Study Guide

Objectives (course unit)

The student knows the basics of web programming and how to implement the storage and processing of data that supports the IoT system. The student is able to implement a simple web application. The student is able to do statistical calculations from data. The student is familiar with the most common modern techniques of data storage and web programming.

Content (course unit)

Web programming (6 ECTS): Web programming techniques and languages, data reading from api, data processing, data display to end user. Command line basics.

Basics of statistics and its concepts (2 ECTS).

Prerequisites (course unit)

Basics of C++ Programming

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to produce a simple web page and format the structure of the page.

Assessment criteria, good (3-4) (course unit)

The student is able to create a versatile web application and take advantage of APIs.

Assessment criteria, excellent (5) (course unit)

The student is able to create and publish a web application with an easy-to-use structure. The student is able to store, read, process and display data to the end user.

Location and time

Schedule in learning environment

Exam schedules

Will be announced in January 2024

Time will be scheduled during the semester for completing the final assignment.

Retakes and raising grades can be arranged by completing a project and/or extra work.

Assessment methods and criteria

Programming
------------------
The final grade of the course is calculated by combining the converted assignment completion activity and exam score of the course.
Assignments + exam = final grade
1 + 4 = 5

You can pass the course with 1, by only completing over 60% of the assignments during the course.
You can pass the course by only doing the exam, but the maximum grade that can be received through the exam is 4.
Conversion tables for % to grade will be in the online learning environment


Math
-------
Math part is evaluated based on the activity and know-how demonstrated on the lessons and by returned assignments by grade 0-5.

Math part points are divided as follows:
Tuntityöskentely: max 12p
Assignments: max 18p

Math part point limits
35%: 1
50%: 2
65%: 3
80%: 4
90%: 5
- - - - - -- - -
The overall course grade will be calculated as a cu-weighted average of Programming part and Math part evaluations.

(Math part info updated on 4.3.2024)

Assessment scale

0-5

Teaching methods

Lectures
Exercises
Project
Exam

Learning materials

Learning Environment

Student workload

Programming
----------------
4 hours of classroom lectures per week.
Homework is the exercises not completed during the lecture.


Math
-------
Math part ca 50 h
Contact teaching 3 h every second week
Independent work ca 35 h

---
The overall course grade will be calculated as a cu-weighted average of Programming part and Math part evaluations.

(Math part info updated on 4.3.2024)

Content scheduling

Programming part
-------------------------
Git
HTML
CSS
JavaScript


Math part
-------------
Statistical descriptors
Statistical inference
Data visualization

Objectives (course unit)

The student knows the basics of web programming and how to implement the storage and processing of data that supports the IoT system. The student is able to implement a simple web application. The student is able to do statistical calculations from data. The student is familiar with the most common modern techniques of data storage and web programming.

Content (course unit)

Web programming (6 ECTS): Web programming techniques and languages, data reading from api, data processing, data display to end user. Command line basics.

Basics of statistics and its concepts (2 ECTS).

Prerequisites (course unit)

Basics of C++ Programming

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to produce a simple web page and format the structure of the page.

Assessment criteria, good (3-4) (course unit)

The student is able to create a versatile web application and take advantage of APIs.

Assessment criteria, excellent (5) (course unit)

The student is able to create and publish a web application with an easy-to-use structure. The student is able to store, read, process and display data to the end user.

Location and time

Schedule in learning environment

Exam schedules

Will be announced in January 2024

Time will be scheduled during the semester for completing the final assignment.

Retakes and raising grades can be arranged by completing a project and/or extra work.

Assessment methods and criteria

Programming
------------------
The final grade of the course is calculated by combining the converted assignment completion activity and exam score of the course.
Assignments + exam = final grade
1 + 4 = 5

You can pass the course with 1, by only completing over 70% of the assignments during the course.
You can pass the course by only doing the exam, but the maximum grade that can be received through the exam is 4.
Conversion tables for % to grade will be in the online learning environment


Math
-------
Math part is evaluated based on the activity and know-how demonstrated on the lessons and by returned assignments by grade 0-5.

Math part points are divided as follows:
Tuntityöskentely: max 12p
Assignments: max 18p

Math part point limits
35%: 1
50%: 2
65%: 3
80%: 4
90%: 5
- - - - - -- - -
The overall course grade will be calculated as a cu-weighted average of Programming part and Math part evaluations.

(Math part info updated on 4.3.2024)

Assessment scale

0-5

Teaching methods

Lectures
Exercises
Project
Exam

Learning materials

Learning Environment

Student workload

Programming
----------------
4 hours of classroom lectures per week.
Homework is the exercises not completed during the lecture.


Math
-------
Math part ca 45 h
3x3h contact teaching
Independent work ca 35 h

---
The overall course grade will be calculated as a cu-weighted average of Programming part and Math part evaluations.

(Math part info updated on 4.3.2024)

Content scheduling

Programming part
-------------------------
Git
HTML
CSS
JavaScript


Math part
-------------
Statistical descriptors
Statistical inference
Data visualization

Objectives (course unit)

The student knows the basics of web programming and how to implement the storage and processing of data that supports the IoT system. The student is able to implement a simple web application. The student is able to do statistical calculations from data. The student is familiar with the most common modern techniques of data storage and web programming.

