AI-DS Project (DAP391m)
Vietnamese title: Dự án TTNT-KHDL
Credits: 3
Prerequisites: PFP191, ADY201m

Description:
This course will take student from the basics of data analysis with Python to building and evaluating data models. The students will learn to work with some Data Visualization tools and techniques and create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. A student will be able to write an application that leverages multiple Watson AI services. This course allows students to practice data scientists' work in real life when working with datasets.

Learning outcomes:
Understand the basics of data analysis with Python to building and evaluating data models.
Understand how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations.
Know difference ways to effectively visualize both small and large-scale data
Implement various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more
Implement an application that leverages multiple Watson AI services (Discovery, Speech to Text, Assistant, and Text to Speech)
Implementing different techniques to develop applications and AI powered solutions.
Implement the small mini capstone data science projec includes steps: involving data collection, data wrangling, exploratory 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 https://flm.fpt.edu.vn/ for up-to-date course information

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

Materials:
description: Data Analysis with Python
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/data-analysis-with-python
description: Data Visualization with Python
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/python-for-data-visualization
description: Building AI Applications with Watson APIs
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/building-ai-applications
description: Applied Data Science Capstone
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/applied-data-science-capstone
description: GP 1: Simple Linear Regression for the Absolute Beginner
author: Coursera
publisher: Coursera
note: https://www.coursera.org/projects/simple-linear-regression-for-the-absolute-beginner
description: GP 2: Predict Ad Clicks Using Logistic Regression and XG-Boost
author: Coursera
publisher: Coursera
note: https://www.coursera.org/projects/predict-ad-clicks-using-logistic-regression-and-xg-boost

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: lecturer's comments and questions
grading_guide: All chapters; knowledge and skills
note: in class, by instructor.
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.
category: On going assessment
type: on-going
weight: 30.0
duration: From start to finish in schedule.
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, 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
type: on-going
weight: 10.0
duration: 45'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 1,2,3
note: in class, by instructor
category: Project Planning
type: on-going
weight: 10.0
duration: 30'
question_type: CLO1
no_question: Home work,
knowledge_and_skill: 6
grading_guide: - cover chapter 1,2,3,4
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7
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
- Project Topic Introduction and discuss
learning_outcomes: IT
materials: mat1
session: - Project Topic Introduction and discuss
- Student create group
- Student register the topic
learning_outcomes: I
materials: mat2
session: - Student presents Project Planning
learning_outcomes: U
materials: mat3
session: - Student presents Project Planning (cont)
learning_outcomes: U
materials: mat4
session: - Project report 1: P1
+ Business Understanding
learning_outcomes: U
materials: mat5
session: - Project report 1: P2
+ Data Understand: Collect data, understand data
learning_outcomes: U
session: - Project report 1: P3

+ Perform data preprocessing
learning_outcomes: U
session: Project:
1.1. Data Analysis with Python- importing Datasets 1
1.2. Data Analysis with Python- importing Datasets 2
learning_outcomes: U
session: Project:
1.3 Data Analysis with Python- Data Wrangling 1
1.4 Data Analysis with Python- Data Wrangling 2
learning_outcomes: U
session: - Project report 2: P1
+ Data Analysis
learning_outcomes: U
session: - Project report 2: P2
+ Data Analysis
learning_outcomes: U
session: - Project report 2: P3
+ Data Analysis
learning_outcomes: U
session: - Project report 2: P4
+ Data Analysis
learning_outcomes: U
session: - Project:
1.5 Data Analysis with Python- Exploratory Data Analysis 1
learning_outcomes: U
session: - Project:
1.6 Data Analysis with Python- Exploratory Data Analysis 2
learning_outcomes: U
session: - Project report 3:
+ Data Visualization in advanced: Waffle Charts
learning_outcomes: U
session: - Project report 3:
+ Data Visualization in advanced: Area Plots, Histograms
learning_outcomes: U
session: - Project report 3:
+ Data Visualization in advanced: Bar Charts, Pie Charts
learning_outcomes: U
session: - Project report 3:
+ Data Visualization in advanced: Scatter Plots, Word Clouds
learning_outcomes: U
session: - Project report 3:
+ Data Visualization in advanced: Choropleth Maps
learning_outcomes: U
session: - Project:
2.1 Data Visualization with Python- Introduction to Data Visualization Tools 1
learning_outcomes: U
session: - Project:
2.2 Data Visualization with Python- Introduction to Data Visualization Tools 2
learning_outcomes: U
session: - Project:
2.3 Data Visualization with Python- Basic and Specialized Visualization Tools 1
learning_outcomes: U
session: - Project:
2.4 Data Visualization with Python- Basic and Specialized Visualization Tools 2
learning_outcomes: U
session: - Project:
2.5 Advanced Visualizations and Geospatial Data 1
learning_outcomes: U
session: - Project:

