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Sharing update from SSRN publishing... "Dear Ashutosh Bhatnagar: Your paper, "NEIGHBORHOODS OF PRIMES", was recently listed on SSRN's Top Ten download list for: Discrete Mathematics & Combinatorics eJournal and Number Theory eJournal. As of 02 October 2024, your paper has been downloaded 16 times. You may view the abstract and download statistics at: https://lnkd.in/eMmhurr4. Top Ten Lists are updated on a daily basis. Click the following link(s) to view the Top Ten list for: Discrete Mathematics & Combinatorics eJournal Top Ten and Number Theory eJournal Top Ten. Click the following link(s) to view all the papers in: Discrete Mathematics & Combinatorics eJournal All Papers and Number Theory eJournal All Papers. To view SSRN's Top Ten lists for any network, subnetwork, eJournal or topic on the Browse list (reachable through the following link: https://lnkd.in/eEmS8twm), click the "i" button to the right of the name, and then select the "Top Downloaded" link in the popup window. Your paper may be included in future Top Ten lists for other networks or eJournals. If so, you will receive additional notices at that time. Questions? Visit the SSRN Support Center. This email helps you gauge the impact of your paper. To unsubscribe from ALL notifications, including SSRN eJournals, click here. Thank you, The SSRN Team"

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📢 Best Advice from a Senior Data Scientist Packed in One Podcast: Insights from Abhishek Das!...🌟 🔷 Speaker: Abhishek Das Senior Data Scientist at Aptus Data Labs Ex-HSBC 9+ Years of Experience in Advanced Analytics and Machine Learning 🔷 Hosted by: Avinash Dara Here are some highlights from his talk: 🔷 His Personal Life: • He came from an Electronics and Communication Engineering background with an interest in research. • He was always interested in the business field to scale. • In his college placements, he joined e2open where the environment was new to him. He took some time to understand and scale in that industry. After that, his data science career started. In addition to this, he also started writing a book. 🔷 He worked across diverse industries such as: • Transportation, pharmaceuticals, manufacturing, supply chain, e-commerce, and banking. • Each sector has shown him the immense value data science brings to businesses, driving growth through informed decision-making. 🔷 He explained what Data Science is: • Data science is leveraging data to add value to a business. • He gave a practical example like Uber and Ola, which calculate the ride price based on other factors. 🔷 Career Paths in Data Science: 1. Data Engineer o Roles: Building and maintaining data infrastructure o Skills to acquire: Data engineering tools and technologies 2. Data Analyst o Roles: Extracting data, proficient in SQL, visualization, and providing insights o Skills to acquire: Strong SQL skills, data extraction abilities, and expertise in visualization tools 3. Data Scientist o Roles: Focusing on machine learning, algorithms, optimization, and prediction o Skills to acquire: Proficiency in machine learning, algorithms, and predictive analytics 4. ML Engineer o Roles: Researching, creating, and improving algorithms, and deploying models o Skills to acquire: Deep understanding of algorithm development and deployment 🔷 His advice for interview tips: 1. Be truthful about your skills. 2. Clearly indicate your proficiency levels. 3. Showcase 2-3 relevant projects, including ML projects. 4. Align your projects with industry-relevant problems, such as financial predictions or sales forecasting. 🔷 His approach for finding opportunities: • Apply to startups and MNCs. • Optimize your resume on LinkedIn. • Network with hiring managers and senior employees for referrals. • Target analytics-driven companies. 🔷 He also explained about LinkedIn: • LinkedIn is an incredible platform for networking and professional development. • Maintain a problem-solving mindset and continually showcase your skills and projects. #datascience #dataengineering #mlengineer #machinelearning #ccbp #advise #podcaste #datascientist #learning

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Something I've written on how a T20 batting innings can be assessed. This framework can be extended to assessment of individual players as well, which I will do in the susequent posts. For now, please feel free to comment and if you like the content, please subscribe. #cricket #cricketanalytics #data #dashboards #datavisualization #sportsanalytics https://lnkd.in/gb7T-UzJ

