𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗡𝗼𝘄: 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 The world runs on data, and the ability to analyze it effectively is the key to success. This AI and Data Analytics training equips you with the skills to unlock the hidden potential within your data, transforming you from a passive observer to an active decision-maker. Whether you're a business professional, aspiring data scientist, or simply curious about the future of technology, this training will provide you with the knowledge and skills to thrive in the data-driven world. 𝗝𝗼𝗶𝗻 𝘂𝘀 𝗮𝗻𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮! For registration and more info please visit the following link: https://bit.ly/3RFUKT5 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼 𝗰𝗮𝗹𝗹 𝘂𝘀 𝗼𝗻: 𝟮𝟭𝟯 𝟮𝟲𝟮𝟲 𝗼𝗿 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: 𝟱𝟴𝟰𝟴 𝟲𝟮𝟴𝟰 𝗧𝗵𝗲 𝗰𝗼𝘂𝗿𝘀𝗲 𝗶𝘀 𝗠𝗤𝗔 𝗮𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗮𝗻𝗱 𝗛𝗥𝗗𝗖 𝗿𝗲𝗳𝘂𝗻𝗱𝗮𝗯𝗹𝗲 (𝗱𝗲𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗿𝗲𝗳𝘂𝗻𝗱 𝘀𝘁𝗮𝘁𝘂𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗹𝗮𝘁𝘁𝗲𝗿) #ArtificialIntelligence #GenerativeAI #DataAnalytics #DataVisualizations #Dashboard #Data #Ethics #DataGovernance
Athena Business School’s Post
More Relevant Posts
-
A few weeks ago, I came across an advertisement for the Elite Global AI Cohort 2.0 on LinkedIn. I decided to choose the Data Analytics and Business Analytics course, specifically focusing on AI in Data Analysis. The course covered a wide range of topics, like data summary, data visualization, hypothesis generation, data cleansing, and statistical concepts such as descriptive statistics, probability, and inferential statistics. We explored into various data visualization techniques like bar charts, pie charts, scatter plots, and line graphs. We also explored data modelling and prediction, which included model selection, training, evaluation, and deployment. Additionally, the course emphasized the importance of ethics and privacy in data analysis, covering aspects like fairness, transparency, privacy, and accountability. We learned how to effectively communicate insights through data storytelling, understanding our audience, and making actionable recommendations. We used tools like Data Squirrel and Gamma AI, which enhanced our learning experience. The training was truly remarkable. I'm sincerely grateful to the program's organizing team #EliteGlobalAI and everyone involved in making the experience a success. Thank you for providing us with such valuable learning in data analysis, I completed the course joyfully and am thankful to Elite Global AI. #DataAnalysis #DataAnalyst #EliteGlobalAI #EliteGlobalAICohort2_0 #Artificial_InteligenceCohort2_0
-
-
Data Scientist Intern @ Maruti Suzuki True Value | MSc Data Science @ CIT | 2x Hackathon Winner 🏆 | Python, SQL, Machine Learning | Follow me to Accelerate Your Data Science Learning⚡
💡 𝐁𝐘𝐓𝐄 24 𝐎𝐅 𝐌𝐀𝐂𝐇𝐈𝐍𝐄 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐄𝐒𝐒𝐄𝐍𝐓𝐈𝐀𝐋𝐒 𝐔𝐍𝐏𝐀𝐂𝐊𝐄𝐃 📦 Have you ever wondered how machines handle categorical data like "Red," "Yellow," or "Green"? 🤔 One-hot encoding simplifies this challenge! 🎯 📄Check out this crisp, beginner-friendly PDF to grasp the concept and see it in action. 🏷️Tagging some brilliant minds in the Data Science & AI community! Hargurjeet Singh Ganger ; Daksh Bhatnagar, Data Analyst ; Korrapati Jaswanth ; Shivam Shrivastava ; Mohamed Kayser ; Akshay Kumar ; Sarthak sharma ; Khushi Dubey 𝗥𝗲𝗽𝗼𝘀𝘁 with comments to educate and grow your own network! Follow Tarun K T for more on breaking down ML concepts! #MachineLearning #DataScience #AI #ML #MLEssentials #MLForBeginners #TechEducation #OneHotEncoding
-
𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐢𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚 𝐢𝐦𝐩𝐚𝐜𝐭 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐟 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥𝐬? 📈 Dealing with imbalanced data is essential for developing machine learning models that provide accurate insights and effectively serve diverse scenarios. ⏩ Here are some effective techniques: 1. Resampling Techniques: Balance your data by adding more instances of the minority class or reducing the majority class. 2. Data Augmentation: Boost the minority class by creating new data points from existing ones. 3. SMOTE (Synthetic Minority Over-sampling Technique): Generate synthetic examples to even out your dataset. 4. Ensemble Techniques: Combine several models to improve predictions, especially with imbalanced data. 5. One Class Classification: Focus on learning patterns from just the minority class, useful when examples are rare. 6. Cost-Sensitive Learning: Adjust misclassification costs to make the model prioritize the minority class. 7. Evaluation Metrics: Use precision, recall, and F1-score for a clearer performance picture than accuracy alone. Effectively addressing imbalanced data with these techniques is not just about improving model accuracy, it's about ensuring fairness and equity in decision-making processes. By prioritizing inclusivity and fairness, we create data-driven solutions that benefit society as a whole. [ Explore more in the post ] <><><><><> If you found this helpful don't forget to save this for later and comment your thoughts. 💬 Repost to help others ♻ Follow Piku Maity for more invaluable insights on Data & AI Happy Learning!! #dataprocessing #datacleaning #datascience #machinelearning
