms The Data Value Chain | Maker Turtle
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AI PM / Leveraging AI on Data Integration to Boost Productivity on Engineering and Operational areas.
Embracing Controlled Chaos: Delivering Reliable AI-powered Solutions through High-Quality Data used to create AI models All IT administrators understand that having a backup plan is crucial, but the real test of its effectiveness comes during unexpected chaos brought on by critical failures. This is where the concept of 'controlled chaos' plays a vital role. By intentionally introducing challenging scenarios, we can truly assess and improve our recovery strategies. The more scenarios and tests we conduct, the more reliable the system becomes... but there's always an unexpected scenario lurking in the shadows. What sets our current approach apart is the integration of Artificial Intelligence (AI). AI has the potential to significantly accelerate recovery processes in chaotic situations, thereby adding extra reliability to our solutions. However, the efficacy of AI heavily depends on the quality of the data it's trained with. This leads us to an intriguing predicament reminiscent of the classic 'chicken and egg' scenario: which should take precedence, the development of AI tools or the preparation of clean, reliable data for the quality-based learning process of these tools? Despite this clear dilemma, real-world application often reveals a different story. In daily practice, when working with skilled teams, it becomes evident that theoretical simplicity doesn't always align with practical reality. This balance between AI and data quality is further illustrated by industry perceptions and challenges. For instance, according to a recent survey, 61% believe the Data Analyst is the primary role in data science. Yet, 72% state that the most challenging part of a data science project is "Data cleaning and processing," typically the domain of Data Engineers. This is even more important when we are talking about Industrial AI with billions of data coming in. Thus, whether advancing in AI now or later, it’s crucial to always prioritize clean data by keeping the end goal in mind and setting proper information requirements from the start. In some industrial projects, this is achieved by applying e.g. the CFIHOS Standard. In the Architecture, Engineering, and Construction (AEC) sector, it can be done by introducing OMNICLASS or UNICLASS classification systems, among other methods. We can multiply such methods. With properly cleaned data, even less experienced Data Analysts can work wonders, and ... everyone has a better chance of peaceful sleep.
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Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area https://lnkd.in/dyfsUsNX AI News, AI, AI tools, Innovation, itinai.com, Jin Cui, LLM, t.me/itinai, Towards Data Science - Medium 🚀 Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area 🚀 🔍 Motivation: Publicly available data on socio-economic characteristics of geographic areas in Australia, such as income, occupation, education, employment, and housing, presents an opportunity to rank these areas based on their advantage or disadvantage. 🔧 The Problem: Understanding which data points explain the most variations is crucial for deriving a score that accurately reflects the socio-economic status of different geographic locations. 📊 The Data: Utilizing data from the Australian Bureau of Statistics (ABS) at the Statistical Area 1 (SA1) level, we have a detailed dataset to analyze and derive meaningful insights. 🔍 The Steps: Our Python code showcases the application of Principal Component Analysis (PCA) to derive a socio-economic score, which is then validated against the Index of Economic Resource (IER) published by ABS. ✅ The Validation: The derived scores are rigorously validated against the published IER scores to ensure accuracy and alignment with the ABS methodology. 🎯 Concluding Thought: By leveraging dimensionality reduction techniques like PCA, we can effectively calibrate socio-economic scores, providing valuable insights for informed decision-making. 🌟 Spotlight on a Practical AI Solution: Explore our AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. 🔗 Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Towards Data Science – Medium - Twitter – @itinaicom If you're looking to harness the power of AI to stay competitive and drive business growth, connect with us at [email protected] and stay tuned on our Telegram channel or Twitter for continuous insights into leveraging AI. Let's unlock the potential of AI together! #AISolutions #AIforBusiness #DataInsights
Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area https://itinai.com/deriving-a-score-to-show-relative-socio-economic-advantage-and-disadvantage-of-a-geographic-area/ AI News, AI, AI tools, Innovation, itinai.com, Jin Cui, LLM, t.me/itinai, Towards Data Science - Medium 🚀 Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantag...
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