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Reality of data analys career in 2024 Recorded an unfiltered discussion with industry experts Summarizing it in my own words (long read) [1] Data analytics is not just about building dashboard in 15 minutes or 1 hour. It is just a minor part of data analyst work. Most "influencers" are fantasizing it to make it appear so easy for vulnerable people for their own benefits. [2] It is not that easy for freshers or anyone to get a DA job as it is glorified on the internet. As of Aug 2024 there is a huge competition and limited roles. A fresher should do something outstanding to get recruiters attention- like having a solid project portfolio, online credibility on LinkedIn, cold emailing, messaging on LinkedIn to get recommendations etc. [3] Do not buy any paid course directly if you have no idea about this field. Spend 3-4 days and do solid research. Learn from free resources first to understand if you like this role, do more research about market condition. Most importantly do not blindly believe anyone on the internet - do your own research. [4] Offline networking can be a game changer for both freshers, people with career break and experienced people wanting to transition career. Check for the offline events near your place and join them. [5] 100 % Job guarantee, pay after placement, get 24 LPA - all these are scams running in the market. If you do not pay time extra for research you will loose both your money and time. [6] Domain knowledge is key. Freshers can domain knowledge by starting with business KPIs, understanding the business process behind the apps use, checking customer stories on Microsoft, following case studies, solving unguided projects (check codebasics resume project challenges. This won't completely replace the need for practical experience but it will be better than having no experience. [7] People with career gap or transitioning career should try to combine their past domain experience with their tool skills to make a strong resume and apply for roles particular to that domain. For example, a person with sales experience in telecom domain should have projects particular to that domain/function to show relevant experience and also maintain the salary range. There are more insights check the full video on YT if you are interested. Please spread this in your network so that it would reach the "insta aspirants" too. They are the most vulnerabale and often targeted by scammers.

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Question : Why does an AI product manager need to monitor #AI model❓ Once AI model is developed and deployed, the job doesnt end there. Model encodes pattern of the data it has seen during training phase. There may be several patterns or edge cases which are not part of training data. If model encounters any such patterns in testing or production phase, it will produce results which wont make sense without giving any warning about it. 👉 Data drift is a common challenge faced by AI-powered products. Over time, the distribution of input data can shift, leading to a mismatch between the model's training data and the real-world data it encounters. This can result in degraded model performance, unexpected outputs, and even complete model failure. 👉 The consequences of data drift can be severe, ranging from suboptimal user experiences to regulatory compliance issues. Product managers must be vigilant in detecting and addressing these challenges to ensure the continued success of their AI-powered offerings. 👉 Sudden changes in metrics can indicate input data drift. Additionally, monitoring the distribution of model outputs can reveal output drift, which may signal a need to retrain or fine-tune the model. Detecting data drift early is crucial. Product managers can leverage techniques such as A/B testing, anomaly detection, and continuous monitoring to identify and address these issues. Once detected, the product team can take appropriate actions, such as retraining the model, adjusting the data pipeline, or even considering a model architecture change. 💡 Quick Tip: Integrate model monitoring into your product roadmap and allocate dedicated resources to ensure the long-term success of your AI-powered offerings. Establish a comprehensive model monitoring dashboard to gain real-time visibility into the health and performance of your AI models. #artificialintelligence #productmanagement #datascience #productmanager

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Very interesting module on Data Modelling- especially how to use PBI to execute dimensional modelling and making Star and Snowflake schemas. Performance comparison between the types of schemas was a real eye opener.

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As data sets get larger, interpreting correlation matrices can become quite challenging. But fear not! A correlation matrix plot can come to the rescue, making it a breeze to understand the correlation structure in your data. Here's why it's awesome: 🔍 Visual Clarity: Instead of sifting through a sea of numbers, the plot provides a visual representation, making it easier to spot patterns and relationships at a glance. 📈 Intuitive Interpretation: With colors and shapes, it's simpler to grasp which variables are positively or negatively correlated, helping you make informed decisions. 🔄 Symmetry: The plot mirrors the correlation matrix, ensuring that you can quickly identify symmetric patterns without getting lost in rows and columns. 🌟 Efficiency: Save time and effort by leveraging this graphical tool to streamline your data analysis process, especially when dealing with large data sets. So, next time you're tackling a complex correlation matrix, consider creating a correlation matrix plot. Your eyes will thank you! Important note: Correlations can be influenced by factors such as sensitivity to outliers and the inability to handle non-linear relationships. Investigating these aspects is crucial. The graph below was created using the corrplot() function from R's corrplot package. This function offers extensive options for further data evaluation, so I highly recommend checking it out. Join me for an insightful webinar on May 7, where we'll delve into "Data Analysis & Visualization in R"! In addition to exploring various aspects of data analysis and visualization, I'll demonstrate the process of calculating and plotting a correlation matrix using R programming. More info: https://lnkd.in/eVXD2x78 #rstats #statistics #correlation #dataanalysis

