Basics of Web DevelopmentLaajuus (8 cr)
Code: 5G00GC28
Credits
8 op
Objectives
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
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
Basics of Programming
Assessment criteria, satisfactory (1-2)
The student is able to produce a simple web page and format the structure of the page. The student knows statistical key figures.
Assessment criteria, good (3-4)
The student is able to create a versatile web application and take advantage of APIs. The student knows and can use statistical key figures.
Assessment criteria, excellent (5)
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. The student can use statistical key figures in presenting data.
Enrolment period
15.09.2024 - 27.10.2024
Timing
24.10.2024 - 23.02.2025
Credits
8 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Esa Parkkila
- Miika Huikkola
Person in charge
Louis Botha
Groups
-
24I260EA
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 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. The student knows statistical key figures.
Assessment criteria, good (3-4) (course unit)
The student is able to create a versatile web application and take advantage of APIs. The student knows and can use statistical key figures.
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. The student can use statistical key figures in presenting data.
Location and time
Schedule in learning environment
Exam schedules
Will be announced in October 2024
Time will be scheduled during the course for completing the larger final assignment.
Retakes and raising grades can be arranged by completing a project and/or extra work.
Math part final exam on week XX, 1st retake on week YY, 2nd retake on week ZZ
Assessment methods and criteria
Programming part
Exam is mandatory to pass the programming part of the course and is graded 0 - 5.
Maximum points for the exam is 50 points.
Doing exercises give extra points for the exam:
If you do min 50% of given exercises (points) -> 3 extra points for the exam
If you do min 70% of given exercises (points) -> 6 extra points for the exam
If you do min 90% of given exercises (points) -> 9 extra points for the exam
Normal exam practices:
- Allowed 1 retake to raise exam grade
- Allowed 2 retakes to pass the exam
[ 0, 20] => 0
[21, 26] => 1
[27, 32] => 2
[33, 38] => 3
[39, 44] => 4
[45, 50] => 5
---------
Math part
Course math part will be held during periods 2&3, i.e., between 21.10.2024-23.2.2025
Math part is evaluated based on the course activity (50%) and exam (50%) by grade 0-5.
Math part point limits
30%: 1
45%: 2
60%: 3
75%: 4
90%: 5
- - - - - -- - -
The overall course grade will be calculated as a cu-weighted average of Programming part (6/8) and Math parts (2/8).
Assessment scale
0-5
Teaching methods
Lectures
Assignments
Project (larger assignment)
Exam
Learning materials
Online Learning Environment
Student workload
Programming Part
4 hours of classroom lectures per week. (60h)
Independent work (102h)
Homework is the exercises not completed during the lecture.
Math part ca 55 h
~7x(1h + 2h) contact teaching and exams
Independent work ca 35 h
Content scheduling
Programming part
Git
HTML
CSS
JavaScript
Node
Math part
Statistical descriptors
Statistical inference
Data visualization
Completion alternatives
Programming Part
Complete only the exam
Practical training and working life cooperation
Web development:
Average 4 hours of classroom lectures per week, 60h of lectures.
Homework is the exercises not completed during the lecture.
Math part ca 55 h
Approx. 20 h contact teaching & exams
Independent work ca 35 h
Enrolment period
15.09.2024 - 27.10.2024
Timing
24.10.2024 - 02.03.2025
Credits
8 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Esa Parkkila
- Miika Huikkola
Person in charge
Louis Botha
Groups
-
24I260EB
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 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. The student knows statistical key figures.
Assessment criteria, good (3-4) (course unit)
The student is able to create a versatile web application and take advantage of APIs. The student knows and can use statistical key figures.
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. The student can use statistical key figures in presenting data.
Location and time
Schedule in learning environment
Exam schedules
Will be announced in October 2024
Time will be scheduled during the course for completing the larger final assignment.
Retakes and raising grades can be arranged by completing a project and/or extra work.
Math part final exam on week XX, 1st retake on week YY, 2nd retake on week ZZ
Assessment methods and criteria
Programming part
Exam is mandatory to pass the programming part of the course and is graded 0 - 5.
Maximum points for the exam is 50 points.
Doing exercises give extra points for the exam:
If you do min 50% of given exercises (points) -> 3 extra points for the exam
If you do min 70% of given exercises (points) -> 6 extra points for the exam
If you do min 90% of given exercises (points) -> 9 extra points for the exam
Normal exam practices:
- Allowed 1 retake to raise exam grade
- Allowed 2 retakes to pass the exam
[ 0, 20] => 0
[21, 26] => 1
[27, 32] => 2
[33, 38] => 3
[39, 44] => 4
[45, 50] => 5
---------
Math part
Course math part will be held during periods 2&3, i.e., between 21.10.2024-23.2.2025
Math part is evaluated based on the course activity (50%) and exam (50%) by grade 0-5.
