Data Filtering dengan Rapidminer - Seri Perkuliahan Data Analytic & Data Mining #06

[@CimolOk-nz5yj]

🎯 Key Takeaways for quick navigation: 00:00 📚 *Introduction to Data Engineering and Dataset Import* - Introduction to the topic and dataset import process. - Explanation of importing a CSV dataset. - Initial dataset configuration and type setting for columns. 02:16 🧹 *Cleaning Data and Handling Missing Values* - Importance of cleaning data in analytics and mining. - Techniques to handle missing values. - Demonstration of removing rows with missing values. 04:30 🔄 *Basic Data Filtering Techniques* - Overview of basic data filtering techniques. - Explanation of the simplest method to remove missing values. - Introduction to the concept of data imputation. 06:04 🎛️ *Advanced Filtering: Brand Selection* - Advanced filtering techniques using brand as a criterion. - Specific example of filtering data for the Samsung brand. - Explanation of filtering conditions and parameters. 09:00 🔍 *Filtering Multiple Brands and Understanding Logical Operators* - Process of filtering multiple brands (Samsung and Apple). - Understanding the use of logical operators in filtering. - Explanation of 'match all' and 'match any' concepts in data filtering. 12:16 📱 *Narrowing Down to Specific Categories* - Techniques to filter data by specific categories. - Example of filtering smartphone products from Samsung and Apple. - Discussion on the importance of accurate category filtering. 16:17 💰 *Filtering Based on Price Range* - Method for filtering data based on price criteria. - Demonstration of setting up a filter for products above a certain price. - Explanation of the importance of specific filter criteria. 22:16 ⚙️ *Combining Multiple Filters and Troubleshooting* - Combining multiple filters for detailed data analysis. - Troubleshooting issues when combining different types of filters. - Understanding the impact of filter combinations on data results. 27:30 🎓 *Conclusion and Preview of Upcoming Topics* - Conclusion of the current session on data filtering. - Preview of upcoming topics in data engineering. - Encouragement to continue learning and exploring data analytics. Made with HARPA AI

[@EksekutorBiasa]

Terima Kasih Pak Dosen. Jadi amal jariah, sehat sejahtera selalu Pak . aamiin

[@KuliahInformatika]

Terima kasih utk doanya. Aamiin 🤲

[@irfanwidiantoro9039]

izin bertanya pak, kalau tools" semacam rapidminer, knime sudah sepowefull ini jadi mubazir ya pak kalau belajar pandas, seaborn,matplotlib soalnya perlu coding,dll....

[@KuliahInformatika]

pertanyaan bagus. sebetulnya masing2 sudah punya lingkup penggunaannya sendiri2. aplikasi seperti rapidminer dkk, cenderung dipakai untuk menganalisis data secara praktis tanpa melakukan inovasi metode atau semacamnya. sedangkan dengan python, kita bisa fleksibel untuk memodifikasi algoritma atau model machine learning. BTW, untuk visualisasi data, python masih lebih unggul daripada rapidminer

[@irfanwidiantoro9039]

iziin bertanya lagi pak, didunia kerja kebanyakan praktisi data science/analyst itu kebanyakan memanipulasi data melalui python atau menggunakan tools semacam rapidminer ya? atau mungkin kombinasi keduanya? untuk manipulasi menggunakan rapidminer visualiasasinya menggunakan python? soalnya kalau manipulasi pakai pandas ribet paa,,fungsi"nya banyak@@KuliahInformatika

[@usyfirdausy5991]

Terima kasih Pak Dosen, saya suka sekali tutorialnya, tempo pas, penjelasan mudah dimengerti, gambar bagus, alur terstruktur. Barakallaah Pak Dosen, sehat terus berkarya terus. 🥰

[@KuliahInformatika]

Aamiin.. Terima kasih banyak ya 😊

[@atsiripedia972]

Terima kasih Pak, Ilmunya sangat bermanfaat. saya lagi belajar menggunakan KNIME ternyata ada rapidminer yang memiliki tutorial yang jelas dan lengkap. Mohon bantu bahas tentang knime Pak. Terima kasih dan salam.