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As data sets get larger, interpreting correlation matrices can become quite challenging. But fear not! A correlation matrix plot can come to the rescue, making it a breeze to understand the correlation structure in your data. Here's why it's awesome: 🔍 Visual Clarity: Instead of sifting through a sea of numbers, the plot provides a visual representation, making it easier to spot patterns and relationships at a glance. 📈 Intuitive Interpretation: With colors and shapes, it's simpler to grasp which variables are positively or negatively correlated, helping you make informed decisions. 🔄 Symmetry: The plot mirrors the correlation matrix, ensuring that you can quickly identify symmetric patterns without getting lost in rows and columns. 🌟 Efficiency: Save time and effort by leveraging this graphical tool to streamline your data analysis process, especially when dealing with large data sets. So, next time you're tackling a complex correlation matrix, consider creating a correlation matrix plot. Your eyes will thank you! Important note: Correlations can be influenced by factors such as sensitivity to outliers and the inability to handle non-linear relationships. Investigating these aspects is crucial. The graph below was created using the corrplot() function from R's corrplot package. This function offers extensive options for further data evaluation, so I highly recommend checking it out. I recently hosted an insightful webinar titled "Data Analysis & Visualization in R." In the session, we delved into various aspects of data analysis and visualization, including a demonstration on how to calculate and plot a correlation matrix using R programming. I’ve developed a mini-course based on this live webinar, where I offer the live session recording, exercises, solutions, and additional resources. More details are available at this link: https://lnkd.in/eVXD2x78 #dataviz #visualanalytics #bigdata #rprogramming #dataanalytics

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As data sets get larger, interpreting correlation matrices can become quite challenging. But fear not! A correlation matrix plot can come to the rescue, making it a breeze to understand the correlation structure in your data. Here's why it's awesome: 🔍 Visual Clarity: Instead of sifting through a sea of numbers, the plot provides a visual representation, making it easier to spot patterns and relationships at a glance. 📈 Intuitive Interpretation: With colors and shapes, it's simpler to grasp which variables are positively or negatively correlated, helping you make informed decisions. 🔄 Symmetry: The plot mirrors the correlation matrix, ensuring that you can quickly identify symmetric patterns without getting lost in rows and columns. 🌟 Efficiency: Save time and effort by leveraging this graphical tool to streamline your data analysis process, especially when dealing with large data sets. So, next time you're tackling a complex correlation matrix, consider creating a correlation matrix plot. Your eyes will thank you! Important note: Correlations can be influenced by factors such as sensitivity to outliers and the inability to handle non-linear relationships. Investigating these aspects is crucial. The graph below was created using the corrplot() function from R's corrplot package. This function offers extensive options for further data evaluation, so I highly recommend checking it out. I recently hosted an insightful webinar titled "Data Analysis & Visualization in R." In the session, we delved into various aspects of data analysis and visualization, including a demonstration on how to calculate and plot a correlation matrix using R programming. I’ve developed a mini-course based on this live webinar, where I offer the live session recording, exercises, solutions, and additional resources. For more information, visit this link: https://lnkd.in/eVXD2x78 #statistics #datastructure #datavisualization #datavisualization #dataanalytic #datasciencecourse #rstats

