2110446 Data Science Course at Chula 2022

Short links for exercises:

Week1: Intro to Numpy, Pandas

1. Numpy:

2. Pandas:

3. Pandas with Youtube stat data:

4. (Advanced) Pandas with Youtube stat data:

Assignment (Pandas with Youtube stat data):

Week2: Data Preparation

1. EDA:

2. Impute Missing Value:

3. Split Train/Test:

4. Outliers with Log:

5. Outliers with Log (Titanic DataSet):

Assignment:

Week3: Statistical Analysis

1. Basic Stat:

2. Intermediate Stat:

Assignment (Stat):

Week4: Regression

Assignment:

Week5: Traditional ML

1. Decision Trees:

2. Linear Regression:

3. Logistic Regression:

4. Neural Network:

5. K Nearest Neighbors:

6. SVM:

7. Save and Load Model:

8. K-Means:

9. Market-Basket Analysis:

Assignment (Safe to eat or deadly poison?):

Week6: Intro to Deep Learning

1. Image classification (basic): flower classification

2. Image classification (advanced): flower classification

3. Semantic Segmentation (UNET): The Oxford-IIIT pet dataset

4. LSTM: Stock price prediction

5. SARIMAX: PM2.5 forecasting

Assignment (Fashion MNIST):

Week7: Data Extraction

1. Basic Webpage Scraping:

2. Wikipedia Page Data Extraction:

3. REST API Data Extraction:

4. Twitter Data Extraction:

5. Selenium:

Assignment:

Week8: Data Ingestion

All codes and scripts are here: this link

1. Kafka Sample Producer:
2. Kafka Sample Consumer:
3. Kafka Sample Producer with AVRO:
4. Kafka Sample Consumer with AVRO:
5. Sensor FileWriter Consumer:
6. Sensor Counter Consumer:

Data Set:

Sample (ASVC):

Anyway, you can download all source codes for week11_airflow through this link (week8_dataingestion.zip).

Week9: Spark

1. Basic Spark:
2. Spark SQL:
3. Spark ML:

Data Set:

1. Bank:
2. Star Wars:

Week10: Spark Streaming

1. Basic Spark Streaming:
2. Spark Streaming Window Operations:
3. Basic Structured Streaming:
4. Structured Streaming Window Operations:
5. Structured Streaming and Kafka:

Data Set:

Star Wars:

Week11: Airflow

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.

Reference:

1. https://www.kaggle.com/code
2. https://www.tensorflow.org/tutorials
3. https://github.com/topics/machine-learning
4. https://archive.ics.uci.edu/ml/datasets.php
5. https://colab.research.google.com/notebooks/