Zia Zaman’s Post

"We need to start thinking about how we can explain AI in a way that will benefit the users and help them to understand how systems are reaching their conclusions." One of the key takeaways from Jakub Janowiak podcast episode on building trustworthy AI systems. Read more on this and other insights here >> https://lnkd.in/gizXWWXN

NEW EPISODE! | Just like you can’t make an omelet without eggs, you can’t make AI happen without humans. And unlike the eggs in an omelet, we don’t have to break any humans to make AI work, in fact, it can make us stronger, but only if we use it to maximize the effectiveness of the “humans in the loop.” That’s what Tiankai Feng talks about in our latest podcast episode, “Data Governance and AI.” A data leader by day, a musician at night, and an optimist at heart. Tiankai Feng is the Data Strategy and Data Governance Lead at Thoughtworks Europe. Tiankai has been in the data game for over 10 years and is passionate about the human aspect of data - working with his clients on improving communication, collaboration, and creativity in data. We get into some amazing topics like empathy, music (Tiankai loves to write/perform songs about data), explainability in AI, the importance of communication, and of course data governance. LISTEN NOW -> https://bit.ly/3z9iKY8

Continuing Our Series on 2025’s Hottest Data Engineering Trends Next Up → Data Pipelines for AI Applications Data pipelines are the backbone of AI systems, enabling real-time interactions and advanced analytics by feeding AI models with the necessary data. For AI to truly deliver value, businesses need robust, reliable pipelines to process and integrate unstructured data into AI workflows seamlessly. 📊 Why It Matters Building code to extract and transform data from various sources often takes weeks, slowing innovation. AI can automate much of this work, accelerating progress and efficiency. "AI will empower data engineers to focus on higher-value tasks. It’ll make their life easier in areas that they don’t want to invest. Maintaining pipelines for rapidly changing APIs isn’t exactly thrilling work. AI takes over the drudgery, letting engineers focus on innovation." 🎙️ Tune In Catch Rivery’s CEO, Itamar Ben Hemo, on the latest Grit Daily News podcast, discussing the critical role data pipelines play in driving AI applications. https://lnkd.in/ezujTYTb

Data Science in Another World: A Data Scientist's Isekai Survival Guide 🧝🧙🥷🧚 Imagine being transported to a fantastical world—a classic isekai scenario—but as a data scientist. How would you survive and thrive in such a realm? Let's delve into the whimsical yet practical aspects of navigating an isekai world with data science skills! 1. 🕵️ Analytical Magic As a data scientist, your analytical skills are your greatest asset. Use data analysis techniques to understand the new world's flora, fauna, and magical phenomena. Analyze patterns, correlations, and anomalies to uncover hidden secrets and optimize survival strategies. 2. 🦾 Algorithmic Adaptation Just as you create algorithms to solve complex problems in the real world, apply algorithmic thinking to your isekai adventures. Develop decision trees for risk assessment, predictive models for weather forecasting, and optimization algorithms for resource management. 3. 🌱Data Gathering Quests Treat data gathering quests as exciting opportunities to collect valuable information. Whether it's mapping uncharted territories, studying mythical creatures, or cataloging magical artifacts, approach each quest with a data-driven mindset to gain insights and unlock new possibilities. 4. 🤖 Machine Learning Magic Leverage your knowledge of machine learning to train magical creatures or construct enchanted objects. Develop AI companions for assistance, automate tasks with magical constructs, or create predictive models for predicting mystical phenomena. 5. 💬 Ethical Enchantment Just as ethics is crucial in data science, maintain ethical standards in your isekai endeavors. Respect local customs and cultures, use data responsibly, and ensure that your magical innovations benefit rather than harm the world around you. 6. 🫂 Collaborative Conjuring Form alliances and collaborate with other isekai inhabitants. Combine your data science expertise with their unique skills and knowledge to tackle challenges collaboratively. Build data-driven communities for mutual support and progress. In conclusion, while getting isekai'd as a data scientist may initially seem daunting, your analytical prowess, algorithmic mindset, and ethical compass will guide you through fantastical realms with confidence. Embrace the magic of data science in another world, and let your data-driven journey unfold! P.S. Yes, I am an otaku. Reading manga, Bridget #DataScienceIsekai #FantasyAnalytics #SurvivalGuide