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Nitin Seth's book "Mastering the Data Paradox: Key to Winning in the AI Age" delves into the crucial role of data in the era of artificial intelligence and provides actionable insights for organizations to effectively leverage data and gain a competitive edge. Here are seven key lessons from the book: 1. Data as a Driving Force: The book highlights the emergence of a data-first world, where data has become a central driving force shaping industries and fueling innovation. It emphasizes the importance of recognizing the value and potential of data in driving business success 2. Navigating Data Paradoxes: Nitin Seth explores the complexities and paradoxes associated with data. Despite the abundance of data, actionable insights can be scarce. The book provides strategies and frameworks to navigate these paradoxes and effectively harness the power of data 3. Data as a Strategic Asset: Seth emphasizes the need to view data as a strategic asset. He explores how organizations can turn data into a valuable resource and leverage it to drive innovation, make informed decisions, and achieve success 4. The Role of Artificial Intelligence: The book discusses the dawn of the AI age and the role of artificial intelligence in leveraging data effectively. It explores the potential of AI technologies such as machine learning, predictive analytics, and IoT in driving innovation and staying ahead of the curve 5. Future Trends and Opportunities: Nitin Seth explores emerging trends and technologies that are shaping the future of data and AI. From advancements in machine learning to the rise of edge computing, the book identifies key trends and opportunities for organizations to capitalize on in the AI age 6. Data Literacy and Culture: The book emphasizes the importance of fostering a data-centric culture within organizations. It highlights the need for data literacy and the development of a data-driven mindset at all levels of an organization to effectively leverage data 7. The Unified Solution Framework: Nitin Seth proposes a thirteen-component solution framework called the Unified Solution Framework (USF) to help organizations succeed in the data-first world and the AI age. The framework provides a comprehensive approach to harnessing the power of data and driving business success These lessons from "Mastering the Data Paradox: Key to Winning in the AI Age" provide valuable insights into the pivotal role of data in the AI age and offer actionable strategies for organizations to effectively leverage data and gain a competitive edge. By understanding the complexities of data, embracing AI technologies, and fostering a data-driven culture, organizations can navigate the data paradox and harness the power of data for innovation and success.

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Data-driven decision-making demands robust statistical guarantees. With the widespread adoption of experimentation, the gaps in classical statistics have come to light. It is no longer adequate to simply gather data, perform a statistical test, and declare findings statistically significant based on a p-value. As Ron Kohavi has quoted, "Getting numbers is easy; getting numbers you can trust is hard." We at VWO have built a new stats engine that addresses four major needs of the experimentation market. - The ability to test with a diverse set of metrics and define crucial guardrail metrics that lead to auto-disabling of variations if impacted. - A Sequential Stats Engine that provides robust and tunable statistical guarantees with continuous monitoring of statistics. - Recommendations that help you stop a test early if it is unlikely to reach significance (saving visitor quota). - Background health checks that inform you if anything is wrong with the campaign. Join me for a fireside chat on the 29th of July, 11 am Eastern Time where we will discuss the evolution of VWO statistics and how online experimentation is transforming. https://lnkd.in/gWrEPBkR

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I am enjoying these customized donut charts by Shreya Arya. It is a reminder that combining charts with images is a great visual strategy to tell a story. It can make a graph much more memorable. https://lnkd.in/g25yzdrv #vizoftheday

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𝐂𝐀𝐓 𝟐𝟎𝟐𝟒 𝐌𝐚𝐡𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 | 𝐋𝐞𝐚𝐫𝐧 𝐰𝐡𝐢𝐜𝐡 𝐒𝐥𝐨𝐭 𝐰𝐚𝐬 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭 𝐚𝐜𝐜𝐨𝐫𝐝𝐢𝐧𝐠 𝐭𝐨 𝐃𝐀𝐓𝐀 !! - 𝐼 𝑎𝑚 𝑛𝑜𝑡 𝑎 𝑝𝑜𝑙𝑖𝑡𝑖𝑐𝑖𝑎𝑛 - 𝐾𝑖 𝐾𝑢𝑐ℎ 𝑏ℎ𝑖 𝐵𝑜𝑙 𝐷𝑢𝑛 , 𝑌𝑒𝑎 𝑘𝑜 𝐴𝑠𝑡𝑟𝑜𝑇𝑎𝑙𝑘 𝑘𝑎 𝐵𝑎𝑛𝑑𝑎 ℎ𝑢 - Ki predict Future - But I ask question to Data Why it's MAHA - ANALYSIS - It is not an analysis created inside the AC room without taking CAT. This is based on a data survey. https://lnkd.in/g96Na5gy #cat2024 #catanalysis #unacademy #iim #mba