-
-
Data Analyst @ Target | Data Science, Data Analytics | Top data analytics voice on LinkedIn!
🔍 Data Cleaning: The Hidden Struggle in Analytics 🧹 Ever been knee-deep in messy datasets, wondering why you chose analytics in the first place? 😅 We've all been there. Data cleaning can feel tedious, but it's the foundation of any successful data science project. It’s not the glamorous part, but it’s a crucial step to ensure reliable insights. Here are a few simple techniques to clean your data: 1.Remove Duplicates: Ensuring no repeated records exist. 2.Handle Missing Values: Fill in or remove gaps in your data. 3.Correct Inconsistencies: Standardize values (e.g., USA vs. US). 4.Filter Outliers: Remove data points that don’t make sense. 5.Normalize Data: Scale your data so it's easier to compare. Remember, clean data = trusted analysis! 💡 #DataScience #Analytics #Data #AI #memes #datamemes #DataCleaning #BigData #MachineLearning #DataPreparation #AI #Tech
-
-
Artificial Intelligence and Cybersecurity Researcher || Digital Transformation || IS Security & Audit || Information Systems || Data Engineering || Machine Learning I| AI Ethics
In cybersurity anomaly intrusion detection, the application of these techniques in handling imbalanced data is crucial for our models to classify accurately and precisely.
𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐢𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚 𝐢𝐦𝐩𝐚𝐜𝐭 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐟 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥𝐬? 📈 Dealing with imbalanced data is essential for developing machine learning models that provide accurate insights and effectively serve diverse scenarios. ⏩ Here are some effective techniques: 1. Resampling Techniques: Balance your data by adding more instances of the minority class or reducing the majority class. 2. Data Augmentation: Boost the minority class by creating new data points from existing ones. 3. SMOTE (Synthetic Minority Over-sampling Technique): Generate synthetic examples to even out your dataset. 4. Ensemble Techniques: Combine several models to improve predictions, especially with imbalanced data. 5. One Class Classification: Focus on learning patterns from just the minority class, useful when examples are rare. 6. Cost-Sensitive Learning: Adjust misclassification costs to make the model prioritize the minority class. 7. Evaluation Metrics: Use precision, recall, and F1-score for a clearer performance picture than accuracy alone. Effectively addressing imbalanced data with these techniques is not just about improving model accuracy, it's about ensuring fairness and equity in decision-making processes. By prioritizing inclusivity and fairness, we create data-driven solutions that benefit society as a whole. [ Explore more in the post ] <><><><><> If you found this helpful don't forget to save this for later and comment your thoughts. 💬 Repost to help others ♻ Follow Piku Maity for more invaluable insights on Data & AI Happy Learning!! #dataprocessing #datacleaning #datascience #machinelearning
-
-
🔍 Continuing to explore the Future of Fair & Ethical AI! I’m excited to share that I’ve completed a new AI Data Fairness and Bias certification! In an era where algorithms shape decisions in finance, healthcare, and beyond, it’s essential to ensure AI systems reflect fair and inclusive practices. This certification covered everything from identifying bias in data collection and auditing models for fairness, to understanding the trade-offs between accuracy and protection, as well as deploying real-world solutions to mitigate predictive bias. AI has the potential to create a more equitable world, but we must be proactive in addressing algorithmic fairness and designing responsibly. If you’re interested in creating a future where AI truly serves everyone, let’s connect and discuss the possibilities! 🌍 #AI #DataFairness #AlgorithmicEthics #MachineLearning #BiasInTech #ResponsibleAI #TechForGood
-
A ton of companies with start a discussion with me about bringing "AI" into their factory and I love their enthusiasm and drive to improve their operations with the very best tools. But there's a buzzkill waiting in the wings. It is an un-sexy (but true) fact that most of these company's data foundation is not robust enough to support their initiatives. Manufacturing as an industry is challenged by this particularly. What can you do, if the AI supercar has the torque but none the towing capacity of the rusty pickup truck called data science and governance? Companies should keep their enthusiasm for the promise of AI without losing their resolve to support it effectively with other practices. There are ways to do this.