Math part point limits
30%: 1
45%: 2
60%: 3
75%: 4
90%: 5
- - - - - -- - -
The overall course grade will be calculated as a cu-weighted average of Programming part (6/8) and Math parts (2/8).
Assessment scale
0-5
Teaching methods
Lectures
Assignments
Project (larger assignment)
Exam
Learning materials
Online Learning Environment
Student workload
Programming Part
4 hours of classroom lectures per week. (60h)
Independent work (102h)
Homework is the exercises not completed during the lecture.
Math part ca 55 h
~7x(1h + 2h) contact teaching and exams
Independent work ca 35 h
Content scheduling
Programming part
Git
HTML
CSS
JavaScript
Node
Math part
Statistical descriptors
Statistical inference
Data visualization
Completion alternatives
Programming Part
Complete only the exam
Practical training and working life cooperation
Web development:
Average 4 hours of classroom lectures per week, 60h of lectures.
Homework is the exercises not completed during the lecture.
Math part ca 55 h
Approx. 20 h contact teaching & exams
Independent work ca 35 h
Enrolment period
15.07.2023 - 29.10.2023
Timing
23.10.2023 - 25.02.2024
Credits
8 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Miika Huikkola
- Louis Botha
Person in charge
Louis Botha
Groups
-
23I260EADegree Programme in Software Engineering
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 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. The student knows statistical key figures.
Assessment criteria, good (3-4) (course unit)
The student is able to create a versatile web application and take advantage of APIs. The student knows and can use statistical key figures.
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. The student can use statistical key figures in presenting data.
Location and time
Schedule in learning environment
Exam schedules
Will be announced in October 2023
Time will be scheduled during the course for completing the larger final assignment.
Retakes and raising grades can be arranged by completing a project and/or extra work.
Assessment methods and criteria
Programming part
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
(EDIT: Grade is still counted from assignments and exam)
---------
Math part
EDIT 8.1.2024.
Course math part will be held during period 3, i.e., between 8.1.2024-23.2.2024
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:
Lesson activity: 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 parts.
Assessment scale
0-5
Teaching methods
Lectures
Assignments
Project (larger assignment)
Exam
Learning materials
Online Learning Environment
Student workload
6 hours of classroom lectures per week.
Homework is the exercises not completed during the lecture.
Math part ca 50 h
~4x2,5h contact teaching
Independent work ca 40 h
Content scheduling
Programming part
Git
HTML
CSS
JavaScript
Node
Math part
Statistical descriptors
Statistical inference
Data visualization
Completion alternatives
Programming part can be passed based on previous competence.
Details will be given in the online learning environment.
Practical training and working life cooperation
Web development:
Average 6 hours of classroom lectures per week, 60h of lectures.
Homework is the exercises not completed during the lecture.
Math part ca 45 h
3x3h contact teaching
Independent work ca 35 h
Enrolment period
15.07.2023 - 29.10.2023
Timing
23.10.2023 - 25.02.2024
Credits
8 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Miika Huikkola
- Louis Botha
Person in charge
Louis Botha
Groups
-
23I260EB
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 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. The student knows statistical key figures.
Assessment criteria, good (3-4) (course unit)
The student is able to create a versatile web application and take advantage of APIs. The student knows and can use statistical key figures.
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. The student can use statistical key figures in presenting data.
Location and time
Schedule in online learning environment
Exam schedules
Will be announced in October 2023
Time will be scheduled during the course for completing the larger final assignment.
Retakes and raising grades can be arranged by completing a project and/or extra work.
Assessment methods and criteria
The final grade of the course is calculated by combining the converted assignment completion activity, project grade 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
(EDIT: Grade is still counted from assignments and exam)
---------
Programming part
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
(EDIT: Grade is still counted from assignments and exam)
---------
Math part
EDIT 8.1.2024.
Course math part will be held during period 3, i.e., between 8.1.2024-23.2.2024
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:
Lesson activity: 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 parts.
Assessment scale
0-5
Teaching methods
Lectures
Assignments
Project (larger assignment)
Exam
Learning materials
Online 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
Node
Math part
Statistical descriptors
Statistical inference
Data visualization
Completion alternatives
Programming part can be passed based on previous competence.
Details will be given in the online learning environment.
Practical training and working life cooperation
Web development:
Average of 6 hours of classroom lectures per week, 60h of lectures.
Homework is the exercises not completed during the lecture.
Math part ca 50 h
~4x2,5h contact teaching
Independent work ca 40 h
Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)
Did not complete over 70% of the assignments during the course
OR
Did not pass the exam
Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)
Any combination of meeting the assignments requirements and passing the exam.
Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)
Any combination of meeting the assignments requirements and passing the exam.
Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)
You will need to complete at least 70% of the assignments
You will need to achieve 90% or more for the exam