Shaowen Wang’s Post

🚀 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐈𝐔 𝐈𝐧𝐝𝐢𝐚𝐧𝐚𝐩𝐨𝐥𝐢𝐬 🎓 🎉 I am really happy to share my experience with the Insights on Computational Data Science Certificate from IU Indianapolis. This has really enabled me to understand computational methods better and their practical application in solving real-world problems.🎉 The curriculum focused on: 💻 data modeling, 📊 algorithm development, 💡visualization techniques giving me hands-on skills to extract actionable insights from complex datasets.🔎 Besides the technical skills, it helped develop my analytical thinking and problem-solving skills to prepare me for the increasing demand for data-driven decision-making across industries.🌎 🚀 I look forward to applying these skills to deliver impactful solutions and contribute to advancements in the dynamic field of data science.🤖 #IUINDIANAPOLIS #DataScience #DataSkills #AlgorithmDevelopment #TechInnovation

I always enjoy spatial analysis and modeling—or just playing around with maps! 🗺️ This past long weekend, I dedicated some time to complete Esri's MOOC on "Spatial Data Science: The New Frontier in Analytics." 📊🚀 As someone with a background in spatial analysis and ArcGIS, it was a great refresher and a chance to further develop my spatial data science skills, especially using ArcGIS Pro. 💻🛰️ The course provided practical insights into applying spatial data science techniques, which are crucial for extracting meaningful patterns and trends from geospatial data. 🌍📊 I was really impressed with how the Pro version makes tasks like suitability modeling so much more efficient. ✅ The Deep Learning section was especially exciting for me and highlighted how spatial data science can unlock new analytical possibilities. 🧠💡 For the "communicating results" section, I had some fun creating a story map based on one of my recent PhD papers. 📝🌐 It's a great way to share research in a more engaging format—perfect if you're not a fan of traditional scientific publications. 📚 Feel free to check it out! I'd love to hear your thoughts. 😊 Link to the story map: https://arcg.is/1ryufz

A blog post about Specialist in Data science in Germany. This post selects 5 top University programs to choose for studying Masters in Data science. #datascience #engineering #Masters #SpecialistPathway #germany

Data Science is a valued and needed topic in Math Education. Virginia’s newly adopted SOL Math standards have a strong focus on Data Science. This article dives into how Utah is leading the nation in their work with Data Science. Check it out: https://lnkd.in/eXqAzz4D

Welcome to a new academic year! As part of a new ERAU course introducing students to academic research, some faculty were asked to briefly share how they perform research. Here's my brief video describing how I perform educational data science research. I hope this gives people insight into the new and exciting future of educational research!

25 Most Important Mathematical Definitions in Data Science 👇 Image Credit: Daily Dose if DS Explore the 25 most important mathematical definitions in data science with us! How many are you familiar with? While some terms like Gradient Descent and Normal Distribution might ring a bell, here are a few more to enrich your data science toolkit: • MLE • Z-score • OLS • Entropy • Eigen Vectors • R2 (R-squared) • KL divergence • SVD • Lagrange multipliers What other mathematical definitions would you add to this list? Share your thoughts below! 👇 And if you're eager to dive deeper into the math behind data science, check out these top-notch free courses from renowned universities and instructors: 🎓 Data Science Math Skills by Duke University 🔗 https://lnkd.in/dSpeUWUF 🎓 Math for Data science, Data analysis and Machine Learning by Sandeep Kumar Mathur 🔗https://lnkd.in/di4-NMH2 🎓 Algebra and Differential Calculus for Data Science by University of Colorado 🔗https://lnkd.in/dgVyXviv 🎓 Mathematics for Machine Learning by Imperial College London 🔗https://lnkd.in/dpUPG5e4 🎓 The Data Science Course: Complete Data Science Bootcamp by 365 Careers 🔗https://lnkd.in/dguwrQxr 🎓 Math for Data Science and Machine Learning by AD Chauhdry 🔗https://lnkd.in/dZBVhB9m 🎓 The Math of Data Science: Linear Algebra by RICE University 🔗https://lnkd.in/d2eeE5nf Enhance your skills today! #DataScience #Mathematics #LearningOpportunity

