AI, Data Science with Python & SQL (ADY201m)
Vietnamese title: TTNT và KHDL với Python và SQL
Credits: 3
Prerequisites: PFP191, DBI202

Description:
The course covers relational database concepts, foundational knowledge of the SQL language. The students will learn some popular tools for data science, understand a methodology that can be used within data science, meet some data science practitioner, understand data gathering, summarize data using descriptive statistics, display and visualize data.

Learning outcomes:
Understand relational database concepts, and foundational knowledge of the SQL language.
Define an overview of what data science is today.
Know tools for Data Science, know how to use at least one tool for Data Science
Understand why we are interested in data science, what a methodology is, and why data scientists need a methodology.
Discusses Python fundamentals, python libraries, API suport for data science
Explain the statistics for Data Science: mean, median, mode, variance, and standard deviation, data visualization, regression analysis
Implement the small data science project by applying Python fundamentals, and Python data structures, and working with data in Python.

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 FLM at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
Jupyter Notebooks
- JupyterLab
- RStudio IDE
- Git, GitHub
-Watson Studio.

Materials:
description: What is Data Science?
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/what-is-datascience?specialization=ibm-data-science
description: Data Science Methodology
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/data-science-methodology?specialization=ibm-data-science
description: Databases and SQL for Data Science with Python
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/sql-data-science
description: Tools for Data Science
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/open-source-tools-for-data-science?specialization=ibm-data-science
description: Python for Data Science, AI & Development
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/python-for-applied-data-science-ai?specialization=ibm-data-science
description: Statistics for Data Science with Python
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/statistics-for-data-science-python
description: Python Project for Data Science
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/python-project-for-data-science?specialization=ibm-data-science
description: GP1-Data Manipulation with dplyr in R
author: Coursera
publisher: Coursera
note: https://www.coursera.org/projects/data-manipulation-with-dplyr-in-r
description: GP2-Scikit-Learn to Solve Regression Machine Learning Problems
author: Coursera
publisher: Coursera
note: https://www.coursera.org/projects/scikit-learn-to-solve-regression-ml-problems

Assessments:
category: Final Project Evaluation
type: on-going
weight: 20.0
duration: 30'/groups
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7
no_question: Oral
knowledge_and_skill: Committee's comments and questions
grading_guide: All chapters; knowledge and skills
note: The examination is conducted by the exam board. A minimum of two teachers is required for grading the Final Project.
Each team is allocated 30 minutes for the presentation of their project followed by a Q&A session.
Evaluation of the final presentation is based on the final report, project demonstration, and Q&A interaction.
The grade is determined by averaging the grades given by the two teachers.
category: On going assessment 1
type: on-going
weight: 10.0
duration: From start to 16th session in schedule.
question_type: CLO1, CLO2, CLO3
no_question: Follow lecturer's proposal
knowledge_and_skill: Follow lecturer's proposal
grading_guide: knowledge and skills
note: in class, by instructor
category: On going assessment 2
type: on-going
weight: 10.0
duration: From 16th to 36th session in schedule.
question_type: CLO4, CLO5
no_question: Follow lecturer's proposal
knowledge_and_skill: Follow lecturer's proposal
grading_guide: knowledge and skills
note: in class, by instructor
category: On going assessment 3
type: on-going
weight: 10.0
duration: From 37th to finish in schedule.
question_type: CLO6, CLO7
no_question: Follow lecturer's proposal
knowledge_and_skill: Follow lecturer's proposal
grading_guide: knowledge and skills
note: in class, by instructor
category: Progress test 1
type: on-going
weight: 10.0
duration: 45'
question_type: CLO1, CLO2, CLO3, CLO4
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 1,2,3,4
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 10.0
duration: 45'
question_type: CLO4, CLO5, CLO6, CLO7
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 5,6,7
note: in class, by instructor
category: Project Planning
type: on-going
weight: 10.0
duration: 30'
question_type: CLO1, CLO2
no_question: Home work,
knowledge_and_skill: 6
grading_guide: - cover chapter 1,2,3,4,5,6
note: in class, by instructor
category: Final exam
type: Final exam
weight: 20.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: - cover chapter 1,2,3,4,5,6,7
note: by exam board,
using computer