Investments in AI are forecast to reach $200 billion by 2025, but 35% of these projects will fail or be delayed for the same reason: Poor data quality. Data teams have long been shouting about the importance of data quality before data can be used. And now, those same data teams are often the last line of defense between GenAI success and $70 billion down the drain. The last thing we want is a failed GenAI push that burns trust in data across the business. So how do we prevent a mess we can’t clean up? Convince our stakeholders that data quality is a priority. They need to think of it as a foundational element of GenAI rather than a "we'll get to it later" issue. Fix the data, then build on it. Good input? Good output. So, start with what you know. The definition of data quality can vary between companies, departments, and even datasets, but you can always break down your data quality strategy into three components: • Explicitly define business requirements in conversation with stakeholders. Data is never perfect, only good enough to satisfy a use case. • Audit how your current data compares to those requirements. There are many ways data can “go wrong,” so it’s important to find gaps that reveal the lowest hanging fruit. • Continuously improve through time-tested approaches: clear ownership, standardized development processes, monitoring, education, and so on. GenAI is new, but we have decades of data governance best practices to build on. At the end of the day, earning stakeholder support is about showing — not just telling. Paint the picture of the goal, show your stakeholders where you are now (and what happens if you roll with what you’ve got), and watch for the “aha” moment. That buy-in is the first step to building our castles on solid ground instead of sand. #dataengineering #dataquality #genai #ai #datascience
-
-
Data Science Manager at GCash (No. 1 Finance App in the Philippines) | Head, Risk Data Science | Machine Learning, Analytics, Risk | I help companies create impactful and value-adding solutions using data
Businesses are thrilled with GenAI because of how quick they can deploy delightful apps (most quick-wins with minimal data— just feed an LLM a corpus of text and it will do) vs. what investment in data (collection, engineering, and quality) it takes for traditional data use cases before. If anything, this exciting shift should be a wake up call for data teams. It’s the perfect time to push for better foundations. I’m with Kevin Hu, PhD on this: we need to step up our game in talking about data quality and make sure we’re working hand-in-hand with our stakeholders to keep improving. #dataquality #dataengineering #data #ai #genai
Investments in AI are forecast to reach $200 billion by 2025, but 35% of these projects will fail or be delayed for the same reason: Poor data quality. Data teams have long been shouting about the importance of data quality before data can be used. And now, those same data teams are often the last line of defense between GenAI success and $70 billion down the drain. The last thing we want is a failed GenAI push that burns trust in data across the business. So how do we prevent a mess we can’t clean up? Convince our stakeholders that data quality is a priority. They need to think of it as a foundational element of GenAI rather than a "we'll get to it later" issue. Fix the data, then build on it. Good input? Good output. So, start with what you know. The definition of data quality can vary between companies, departments, and even datasets, but you can always break down your data quality strategy into three components: • Explicitly define business requirements in conversation with stakeholders. Data is never perfect, only good enough to satisfy a use case. • Audit how your current data compares to those requirements. There are many ways data can “go wrong,” so it’s important to find gaps that reveal the lowest hanging fruit. • Continuously improve through time-tested approaches: clear ownership, standardized development processes, monitoring, education, and so on. GenAI is new, but we have decades of data governance best practices to build on. At the end of the day, earning stakeholder support is about showing — not just telling. Paint the picture of the goal, show your stakeholders where you are now (and what happens if you roll with what you’ve got), and watch for the “aha” moment. That buy-in is the first step to building our castles on solid ground instead of sand. #dataengineering #dataquality #genai #ai #datascience
-
-
Data quality always needs to be a priority. Period. This was always the case with AI and is no different with GenAI. The struggle is to sometimes have to convince stakeholders to allocate proper time to data preparation.
