Day 1 (Instructor: Asst.Prof.Dr.Chanwit Boonchuay)

Data science backgrounds

• What is data science and data process?

• Why corporate data scientist?

• Corporate data science tasks

• Data types and formats

• Computational thinking

• Statistics for data science

Exploratory data analysis(EDA)

• Typical data format and types of EDA

• Univariate non-graphical and graphical EDA

• Multivariate non-graphical and graphical EDA

Machine learning essentials

• Types and categories of ML

• Regression modeling

• Classification modeling

• Clustering & Retrieval modeling

Day 2 (Instructor: Gling Kanchanasuwa)

Data preparation and exploration

• Step-by-step for data preparation and exploration using python (jupyter notebook)

• Business case examples for data preparation and exploration

• Workshop on data preparation and exploration with real-world data sets

Regression and classification models

• Step-by-step for regression and classification models

• Business case examples for regression and time series forecasting models

• Workshop on data analytics with real-world data sets

Day 3 (Instructor: Gling Kanchanasuwa)

Time series forecasting and clustering models

• Step-by-step for time series forecasting and clustering models

• Business case for classification and clustering models

• • Workshop on data analytics with real-world data sets

Practical examination

Based on given data sets, participants have to finish the assignment as

• Removing outliners in the data set

• Creating a predictive model

• Visualization and interpretation of results