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2190513_DS-ICE_2025s1
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# 2190513 Data Science (ICE) @CU (2025/1)  ## Syllabus: [Syllabus](https://mycourseville-default.s3.ap-southeast-1.amazonaws.com/useruploaded_course_files/2025_1/70559/materials/Syllabus_2190513_DS_ICE_2025s1_v2-7075-17600037520770.pdf) ## Code: ### Week01: Intro to Numpy, Pandas 1. Pandas: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week01_Intro_Pandas/1_Pandas.ipynb) 2. Pandas with Youtube stat data: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week01_Intro_Pandas/2_Pandas_%28Dataset_Trending_YouTube_Video_Statistics%29.ipynb) 3. (Advanced) Pandas with Youtube stat data: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week01_Intro_Pandas/3_Advanced_Pandas_%28Dataset_Trending_YouTube_Video_Statistics%29.ipynb) ### Week02-03: Data Preparation 1. Impute Missing Value: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week02_DataPrep/Lab2_ImputeMissingValue_Pipeline.ipynb) 2. OneHotEncoder: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week02_DataPrep/Lab1_LoansDataSet.ipynb) 3. Outliers - Take Log: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week02_DataPrep/Lab4_Outliers_Titanic_Pipeline.ipynb) 4. Outliers - Remove them with Z-Score: [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week02_DataPrep/Lab5_Outliers_Diabetes_(optional).ipynb>) 5. Split Train/Test: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week02_DataPrep/Lab3_SplitTrainTest.ipynb) ### Week04-07: Traditional ML #### Week04: Tree-based model, Pipeline, Evaluation #### Week05: Regression #### Week06: kNN, GridSearch + Clustering #### Week07: NN 1. Decision Trees: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/1_Decision_Trees_Random_Forests_v3.ipynb) 2. Linear Regression: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/2_Linear_Regression_v2.ipynb) 3. Logistic Regression: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/3_Logistic_Regression_v2.ipynb) 4. Neural Network: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/4_Neural_Network_v3.ipynb) 5. K Nearest Neighbors (GridSearchCV): [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/5_K_Nearest_Neighbors_v2_update_09012025.ipynb) 6. SVM: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/6_Support_Vector_Machine_v2.ipynb) 7. Save and Load Model: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/7_Save_Load_Model_v2.ipynb) 8. K-Means: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/8_K_Means_Clustering_v2.ipynb) 9. Market-Basket Analysis: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/9_Market_Basket_Intro_v2.ipynb) 10. Scikit-learn pipeline: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week03_ML/10_Scikit_learn_Pipeline.ipynb) ### Week08: Intro to Deep Learning 1. Image classification with CNN (PyTorch Lightning): [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/1_Image_classification_CIFAR10_CNN(lightning).ipynb>) 2-1. Image classification with EfficientNetV2s (PyTorch Lightning): [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/2_Image_classification_Animal_EfficientNetV2(lightning).ipynb>) 2-2. Image classification with EfficientNetV2s (PyTorch Lightning) with TensorBoard: [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/2_2_Image_classification_Animal_EfficientNetV2(lightning)_TensorBoard.ipynb>) 2-3. Image classification with EfficientNetV2s (PyTorch Lightning) with Weights & Biases: [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/2_3_Image_classification_Animal_EfficientNetV2(lightning)_wandb.ipynb>) 2-4. Image classification with EfficientNetb0 (Load a Pretrained Model from Hugging Face) (PyTorch Lightning) with Weights & Biases: [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/2_4_Image_classification_Animal_EfficientNet(lightning)_wandb_HuggingFace.ipynb>) 3-1. Object detection with YOLOv8 (basic script): [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/3_2_Object_detection_VOCDetection_yolov8_basic.ipynb) 3-2. Object detection with YOLOv8 (custom dataset): [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/3_3_Object_detection_VOCDetection_yolov8_advanced.ipynb) 4. Semantic segmentation with deeplabv3 (PyTorch Lightning): [](https://raw.githubusercontent.com/pvateekul/2190513_DS-ICE_2025s1/main/<https:/colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/4_Semantic_segmentation_Camseq_deeplabv3_DataInGD(lightning).ipynb>) 