2.6 Advanced Visualizations and Geospatial Data 2
learning_outcomes: U
session: - Project:
2.7 Data Visualization with Python- Creating Dashboards with Plotly and Dash 1
learning_outcomes: U
session: - Project:
2.8 Data Visualization with Python- Creating Dashboards with Plotly and Dash 2
learning_outcomes: U
session: - Project review
learning_outcomes: T
session: - Project review (cont)
learning_outcomes: T
session: - Project review (cont)
learning_outcomes: T
session: - Project review (cont)
learning_outcomes: T
session: - Project report 4: P1
+ Chatbot: Create and integrate
learning_outcomes: U
session: - Project report 4: P2
+ Chatbot: Create and integrate
learning_outcomes: U
session: - Project report 4: P3
+ Chatbot: Create and integrate
learning_outcomes: U
session: - Project report 4: P4
+ Chatbot: Create and integrate
learning_outcomes: U
session: - Project:
3.1 Building AI Applications with Watson APIs- Introduction 1
learning_outcomes: U
session: - Project:

3.2 Building AI Applications with Watson APIs- Introduction 2
learning_outcomes: U
session: - Project:
3.3 Building AI Applications with Watson APIs- Giving it a Voice 1
learning_outcomes: U
session: - Project:
3.4 Building AI Applications with Watson APIs- Giving it a Voice 2
learning_outcomes: U
session: - Project:
3.5 Building AI Applications with Watson APIs- Deployment 1
learning_outcomes: U
session: - Project:
3.6 Building AI Applications with Watson APIs- Deployment 2
learning_outcomes: U
session: - Project Report 5: P1
+ Build model for prediction
learning_outcomes: U
session: - Project Report 5: P2
+ Build model for prediction
learning_outcomes: U
session: - Project Report 5: P3
+ Build model for prediction
learning_outcomes: U
session: - Project Report 5: P4
+ Build model for prediction
learning_outcomes: U
session: - Project:
1. 7 Data Analysis with Python- Model Development 1
learning_outcomes: U
session: - Project:

1.8 Data Analysis with Python- Model Development 2
learning_outcomes: U
session: - Project:
1.9 Data Analysis with Python- Model Evaluation 1
learning_outcomes: U
session: - Project:
1.10 Data Analysis with Python- Model Evaluation 2
learning_outcomes: U
session: - Project report 5: P1
+ Develop applications and AI powered solutions.
learning_outcomes: U
session: - Project report 5: P2
+ Develop applications and AI powered solutions.
learning_outcomes: U
session: - Project report 5: P3
+ Develop applications and AI powered solutions.
learning_outcomes: U
session: - Project report 5: P4
+ Develop applications and AI powered solutions.
learning_outcomes: U
session: - Project:
4.1 Applied Data Science Capstone: Introduction 1
4.2 Applied Data Science Capstone: Introduction 2
learning_outcomes: U
session: - Project:
4.3 Applied Data Science Capstone: Interactive Visual Analytics and Dashboard 1
4.4 Applied Data Science Capstone: Interactive Visual Analytics and Dashboard 2
4.5 Applied Data Science Capstone: Predictive Analysis (Classification) 1
4.6 Applied Data Science Capstone: Predictive Analysis (Classification) 2
learning_outcomes: U
session: - Project:
4.7 Applied Data Science Capstone: Present Your Data-Driven Insights 1
4.8 Applied Data Science Capstone: Present Your Data-Driven Insights 2
'- Progress test
learning_outcomes: U
session: - Evaluate final project
learning_outcomes: T
session: - Evaluate final project (cont)
learning_outcomes: T
session: - Evaluate final project (cont)
- Review course
learning_outcomes: I
T