Schedule:
session: Course Introduction
learning_outcomes: IT
materials: mat1
session: 1.1. Introduction to Data Science 1
learning_outcomes: T
materials: mat2
session: 1.2. Introduction to Data Science 2
learning_outcomes: T
materials: mat3
session: 1.3 Data Science Topics 1
learning_outcomes: I
materials: mat4
session: 1.4 Data Science Topics 2
learning_outcomes: I
session: + Practice- Discussing the data science around us:
- Discussing the data science around us
learning_outcomes: U
session: + Project:
- Topic Introduction and discuss
learning_outcomes: T
session: Project:
- Topic Resgistration
learning_outcomes: T
session: Practice: Data Science Methodology in theory and practice
- Understanding of the data science methodology
learning_outcomes: U
session: Project:
- Understand the Problem
learning_outcomes: U
I
session: Project:
- Business Understanding
- Understand Analytic Approach
learning_outcomes: U
session: Project:
- Data Requirements
- Data Collection
learning_outcomes: U
session: Project:
- Data Understand
- Data Preprocessing
learning_outcomes: U
session: Project:
- Modeling
- Evaluation
learning_outcomes: U
session: Project:
- Deployment
- Feedback
learning_outcomes: U
session: 2.6 Data science methodology
- Project Review 1
learning_outcomes: T
session: 2.7 Data science steps
- Project Review 1 (cont)
learning_outcomes: T
session: Project Planing
learning_outcomes: U
session: Project:
- Accessing Databases using Python
learning_outcomes: U
session: Project:
- Data Analysis with SQL- Basic
learning_outcomes: U
session: Project:
- Data Analysis with SQL- Intermediate
learning_outcomes: U
session: Project:
- Data Analysis with SQL - Advance
learning_outcomes: U
session: +Practice: database basic, intermediate
- practice composing nested queries
- execute select statements to access data from multiple tables
learning_outcomes: U
session: Practice: cont'
learning_outcomes: U
session: - Getting started with SQL
- Data analysis with SQL
learning_outcomes: T
session: + Practice: Introduce students to data science tools. Perform basic operations on data
+ Project:
- Analysis with tool introduction
learning_outcomes: U
session: + Project:
- Analysis with tool introduction
learning_outcomes: U
session: Project:
- Analysis with tool using Python
learning_outcomes: U
session: Project:
- Analysis with tool using R
learning_outcomes: U
session: Project:
- Analysis with tool using Scala
learning_outcomes: U
session: Project:
- Analysis with tool using SQL
learning_outcomes: U
session: Project:
- Analysis with tool using GitHub
learning_outcomes: U
session: Project:
- Analysis with tool using Jupyter Notebooks
learning_outcomes: U
session: Project:
- Analysis with tool using RStudio IDE
learning_outcomes: U
session: Project:
- Analysis with tool using Watson Studio
learning_outcomes: U
session: Project Review 2
learning_outcomes: T
session: Project Review 2 (cont)
learning_outcomes: T
session: Progress test and/or
Review Exercises
Guiding Exercises/Assignment
learning_outcomes: U
session: Project:
- Understand python API
learning_outcomes: U
session: Project:
- Analysis with Python- Collect Data
learning_outcomes: U
session: Project:
- Analysis with Python- Understand Data
learning_outcomes: U
session: Project:
- Analysis with Python- Preprocessing Data
learning_outcomes: U
session: Project:
- Analysis
learning_outcomes: U
session: Project:
- Analysis
learning_outcomes: U
session: - Practice: Python for Data Science, AI & Development
learning_outcomes: U
session: Project:
- Basic Data Statistics
learning_outcomes: U
session: Project:
- Advance Data Statistics
learning_outcomes: U
session: Project:
- Basic Data Visualization
learning_outcomes: U
session: Project:
- Advance Data Visualization
learning_outcomes: U
session: Project:
- Basic Data Probability Distributions
learning_outcomes: U
session: Project:
- Advance Data Probability Distributions
learning_outcomes: U
session: Project:
- Basic Regression Analysis
learning_outcomes: U
session: Project:
- Advance Regression Analysis
learning_outcomes: U
session: Project:
- Advance Regression Analysis
learning_outcomes: U
session: Project:
- Advance Regression Analysis
learning_outcomes: U
session: Practice: Statistics for Data Science with Python- Housing data
learning_outcomes: U
session: - Project Review 3
learning_outcomes: T
session: - Project Review 3 (cont)
learning_outcomes: T
session: Progress test and/or
Review Exercises
Guiding Exercises/Assignment
learning_outcomes: T
session: - Review course
- Final report
learning_outcomes: I
T