Applied Data Science with R - Khoa học dữ liệu ứng dụng với R (DSR301m)
Vietnamese title: Khoa học dữ liệu ứng dụng với R
Credits: 3
Prerequisites: DBI202

Description:
This course introduces the basics of the R language such as data types, techniques for manipulation. Students will be able to perform basic R programming tasks to complete the data analysis process, create relational databases and query the data using SQL and R and communicate their data findings using data visualization techniques. Students will work with tools like R Studio and related R libraries for data science such as Tidyverse, Tidymodels, R Shiny, ggplot2.

Learning outcomes:
Understand the basics of the R language
Understand how manipulate data with R programming language and implement fundamental programming tasks
Learn and apply foundational knowledge of the SQL &R languages, and how to access databases from Jupyter notebooks using SQL and R
Know how to wrangle data for data analysis, develop models, improve models, evaluate and tune the performance
Understand how to describe and build graphs using R packages. Implement various types of basic charts to visualize like bar charts, histograms, pie charts, scatter plots, line plots, and box plots
Implement a small data science project includes steps: data wrangling, data analysis, data visualization, model development, model evaluation, and reporting

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU LMS at http://lms.fpt.edu.vn for up-to-date course information

Tools:
Jupyter Notebooks, RStudio IDE

Materials:
description: Introduction to R Programming for Data Science
publisher: Coursera
note: https://www.coursera.org/learn/introducton-r-programming-data-science
description: SQL for Data Science with R
publisher: Coursera
note: https://www.coursera.org/learn/sql-data-science-r
description: Data Analysis with R
publisher: Coursera
note: https://www.coursera.org/learn/data-analysis-with-r?specialization=applied-data-science-r
description: Data Visualization with R
publisher: Coursera
note: https://www.coursera.org/learn/data-visualization-r?specialization=applied-data-science-r

Assessments:
category: Assignment
type: on-going
weight: 30.0
duration: 8-10 weeks
question_type: CLO6
no_question: Mini project
knowledge_and_skill: 1
grading_guide: - cover chapter 1, 2, 3, 4
note: at home
category: Lab 1
type: on-going
weight: 5.0
duration: 30'
question_type: CLO1, CLO2
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 1
note: in class, by instructor
category: Lab 2
type: on-going
weight: 5.0
duration: 30'
question_type: CLO3
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 2
note: in class, by instructor
category: Lab 3
type: on-going
weight: 5.0
duration: 30'
question_type: CLO4
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 3
note: in class, by instructor
category: Lab 4
type: on-going
weight: 5.0
duration: 30'
question_type: CLO5
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 4
note: in class, by instructor
category: Progress test 1
type: on-going
weight: 10.0
duration: 45'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 1,2
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 10.0
duration: 45'
question_type: CLO1, CLO4, CLO5
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 3,4
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: - cover chapter 1, 2, 3, 4
note: by exam board,
using computer

Schedule:
session: - Course Introduction
- Chapter 1:
Introduction to R Programming for Data Science
learning_outcomes: I
materials: mat1
session: 1.1.Introduction to R Language 1
learning_outcomes: T
materials: mat2
session: 1.2.Introduction to R Language 2
learning_outcomes: T
materials: drive.google.com
session: 1.3 Getting Data into R 1
learning_outcomes: T
materials: drive.google.com
session: 1.4 Getting Data into R 2
learning_outcomes: T
materials: drive.google.com
session: 1.5 Getting Data into R 3
learning_outcomes: T
materials: drive.google.com
session: 1.6 Getting Data into R 4
learning_outcomes: T
materials: drive.google.com
session: 1.7 Getting Data into R 5
learning_outcomes: T
materials: drive.google.com
session: 1.8 R Programming Fundamentals 1
learning_outcomes: T
materials: drive.google.com
session: 1.9 R Programming Fundamentals 2
learning_outcomes: T
materials: drive.google.com
session: Lab 1a: Introduction to R Programming
learning_outcomes: TU
materials: drive.google.com
session: Lab 1b: Introduction to R Programming
learning_outcomes: TU
materials: drive.google.com
session: Lab 1c: Introduction to R Programming
learning_outcomes: TU
materials: drive.google.com
session: Guiding assignment
learning_outcomes: U
session: - Chapter 2: SQL for Data Science with R

