Vignan's University’s Post

As 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 continues its rise 📈 in popularity as a 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘷𝘦 and 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘷𝘦 field, let's learn about the giants that paved the way and their contributions that revolutionized the field! In this new addition to Data Bytes, let's learn about American Statistician and Mathematician 𝗝𝗼𝗵𝗻 𝗧𝘂𝗸𝗲𝘆 (1915-2000) and his major contributions to data science and statistics 📊 Content and Layout by: Franczine Tan #UPDataScienceSociety #PoweredbyDataDrivenPeople

🚀 Day 122 in my data science journey! Today's highlights: 🔍 Understanding Covariance: Interpretation and calculation. ⚠️ Disadvantages: Limitations explored. 🔄 Covariance with Itself: Demystified! 🔍 Purpose of Correlation: Solving relationship problems. 📈 Exploring Correlation: Definition and differences from covariance. 🔍⚖️ Correlation vs. Causation: Important distinction! 📊 Visualizing Multiple Variables: Insights unlocked! Excited for more discoveries! 💡 #DataScience #LearningJourney 📚

Data science students recently showcased their incredible creativity and skills in a class on data visualization techniques. 📊🖼️ This edition's theme was all about food, and their prepared posters were remarkable! 🍔🌮🍕🍦 From exploring the impact of food on the environment to analyzing the relationship between food and sports performance. Are you curious to see what they have prepared? Check out the posters in the attached image! 👀🖼️ I thank the teachers who conducted this course at the Warsaw University of Technology with me: Mateusz Krzyziński, Mikołaj Spytek, and Katarzyna Woźnica. Your guidance and expertise have been invaluable in shaping the skills and creativity of these students. As we wrap up this edition, we can't help but wonder: What should be the theme for the next poster showcase? Share your ideas in the comments below! 💡✨ More posters can be found on the blog: https://lnkd.in/dsg6_FbT #datascience #datavisualization

📊 Day 3 Data Science Challenge: Exploring Discrete Probability Distributions Day 3 of the Data Science challenge delved into discrete probability distributions, focusing specifically on the Bernoulli and Binomial distributions. The Bernoulli distribution models the probability of success or failure in a single experiment, with only two possible outcomes. On the other hand, the Binomial distribution extends this concept to multiple independent Bernoulli trials, where each trial has the same probability of success. By understanding these distributions, we gain insights into the likelihood of various outcomes in discrete scenarios, laying the groundwork for more sophisticated statistical analysis and modeling. 🎲📈 #DataScience #ProbabilityDistributions 🧮📚

Congratulations to our newest Data Science Specialists! 🌟🎉 As you step out into the world of data science, remember that your learning doesn't end here. The field is ever-evolving, and your ability to adapt and learn continuously will be your greatest asset. Stay curious, keep pushing boundaries, and embrace the challenges that lie ahead. Once again, congratulations on your remarkable achievement! PCC is proud of you!🎓🌐 #PhilippineCodingCamp #ProgrammingOurFuture #DataScience #BootCamp #CareerGrowth #CareerAdvancement

Top #R packages for Missing data (diagnostics and imputation) - The list curated by me. As discussed i will be publishing tomorrow a selected list of top R packages for dealing with missing data, based on several years of experience. Missing data diagnostics / identifying MAR, MNAR, MCAR but also missing data imputation starting from simpler MI approaches to more complex ones. These topics were criteria for the selection i made. The list will be published on my 'Advanced Stats and Data Science' LinkedIn Newsletter, and will also be featured on my timeline 2nd August (tomorrow). #rstats #datascience #biostatistics #stats #rprogramming #packages #data #scientist

Hey LinkedIn crew! Day 14 of the 100 Days of Data Science Challenge brought a deep dive into the assumptions of linear regression! 📊 Today's Learnings: - Explored key assumptions like linearity, ensuring the relationship between variables is accurately represented. - Delved into homoscedasticity, checking for consistent variance in residuals across all levels of predictor variables. - Examined the normality of residuals, ensuring a Gaussian distribution for unbiased results. - Studied multicollinearity, understanding its implications and exploring the math behind it. - Explored types of multicollinearity, calculated Variance Inflation Factors (VIF), and learned strategies to remove multicollinearity. 💡 Key Takeaways: Mastering assumptions is crucial for building reliable linear regression models. Multicollinearity, with its nuances, adds a layer of complexity that can be managed with VIF and strategic removal techniques. 🚀 What's Next: Tomorrow, diving into Ridge Regression—a powerful approach to handle multicollinearity and enhance model stability. #100DaysOfDataScience #LinearRegression #Assumptions 📈 Let's keep this data-driven adventure rolling! P.S. If you're on your own data journey, share your thoughts below! 🌱🤝

Don't let misconceptions hold you back. Discover the truth behind common myths and facts about the data science field. ZELL will take you to the reality! #datascience #datascientistjob #zelearninglabbinstitute #phddatascience #mythsvsfacts #datasciencemyths #datasciencecourse #zell #learninglabb #learninglab #datasciencefacts #mythsandreality

Diving deep into dataframe and statistics has been a game-changer! From unraveling the complexities of data manipulation to conquering statistical analysis, this journey has truly elevated my data science prowess. Thrilled to grow with #DigitalSkola and can't wait to bring these skills to life in real-world projects. 🚀✨ #DigitalSkola #LearningProgress #DataScience

🚀 𝐁𝐞𝐬𝐭 𝐛𝐨𝐨𝐤𝐬 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 🚀 📚 Here are some resources for those diving into the world of data science : 1️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡: 𝐅𝐢𝐫𝐬𝐭 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 by Joel Grus 2️⃣ 𝐓𝐡𝐢𝐧𝐤 𝐒𝐭𝐚𝐭𝐬 by Allen B. Downey 3️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 by Wes McKinney 4️⃣ 𝐀𝐧 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐑 by Gareth James 5️⃣ 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐜𝐢𝐤𝐢𝐭-𝐋𝐞𝐚𝐫𝐧, 𝐊𝐞𝐫𝐚𝐬 & 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 by Aurélien Géron 6️⃣ 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 by François Cholle 7️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐇𝐚𝐧𝐝𝐛𝐨𝐨𝐤: 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 by Jake VanderPlas 8️⃣ 𝐓𝐡𝐞 𝐇𝐮𝐧𝐝𝐫𝐞𝐝-𝐏𝐚𝐠𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐁𝐨𝐨𝐤 by Andriy Burkov 🔗 For those eager to kickstart their learning journey, send me a message, and I'll gladly share the Google Drive link that contains the PDF version of these books with you! #datascience #learningjourney