1. Course Code2262
2. Course Title
G52e: Data Science Exercises
3. Teacher
HAMIDULLAH, Sokout
4. TermSpring 2
5. Course Requirements (Courses / Knowledge prerequisite for this course)
Data Science Course (2261)

6. Course Overview and Objectives
The major goal of Data Science is to solve companies (institutions, government, privatesectors, .. etc) problems using available data. Data Science entails everything that has
something to do with data such as: Collecting the data, Cleaning up the data, Visualizing
data, Analyzing it, and Creating predictive models based on the data. Playing with data will
paves the way to gain insights that could help us with good decision making.
In this course we will starting from the very basics (pre-processing data), we will build up
your skills and soon you will be able to solve advanced statistics tasks using machinelearning algorithms in Python such as CLUSTERING, REGRESSION, CLASSIFICATION.
As well as you learn with integration and visualization with Python.
7. Course Outline
1 Course Orientation and Data Analysis with Python
2 Data Visualization with Python
3 Data Visualization with Python
4 Data Visualization with Python Exercises
5 Data Preprocessing
6 Data Preprocessing Exercises with Machine Learning (Regression)
7 Machine Learning Algorithmes (Classification)
8 Classification Exercises
9 Machine Learning Algorithms (Clustering)
10 Clustering Exercises11 Model Selection
12 Exercises
13 Integration Concept
14 Integration Exercises (Homework)
15 Students Presentation
16 Students Presentation
8. Textbooks (Required Books for this course)

9. Reference Books (optional books for further study)(1) Machine Learning with Python Cookbook, ISBN: 9781491989388.
(2) Advanced Data Analytics using Python, ISBN: 978-1-4842-3449-5.
(3) Available resources on Internet
10. Course Goals (Attainment Targets)(1) Understanding and applying Visulization with Python(2) Able to preprocess data using Python
Gain in-depth familiarity with various Machine Learning algorithms (supervised learning(3)
algorithms and unsupervised learning algorithms)
Able to Implement machine learning algorithms to real-world problems, and rigorously(4)evaluate their performance using different methods.
Understand the concpet of Integration Programming and implement in real-world(5)problems.
11. Correspondence relationship between Educational goals and Course goals
Educational goals of the school
Basic academic skills
Specialized knowledge and literacy
Ability to continually improve own strengths
Problem setting
Ability to discover andHypothesis planning
resolve the problem in
Human skill
Hypothesis testingsociety(Tankyu skill）
PracticeFundamental
Ability to step forward
Competencies for
Ability to think throughWorking Persons
Ability to work in a team
Professional ethics

High level ICTskills

Course Goals(1) (2) (3) (4) (5)(1) (2) (3) (4) (5)(3) (4) (5)(3) (4) (5)(3) (4) (5)12. Evaluation
Goals
Examination

Evaluation method & point allocation
Quiz
Reports PresentationDeliverables〇〇〇〇〇〇
〇〇
〇
〇
〇

Other

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Allocation
20305013. Evaluation Criteria
Examination
Quiz
Every week multiple choice quizzes are used to evaluate theunderstanding of students and motivate them for further learning.
Reports
Presentation
In the final presentation, students will be asked to work on a real-world
problem using Machine Learning algorithm. The evaluation will be basedon participation in the group presentation, the structure of presentation, therelavance of the argument, time managment and relation to the coursecontents.
Deliverables
Individual and group assignment will be assigned for the students withfocus on learning goals (1,3,4, and 5). The evaluation will be based on
how the students understand the exercises and participation.
Other
14. Active Learning
Hourly percentage of active learning within the whole class time
1 Active learning such as problem solving assignment using the knowledge
and skills acquired in class.
2 Active learning such as group works and discussions.
3 Outcome presentations and feedbacks.
4 Students actively make decisions on how the class should be conducted.
15. Notes
Please bring your computers in the class.

80%
All the time
All the timeSometimes
Not at all16. Course plan
(Notice) This plan is tentative and might be changed at the time of delivery

Lessen 1: (Course Orientation)(90 min)1. Course Orientation and Introduction to Data Science Exercises
2. Data Analysis with Python Part III
Lessen 2: (Data Visualization with Python )(90 min)

1. Data Analysis Exercise
2. Data Visualization with Python

Lessen 3: (Data Visualization with Python)(30 min Lecture(60 min Exercises)

1. Data Visualization with Python
a. Bar chartsb. Histogramc. Regression
Lessen 4: (Data Visualization with Python)
1. Data Visualization with Python
a. Word Cloudb. Waffle Chartc. Exercise
Lessen 5: (Data Preprocessing)
Data Preprocessing with Python
1. Importing Libraries
2. Uploading Data set
3. Dealing with missing data
4. Encoding categorical data
5. Splitting dataset (Traing & Testing)
6. Feature Scalling(30 min Lecture(60 min Exercises)(90 min)Lessen 6: (Data Preprocessing Exercises)(90 min)

1. Machine Learning Algorithmes (Regression)
2. Exercises
Lessen 7: (Machine Learning Algorithmes)(90 min)Classification
Lessen 8: (Classification Exercises)(90 min)

Exercises
Lessen 9: (Machine Learning Algorithmes)(90 min)

Clustering
Lessen 10: (Clustering Exercises)(90 min)

Exercises
Lessen 11: (Model Selection)(90 min)

1. Model Selection Concepts
2. Model Selection Exercises
Lessen 12: (Exercises)(90 min)

Exercises
Lessen 13: (Integration Exercises)(90 min)

1. Integration Concept
2. Integration Exercise
Lessen 14: Integration Exercises (Homework)(90 min)Integration Exercises
Lessen 15: (Student Presentation)
Students Presentation
Lessen : Students Presentation
Students Presentation(90 min)(90 min)