2.1 Remind: Relational Databases and Tables
learning_outcomes: T
session: 2.2 Remind: Intermediate SQL
learning_outcomes: T
session: 2.3 Getting Started with Databases using R 1
learning_outcomes: T
materials: drive.google.com
session: 2.4 Getting Started with Databases using R 2
learning_outcomes: T
materials: drive.google.com
session: 2.5 Working with Database Objects using R 1
learning_outcomes: T
materials: drive.google.com
session: 2.6 Working with Database Objects using R 2
learning_outcomes: T
session: 2.7 Working with Database Objects using R 3
learning_outcomes: T
session: Lab 2a: SQL with R
learning_outcomes: TU
materials: drive.google.com
session: Lab 2b: SQL with R
learning_outcomes: TU
materials: drive.google.com
session: Lab 2c: SQL with R
learning_outcomes: TU
materials: drive.google.com
session: Lab 2d: SQL with R
learning_outcomes: TU
materials: drive.google.com
session: Review assignment
learning_outcomes: U
session: Progress Test 1
learning_outcomes: U
session: Chapter 3: Data Analysis with R
3.1 Introduction to Data Analysis with R 1
learning_outcomes: T
materials: drive.google.com
session: 3.2 Introduction to Data Analysis with R 2
learning_outcomes: T
materials: drive.google.com
session: 3.3 Data Wrangling 1
learning_outcomes: T
materials: drive.google.com
session: 3.4 Data Wrangling 2
learning_outcomes: T
materials: drive.google.com
session: 3.5 Exploratory Data Analysis 1
learning_outcomes: T
materials: drive.google.com
session: 3.6 Exploratory Data Analysis 2
learning_outcomes: T
materials: drive.google.com
session: 3.7 Model Development in R 1
learning_outcomes: T
materials: drive.google.com
session: 3.8 Model Development in R 2
learning_outcomes: T
materials: drive.google.com
session: 3.9 Model Development in R 3
learning_outcomes: T
materials: drive.google.com
session: 3.10 Model Evaluation
learning_outcomes: T
materials: drive.google.com
session: 3.11 Model Evaluation
learning_outcomes: T
materials: drive.google.com
session: Lab 3a: Data Analysis with R 1
learning_outcomes: TU
materials: drive.google.com
session: Lab 3b: Data Analysis with R 2
learning_outcomes: TU
materials: drive.google.com
session: Lab 3c: Data Analysis with R 3
learning_outcomes: TU
materials: drive.google.com
session: Lab 3d: Data Analysis with R 4
learning_outcomes: TU
materials: drive.google.com
session: Lab 3e: Data Analysis with R 5
learning_outcomes: TU
materials: drive.google.com
session: Review the mini Capstone
learning_outcomes: U
materials: FLM and Coursera
session: Chapter 4: Data Visualization with R
4.1 Introduction to Data Visualization
learning_outcomes: T
materials: drive.google.com
session: 4.2 Basic Plots, Maps, and Customization 1
learning_outcomes: T
materials: drive.google.com
session: 4.3 Basic Plots, Maps, and Customization 2
learning_outcomes: T
materials: drive.google.com
session: 4.4 Basic Plots, Maps, and Customization 3
learning_outcomes: T
materials: drive.google.com
session: 4.5 Dashboards 1
learning_outcomes: T
materials: drive.google.com
session: 4.6 Dashboards 2
learning_outcomes: T
materials: drive.google.com
session: 4.7 Dashboards 3
learning_outcomes: T
materials: drive.google.com
session: Lab 4a: Data Visualization with R 1
learning_outcomes: TU
materials: drive.google.com
session: Lab 4b: Data Visualization with R 2
learning_outcomes: TU
materials: drive.google.com
session: Lab 4c: Data Visualization with R 3
learning_outcomes: TU
materials: drive.google.com
session: Progress Test 2
learning_outcomes: U
session: Grading assignment
learning_outcomes: U
materials: drive.google.com
session: Grading assignment
learning_outcomes: U
materials: drive.google.com
session: Grading assignment
learning_outcomes: U
materials: drive.google.com
session: Review course
learning_outcomes: I
materials: drive.google.com
session: Review course
Results
learning_outcomes: I
materials: drive.google.com