Ishango.ai’s Post

Fundamentals of Data Science

🌟 Week 9 Progress Update: Data Science Bootcamp Journey at Digital Skola! 🌟 This week, I've dived deep into various aspects of data science, enhancing my understanding of both data preprocessing and supervised learning algorithms. Here's a quick look at what I've learned in Week 9 under the guidance of our tutor, Faiq Miftakhul Falakh, Rizki Fajar Nugroho & Aries Fitriawan: 🧹Data Preprocessing II - Explored how to handle Text Data and preprocess it for better model performance. - Learned techniques to manage Imbalance Datasets to ensure fair and accurate predictions. 📊Supervised Learning I - Classification: Mastered the basics and importance of classifying data. - K-Nearest Neighbor: Learned how to use proximity to predict outcomes. - Decision Tree & Entropy: Delved into decision-making processes in models. - Random Forest & Regression: Explored the power of ensemble methods for robust predictions. - Evaluation Metrics: Understood how to measure model performance effectively. 🔍Supervised Learning II - Algoritma SVM (Part 1 & Part 2): Learned about Support Vector Machines and their applications in classification tasks. - Ensemble Learning: Studied how combining multiple models can lead to better performance. - Multiclass Classification: Explored techniques to classify data into multiple categories. Excited to keep pushing forward and building my data science expertise! 🚀 Jeremy Aryawan Putra, Ahmad Luqman El Fachruddin, Muhammad Farhan Ilhamy, Naufal Daffa Pramudya #DigitalSkola #LearningProgressReview #DataScience

Week 8 in Data Science: Unlocking the Power of Regression & Model Deployment 🚀🌟 👩🏻💻 Holla, Linked In! 🔵 I’m thrilled to share the latest update from my journey at Digital Skola 🏫 as part of the Data Scientist Bootcamp. This week, we’ve been diving into the intriguing areas of Text Data Handling and Supervised Learning, both of which are crucial tools for any data scientist 🔑. 📗 Text Data Handling We’ve explored various topics such as text data processing, tokenization, stop word removal, stemming, lemmatization, and feature engineering. These skills are key to preparing and transforming text data for analysis and were explained thoroughly by Mr. Faiq Miftakhul Falakh 👨🏻💻. 📕 Supervised Learning In addition, we’ve started learning about supervised learning, concentrating on classification techniques like K-Nearest Neighbor (KNN), Decision Trees, Entropy, Random Forest, and Support Vector Machines (SVM). We’ve also touched on ensemble learning, multiclass classification, and evaluation metrics. This section was led by kak Rizki Fajar Nugroho 👨⚖️ and Kak Aries Fitriawan 👨⚖️ . As always, our bald eagle team, Muhammad Faros Arkan, Savithri Maurizki Delya, Raden Wisnu Murti Sulaindra, Cheriel Leily, and Muhammad Andrean has compiled a comprehensive overview of these topics in the slides. Keep an eye out for upcoming content and updates! 👨🎓 Class representative: Kak Zhiffa #DigitalSkola #DataScientist #LearningProgressReview

I’m thrilled to announce that I’ve completed an in-depth Data Science course! This journey has equipped me with valuable skills in data analysis, machine learning, and big data technologies. From mastering data visualization to building predictive models and handling large datasets, this course has been a transformative experience. I'm eager to apply these skills to solve real-world problems and contribute to data-driven decision-making.

This Week's Data Science Journey at Digital Skola🚀 This week, our team dove deep into the foundations of data science and explored the fascinating methodologies behind it. It’s been an exciting ride! 🎉 Here’s what we discovered: 1️⃣ Data Science Fundamentals 📚 We explored the three main pillars of data science that form a solid foundation in this field:  - Mathematics/Statistics for analysis and pattern recognition. 📊   - Computer Science as tools and algorithms. 💻   - Domain Knowledge to make data more relevant and impactful. 🌟   These three pillars are like the triangle of power that will continue guiding us as we explore the world of data science.  2️⃣ Data Processing 🔄 The process was incredibly exciting! From data collection, data cleaning, to building predictive algorithms, each step transforms raw data into valuable insights for decision-making, scoring, and recommendations. It felt like solving a puzzle, piecing together fragments of information! 🧩  3️⃣ Career Insights 💡 Seeing how data science is applied in real-world scenarios—like fraud detection, forecasting, and personalized recommendations—broadened our understanding of its importance and the vast career opportunities it offers. 🌍 The highlight of this week was our first encounter with SQL, a powerful language for managing structured data. 📑 As beginners, this experience opened our eyes to the importance of efficient data management in various industries. It felt like unlocking a whole new world! 🚪✨  Curious to learn more about our journey? Check out the slide deck we’ve created on this topic! 🖥 Ahmad Faiz Armiano Syah, Khoriana ., Mahmud Khasbunal Kafi #DigitalSkola #LearningProgressReview #ProfessionalBranding #DataScience #Batch45 #GradiatorsTeam

Data Science Foundations

Data Science Foundations

I am proud to announce that I have completed the "Data Science and Machine Learning: Making Data-Driven Decisions" program at the Massachusetts Institute of Technology's Institute for Data, Systems, and Society (MIT IDSS). This intensive 12-week online course, which spanned the summer and continued into the start of my fall semester at James Madison University, has significantly enhanced my analytical capabilities. It perfectly complemented my Economics studies, blending advanced data science with economic theory. Key achievements: -Mastered Python for Data Science, enhancing skills in libraries like NumPy and Pandas. -Completed in-depth projects, including a FIFA World Cup Analysis and a comprehensive assessment of the MovieLens dataset. -Applied descriptive and inferential statistics to analyze real-world datasets. -Engaged in over 30 hours of lectures and hands-on projects guided by MIT faculty and industry leaders. This program has sharpened my Python skills and equipped me to tackle complex, data-driven challenges in economic and financial contexts, merging theoretical knowledge with practical application.