2110446 Data Science Course at Chula 2022
Short links for exercises:
Week1: Intro to Numpy, Pandas
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Numpy:
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Pandas:
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Pandas with Youtube stat data:
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(Advanced) Pandas with Youtube stat data:
Assignment (Pandas with Youtube stat data):
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EDA:
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Impute Missing Value:
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Split Train/Test:
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Outliers with Log:
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Outliers with Log (Titanic DataSet):
Assignment:
Week3: Statistical Analysis
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Basic Stat:
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Intermediate Stat:
Assignment (Stat):
Assignment:
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Decision Trees:
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Linear Regression:
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Logistic Regression:
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Neural Network:
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K Nearest Neighbors:
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SVM:
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Save and Load Model:
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K-Means:
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Market-Basket Analysis:
Assignment (Safe to eat or deadly poison?):
Week6: Intro to Deep Learning
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Image classification (basic): flower classification
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Image classification (advanced): flower classification
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Semantic Segmentation (UNET): The Oxford-IIIT pet dataset
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LSTM: Stock price prediction
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SARIMAX: PM2.5 forecasting
Assignment (Fashion MNIST):
Week7: Data Extraction
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Basic Webpage Scraping:
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Wikipedia Page Data Extraction:
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REST API Data Extraction:
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Twitter Data Extraction:
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Selenium:
Assignment:
All codes and scripts are here: this link
- Kafka Sample Producer:
- Kafka Sample Consumer:
- Kafka Sample Producer with AVRO:
- Kafka Sample Consumer with AVRO:
- Sensor FileWriter Consumer:
- Sensor Counter Consumer:
Data Set:
Sample (ASVC):
Anyway, you can download all source codes for week11_airflow through this link (week8_dataingestion.zip).
- Basic Spark:
- Spark SQL:
- Spark ML:
Data Set:
- Bank:
- Star Wars:
- Basic Spark Streaming:
- Spark Streaming Window Operations:
- Basic Structured Streaming:
- Structured Streaming Window Operations:
- Structured Streaming and Kafka:
Data Set:
Star Wars:
All code is here: this link
Anyway, you can download all source codes for week11_airflow_and_fastapi through this link (week11_airflow_and_fastapi.zip).
** Updated python codes/notebooks will be posted here shortly before each lecture.
- https://www.kaggle.com/code
- https://www.tensorflow.org/tutorials
- https://github.com/topics/machine-learning
- https://archive.ics.uci.edu/ml/datasets.php
- https://colab.research.google.com/notebooks/