5. Time series Forecasting: Stock Price: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week08_DL/5_Time_series_forecasting_DataInGD_update.ipynb) ### Week09: Webscraping 1. Basic Web Scraping: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/webscraping/1_basic_web_scraping.ipynb) 2. Wiki Scraping Example: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/webscraping/2_wiki_scraping_example.ipynb) 3. REST API Extraction: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/webscraping/3_REST_API_extraction.ipynb) 4. Selenium: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/webscraping/4_selenium.ipynb) ### Week10: Fast API 1-1. Simple: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/1_simple.ipynb) 1-2. Simple Request: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/1_simple_request.ipynb) 2-1. Path Param: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/2_path_param.ipynb) 2-2. Path Param Request: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/2_path_param_request.ipynb) 3-1. Post: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/3_post.ipynb) 3-2. Post Request: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/3_post_request.ipynb) 4-1. Put: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/4_put.ipynb) 4-2. Put Request: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week09/fastapi/4_put_request.ipynb) ### Week11: Visualization with Streamlit #### Streamlit Runner (for running files below in Colab) [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/streamlit_runner.ipynb) 1. Streamlit Layout: [🔗](https://github.com/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/1_streamlit_layout.py) 2. Streamlit Iris: [🔗](https://github.com/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/2_streamlit_iris.py) 3. Streamlit Gapminder: [🔗](https://github.com/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/3_streamlit_gapminder.py) 4. Streamlit Uber: [🔗](https://github.com/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/4_streamlit_uber.py) 5. Dash Histogram: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/5_dash_histograms.ipynb) 6. Dash Gapminder: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week10_Streamlit/6_dash_gapminder.ipynb) ### Week12: Advanced DL & Text Classification 1. Text Classification (TF-IDF) [`PyTorch`]: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week11_Text_Classification/5_2_tfidf_for_Sentiment_Analysis.ipynb) 2. Text Classification (BERT) [`PyTorch`]: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week11_Text_Classification/5_3_NLP_Transfer_Learning_for_Text_Classification_Using_Hugging_Face_Transformers_Trainer.ipynb#scrollTo=tQqHYsWVTV6D) 3. Text Classification (Phayathaibert) [`PyTorch Lightning`]: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week11_Text_Classification/5_4_Wongnai_Review_Rating_Classifiacation_(Pytorch_lightning).ipynb) 4. Multi-label Text Classification (microsoft/deberta-v3-small) [`PyTorch`]: [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week11_Text_Classification/5_5_Multi_label_Text_Classification.ipynb) ### Week13: Generative AI (Prompt Engineering, Monitoring, Agentic Workflow, RAG, Fine-tuning) 1. Basic API Call with LangChain [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_1_LLM_Basic_API_Call_LangChain.ipynb) 2. Basic Prompt Engineering [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_2_LLM_Prompt_Engineering_Basic_LangChain.ipynb) 3. Advanced Prompt Engineering [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_3_LLM_Prompt_Engineering_Advanced_LangChain.ipynb) 4. LangChain Playground and Tracking with LangSmith [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_4_LLM_LangChiain_Playground_Tracking_with_LangSmith.ipynb) 5. Basic LangGraph [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_5_LLM_Basic_LangGraph.ipynb) 6. RAG with LangChain and Agentic RAG with LangGraph [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_6_LLM_RAG_LangChain_LangGraph.ipynb) <!-- 7. Creating a Simple ReAct Agent using LangGraph [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_7_LLM_Create_a_ReAct_Agent.ipynb) 8. Fine-tuning a Local LLM (Typhoon-7B) [`Hugging Face`] [](https://colab.research.google.com/github/pvateekul/2190513_DS-ICE_2025s1/blob/main/code/Week12_GenerativeAI/7_8_LLM_Typhoon7b_Finetuning_Huggingface.ipynb) -->