Investments in AI are forecast to reach $200 billion by 2025, but 35% of these projects will fail or be delayed for the same reason: Poor data quality. Data teams have long been shouting about the importance of data quality before data can be used. And now, those same data teams are often the last line of defense between GenAI success and $70 billion down the drain. The last thing we want is a failed GenAI push that burns trust in data across the business. So how do we prevent a mess we can’t clean up? Convince our stakeholders that data quality is a priority. They need to think of it as a foundational element of GenAI rather than a "we'll get to it later" issue. Fix the data, then build on it. Good input? Good output. So, start with what you know. The definition of data quality can vary between companies, departments, and even datasets, but you can always break down your data quality strategy into three components: • Explicitly define business requirements in conversation with stakeholders. Data is never perfect, only good enough to satisfy a use case. • Audit how your current data compares to those requirements. There are many ways data can “go wrong,” so it’s important to find gaps that reveal the lowest hanging fruit. • Continuously improve through time-tested approaches: clear ownership, standardized development processes, monitoring, education, and so on. GenAI is new, but we have decades of data governance best practices to build on. At the end of the day, earning stakeholder support is about showing — not just telling. Paint the picture of the goal, show your stakeholders where you are now (and what happens if you roll with what you’ve got), and watch for the “aha” moment. That buy-in is the first step to building our castles on solid ground instead of sand. #dataengineering #dataquality #genai #ai #datascience
-
-
Executive Leader in Advanced Data Analytics, Compliance & Operational Excellence with a dash of mentorship and leadership
🔍 Understanding Data Drift and Addressing Bias in Data 🔍 In today's data-driven world, one of the biggest challenges for organizations is managing data drift—the phenomenon where the statistical properties of data change over time. This can have significant implications for machine learning models and decision-making processes, leading to reduced accuracy and performance. So, what can we do about it? Here’s where identifying and eliminating biases becomes crucial: 1. Monitor for Data Drift Regularly: Set up processes to continuously monitor changes in data distributions. This helps identify when and where the data is drifting, allowing timely interventions. 2. Establish Strong Data Quality Protocols: By maintaining robust validation and testing procedures, organizations can ensure that data remains reliable and reflects the real-world scenarios it's meant to model. 3. Bias Identification: Bias can creep into datasets in subtle ways, from overrepresentation of certain groups to skewed sampling methods. It's essential to assess datasets for biases during collection, pre-processing, and model training stages. 4. Bias Mitigation: Eliminate or reduce bias through techniques such as re-sampling, synthetic data generation, or using fairness constraints. A commitment to fair AI is key to ensuring your models are both accurate and ethical. 5. Transparency and Explainability: Build transparency into your models to better understand how decisions are being made. Use explainable AI (XAI) tools to detect potential biases within predictions, helping stakeholders make informed choices. In short, proactively monitoring data drift and eliminating biases will not only improve model accuracy but also foster trust and fairness in your data science initiatives. #DataDrift #BiasInData #DataQuality #FairAI #MachineLearning #DataScience #ModelPerformance #ArtificialIntelligence #theMARScorp