🌟 Excited to share my latest project: **Medical Insurance Premium Prediction using Machine Learning**! 🌟 In today’s rapidly evolving healthcare landscape, understanding and accurately predicting medical insurance premiums is crucial for both providers and consumers. My project aims to leverage machine learning techniques to enhance the precision of premium forecasting. 🔑 ** Highlights:** Data Collection: Utilized extensive datasets that include a range of factors such as age, health status, lifestyle choices, and geographical location to train the model. Feature Engineering: Implemented advanced feature engineering techniques to identify the most relevant predictors affecting insurance premiums, ensuring the model's robust performance. Model Selection: Experimented with various algorithms, including Linear Regression, Decision Trees, and Random Forest, to determine the best fit for the data. Performance Evaluation: Employed like Mean Absolute (MAE) and R-Squared to assess model accuracy and reliability. The results were promising, with notable improvements over traditional methods. Visualization: Created insightful visualizations to communicate findings effectively, making it easier for stakeholders to understand the factors influencing premium variations- Real-World Impact: This project has the potential to assist insurance companies in stratifying risk and pricing policies more effectively, ultimately leading to fairer for consumers. 🛠️ **Technologies Used**: Python Pandas NumPy Scikit-learn Matplotlib Seaborn I’m looking forward to connecting with professionals in the insurance and data science fields to discuss insights, trends, and future collaborations! 📊💡 If you're interested in learning more about my project or want to share your thoughts, feel free to comment below or reach out directly! #MachineLearning #DataScience #Insurance #HealthTech #PredictiveAnalytics #ProjectShowcase #Python #Pandas #NumPy #ScikitLearn #Matplotlib #Seaborn #ArtificialIntelligence #DataVisualization

API Data Collection Starting any program or field involves a process: first, you don't know much, then you learn, and as you encounter new challenges, this cycle continues. Data science is a very broad field. I thought I had seen it all when I could import CSV files from the cloud, scikit, and websites. However, my recent challenge, which I'm currently finishing, proved me wrong. For data collection, I had to use API REST calls to structure a place where the data would be stored and then request publicly available data. Initially, the API REST calls seemed like a bunch of code, which had to be normalized into a table. Then, I would import, or, as I would say, 'summon' the data from wherever it was publicly available and arrange it into existing columns 😂 . If you don't understand this, don't worry—it's probably because you haven't practiced it. Even I wouldn't have understood it initially. Data science is a beautiful field, but it has its challenges. Here are a few: Time Consumption: This field takes a lot of your time, and the only compensation you get is the success of your operation. Patience: This is indispensable in coding. You also need to learn to control your anger, as what might suffer the impact may be the gadget you are using at that moment. Consistency: Data science is vast, especially if you want to advance into AI development. Consistency is essential to achieve your goals. Determination: Set your targets and ensure you meet them so you can progress. Skepticism: You need to ask yourself questions—why, what, and how. Otherwise, you'll code without understanding what you're doing. Use the Internet: I cannot emphasize enough how much this has helped me. AI can be your assistant, so make use of it, especially in the tech space. There were other intense challenges, but I'll leave it here for now. Happy Sunday! #datascience #dataresearch #datagathering #datascientist #data #datacollection #API #APIREST #analytics #AI

 Certified and ready for new challenges, thanks to Knowledge Solutions Institute.#Learnwithus #AI #KnowledgeSolutionsInstitute #zeroupfrontfee #payafterplacementprogram #Getjobworldwide #AIengineer #Deeplearningegineer #PythonDeveloper #TableauDeveloper #ArtificialIntelligence #AIGlobal #AIFuture #AITrends #MachineLearning #MachineLearningEngineer #MLAlgorithms #MLModelling #MLApplications #DataScience #DataScientist #DataScienceLife #DataScienceCommunity #BigData #BusinessAnalyst #BA #BusinessAnalysis #BusinessInsights #BusinessStrategy #Dataanalyst

 Certified and ready for new challenges, thanks to Knowledge Solutions Institute.#Learnwithus #AI #KnowledgeSolutionsInstitute #zeroupfrontfee #payafterplacementprogram #Getjobworldwide #AIengineer #Deeplearningegineer #PythonDeveloper #TableauDeveloper #ArtificialIntelligence #AIGlobal #AIFuture #AITrends #MachineLearning #MachineLearningEngineer #MLAlgorithms #MLModelling #MLApplications #DataScience #DataScientist #DataScienceLife #DataScienceCommunity #BigData #BusinessAnalyst #BA #BusinessAnalysis #BusinessInsights #BusinessStrategy #Dataanalyst

 Certified and ready for new challenges, thanks to Knowledge Solutions Institute.#Learnwithus #AI #KnowledgeSolutionsInstitute #zeroupfrontfee #payafterplacementprogram #Getjobworldwide #AIengineer #Deeplearningegineer #PythonDeveloper #TableauDeveloper #ArtificialIntelligence #AIGlobal #AIFuture #AITrends #MachineLearning #MachineLearningEngineer #MLAlgorithms #MLModelling #MLApplications #DataScience #DataScientist #DataScienceLife #DataScienceCommunity #BigData #BusinessAnalyst #BA #BusinessAnalysis #BusinessInsights #BusinessStrategy #Dataanalyst

 Certified and ready for new challenges, thanks to Knowledge Solutions Institute.#Learnwithus #AI #KnowledgeSolutionsInstitute #zeroupfrontfee #payafterplacementprogram #Getjobworldwide #AIengineer #Deeplearningegineer #PythonDeveloper #TableauDeveloper #ArtificialIntelligence #AIGlobal #AIFuture #AITrends #MachineLearning #MachineLearningEngineer #MLAlgorithms #MLModelling #MLApplications #DataScience #DataScientist #DataScienceLife #DataScienceCommunity #BigData #BusinessAnalyst #BA #BusinessAnalysis #BusinessInsights #BusinessStrategy #Dataanalyst