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Logarithm is a magical transformation. Before Calculators, Complex Multiplication, Division, were done using pre-computed Log-AntiLog Tables. Remember carefully checking the mantisa and the characteristic from last pages of standard- 6th Maths-textbook ? It helped converting complex Multiplication and Division, to much simpler operations - Addition and Subtraction. And, Slide-rule was a popular mechanical-device with logarithmic-scale for computation before our era of electronic calculators. Its believed, the device was used extensively in the first Moon-landing mission. See IBM's advertisement (1951) pitching how IBM-calculator can do work of 150 engineers with Slide-rules! PS: If you are a Modeler you might have been maximizing 'Joint-LOG-Likelihood' and not Just 'Joint-Likelihood' function, Simplifying multiplicative objective function into additive one and efficiently solving for parameter-coefficients in GLM. Moreover, Log is like a log stick, which turns a notorious mathematical function to a well behaved one ! There is a reason sab-log love log 🤗 #computation #statistics #datascience #predictivemodel

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🌟 Data Science in Action: Real-World Success Stories 🌟 Data science is transforming industries with its powerful applications. Here are some highlights: **1. Healthcare:** Predictive analytics in heart disease prevention is saving lives and cutting costs by enabling early interventions. **2. Retail:** Companies like Amazon use data to personalize recommendations, boosting customer satisfaction and sales. **3. Finance:** Banks detect fraud in real-time using advanced analytics, protecting assets and maintaining trust. **4. Entertainment:** Netflix and Spotify keep users engaged with personalized content recommendations. **5. Agriculture:** Farmers optimize yields with predictive analytics, improving efficiency and sustainability. **6. Transportation:** Uber and Lyft enhance ride experiences by optimizing routes through data analysis. **Success Spotlight:** A healthcare provider reduced readmission rates by 20% using machine learning to predict and manage patient care needs. Data science is driving innovation and creating smarter solutions across sectors. Let's harness its potential for a brighter future! #DataScience #AI #MachineLearning #BigData #Innovation #SuccessStories --- Share your thoughts or success stories below! 🚀

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📊 Demystifying Principal Component Analysis (PCA) 🧠 Ever wondered how we can simplify complex datasets without losing much valuable information? 🤔 In my latest Medium article, I break down Principal Component Analysis (PCA) – a powerful technique in data science used for dimensionality reduction and pattern recognition. Whether you're a beginner or an experienced professional, this post will guide you through the core concepts, practical applications, and key takeaways of PCA. 🔗 Dive into the article here: https://lnkd.in/g9np-y2f #DataScience #MachineLearning #PCA #DimensionalityReduction #DataAnalysis #ArtificialIntelligence #LearningJourney

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Spartificial has published it's new Tableau Course on Udemy and it's available for FREE. A complete Tableau data analysis and visualization walkthrough with multiple projects and a fine touch of Astronomy. Check the link in comments to access the full course for free. #tableau #dataanalysis

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Question : Why does quoting just the accuracy numbers do not make sense for an AI model❓ Its a common practice among data science practitioners to quote accuracy of an #AI model. While it is easier to understand accuracy numbers, this practice can be misleading and fail to capture the nuances of real-world scenarios, leading to misaligned expectations among business stakeholders. 👉 Accuracy alone does not provide a comprehensive picture of a model's capabilities. It fails to account for crucial factors such as the size and distribution of the training, testing, and validation datasets, the presence of class imbalances, and potential biases. Relying solely on accuracy can mask underlying issues and lead to false confidence in the model's performance. 👉 To truly understand a model's effectiveness, it is essential to consider a broader range of metrics. Precision, recall and F1 score offer valuable insights into a model's ability to correctly identify positive and negative instances, handle imbalanced data, and discriminate between classes. 👉 However, even these technical metrics may not resonate with business stakeholders who are primarily concerned with the practical implications of the model's performance. It is crucial to translate these metrics into business-relevant terms that directly align with the product's objectives and success criteria. For example, in a fraud detection system, precision could be framed as the percentage of flagged transactions that are indeed fraudulent, while recall could represent the proportion of actual fraudulent transactions correctly identified. By presenting these metrics in a business context, stakeholders can better understand the trade-offs and make informed decisions about the model's deployment and ongoing monitoring. 💡 Quick Tip: Dont quote accuracy numbers without mentioning the size of training and testing dataset. Consider a diverse set of metrics tailored to your specific use case and business objectives. Communicate these metrics in a language that resonates with stakeholders, fostering a shared understanding and aligning expectations. #artificialintelligence #productmanagement #datascience #productmanager

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