25 Most Important Mathematical Definitions in Data Science 👇 Image Credit: Daily Dose if DS Explore the 25 most important mathematical definitions in data science with us! How many are you familiar with? While some terms like Gradient Descent and Normal Distribution might ring a bell, here are a few more to enrich your data science toolkit: • MLE • Z-score • OLS • Entropy • Eigen Vectors • R2 (R-squared) • KL divergence • SVD • Lagrange multipliers What other mathematical definitions would you add to this list? Share your thoughts below! 👇 And if you're eager to dive deeper into the math behind data science, check out these top-notch free courses from renowned universities and instructors: 🎓 Data Science Math Skills by Duke University 🔗 https://lnkd.in/dSpeUWUF 🎓 Math for Data science, Data analysis and Machine Learning by Sandeep Kumar Mathur 🔗https://lnkd.in/di4-NMH2 🎓 Algebra and Differential Calculus for Data Science by University of Colorado 🔗https://lnkd.in/dgVyXviv 🎓 Mathematics for Machine Learning by Imperial College London 🔗https://lnkd.in/dpUPG5e4 🎓 The Data Science Course: Complete Data Science Bootcamp by 365 Careers 🔗https://lnkd.in/dguwrQxr 🎓 Math for Data Science and Machine Learning by AD Chauhdry 🔗https://lnkd.in/dZBVhB9m 🎓 The Math of Data Science: Linear Algebra by RICE University 🔗https://lnkd.in/d2eeE5nf Enhance your skills today! #DataScience #Mathematics #LearningOpportunity

It isn’t often that I see an NSF solicitation that starts writing your evaluation plan for you. In fact, this call for Data Science Education proposals provides you boatloads of resources for your data science curriculum development efforts too! Wait there’s more! The solicitation is blunt about PI’s need to embed meaningful ethics education into your plans. Yes!!!! Of course, you can’t just paste their sample evaluation questions into your proposal. To be more competitive, you should include context specific, activity and outcome driven evaluation questions. Perhaps include a Logic Model? A Logic Model, well developed, could be truly impactful for your proposal. Need help with your Logica Model and overall evaluation plan development? Ping me. https://lnkd.in/g_fWHSmP

Today, I successfully completed the "Data Science Math Skills" course offered by Duke University, led by Daniel Egger and Paul Bendich. This was the first course I completed under the scholarship awarded to me during the Frontier Technology Skills Scholarship Ceremony, organized by Futurenation and UNDP on December 12, 2024, via Coursera. The career I selected for the scholarship was Data Scientist, and I was awarded many courses related to data science. This course covered essential mathematical concepts for data science, including: -Building Blocks for Problem Solving - Functions and Graphs - Measuring Rates of Change -Introduction to Probability Theory It was a valuable learning experience that has enhanced my understanding of fundamental mathematics crucial for data science. I’m excited to apply this knowledge to future projects and continue exploring advanced concepts in upcoming courses under this scholarship program.

My co-authors, Helen Burn, Ben Baumer, Jason Black, PhD, Zarek Drozda, Debzani Deb, and I wrote a recently published article which explores diverse pathways to data science for various groups, including high school students, those in two-year colleges, and students in small colleges and minority-serving institutions. The article identifies opportunities and challenges associated with pursuing data science for students in these sectors, including factors such as building partnerships and awareness of the field and difficulties in establishing articulation agreements between institutions. Special shout out to ADSA, and in particular, Micaela Parker, for providing the platform for us to collaborate on this paper! We hope you find our paper valuable as you consider building diverse pathways to data science! https://lnkd.in/eHjfrKDU #datascience #MSI #2YC