Content (course unit)

Web programming (6 ECTS): Web programming techniques and languages, data reading from api, data processing, data display to end user. Command line basics.

Basics of statistics and its concepts (2 ECTS).

Prerequisites (course unit)

Basics of C++ Programming

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to produce a simple web page and format the structure of the page.

Assessment criteria, good (3-4) (course unit)

The student is able to create a versatile web application and take advantage of APIs.

Assessment criteria, excellent (5) (course unit)

The student is able to create and publish a web application with an easy-to-use structure. The student is able to store, read, process and display data to the end user.

Location and time

Schedule in learning environment

Exam schedules

Will be announced in January 2023

Time will be scheduled during the semester for completing the final project.

Retakes and raising grades can be arranged by completing a project and/or extra work.

Assessment methods and criteria

Final grade calculated from exam (70%) and project work (30%).

50% of the exercises needs to be returned to pass the course.

Math part is evaluated based on the activity and know-how demonstrated on the lessons and by returned assigments by grades 0, 1 or 2. Math part result has a 25% effect on total course grade.

Math part points are divided as follows:
Course activity: max 10p
Assigments: max 20p

Math part point limits
Under 30%: 0
30%-90%: 1
90% or more: 2

Assessment scale

0-5

Teaching methods

Lectures
Exercises
Project
Exam

Learning materials

Learning Environment

Student workload

6 hours of classroom lectures per week.
Homework is the exercises not completed during the lecture.


Math part ca 45 h
3x3h contact teaching
Independent work ca 35 h

The effect on the final grade will be based on the following formula
round( ( 2*5/2*m + P*cu_P ) / (cu_M + cu_P) )

where
m: math part evaluation {0, 1, 2}
P: Programming part grade (0-5)
cu_P: Programming part cu:s
cu_M: Mathematics part cu:s

Content scheduling

Programming part
Git
HTML
CSS
JavaScript

Math part
Statistical descriptors
Statistical inference
Data visualization

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Under 40% for exam, under 50% of exercises returned or failed to return project work by deadline.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

1. 40% for exam
2. 60% for exam

Project evaluation criteria in learning environment

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

3. 70% for exam
4. 80% for exam

Project evaluation criteria in learning environment

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

5. 90% for exam

Project evaluation criteria in learning environment

Objectives (course unit)

The student knows the basics of web programming and how to implement the storage and processing of data that supports the IoT system. The student is able to implement a simple web application. The student is able to do statistical calculations from data. The student is familiar with the most common modern techniques of data storage and web programming.

Content (course unit)

Web programming (6 ECTS): Web programming techniques and languages, data reading from api, data processing, data display to end user. Command line basics.

Basics of statistics and its concepts (2 ECTS).

Prerequisites (course unit)

Basics of C++ Programming

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to produce a simple web page and format the structure of the page.

Assessment criteria, good (3-4) (course unit)

The student is able to create a versatile web application and take advantage of APIs.

Assessment criteria, excellent (5) (course unit)

The student is able to create and publish a web application with an easy-to-use structure. The student is able to store, read, process and display data to the end user.

Location and time

Schedule in learning environment

Exam schedules

Will be announced in January 2023

Time will be scheduled during the semester for completing the final project.

Retakes and raising grades can be arranged by completing a project and/or extra work.

Assessment methods and criteria

Final grade calculated from exam (70%) and project work (30%).

50% of the exercises needs to be returned to pass the course.

Change on Jan 26th. Math part result is not to be called 'grade'. Reason for change: terminology correction.

Math part is evaluated based on the activity and know-how demonstrated on the lessons and by returned assigments by grades 0, 1 or 2. Math part result has a 25% effect on total course grade.

Points are divided as follows:
Course activity: max 10p
Assigments: max 20p

Math part point limits
Under 30%: 0
30%-90%: 1
90% or more: 2

The effect on the final grade will be based on the following formula
round( ( 2*5/2*m + P*cu_P ) / (cu_M + cu_P) )

where
m: math part evaluation {0, 1, 2}
P: Programming part grade (0-5)
cu_P: Programming part cu:s
cu_M: Mathematics part cu:s

Assessment scale

0-5

Teaching methods

Lectures
Exercises
Project
Exam

Learning materials

Learning Environment

Student workload

6 hours of classroom lectures per week.
Homework is the exercises not completed during the lecture.

Math part ca 45 h
3x3h contact teaching
Independent work ca 35 h

Content scheduling

Programming part
Git
HTML
CSS
JavaScript

Math part
Statistical descriptors
Statistical inference
Data visualization

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Under 40% for exam, under 50% of exercises returned or failed to return project work by deadline.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

1. 40% for exam
2. 60% for exam

Project evaluation criteria in learning environment

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

3. 70% for exam
4. 80% for exam

Project evaluation criteria in learning environment

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

5. 90% for exam

Project evaluation criteria in learning environment