Rajaa hakua
Piilota rajaukset
TH00CI68-3005
ITSlearning ja sähköposti
Ennen kurssin alkua itseopiskeltavaa materiaalia:
Luut ja ytimet - ihmiselimistö lyhyesti : Nienstedt ja Kallio (WSOY) tai
Keho - Anatomia ja fysiologia: Vierimaa ja Laurila (SanomaPro)
TH00CO80-3004
Opintojakso suoritetaan CampusOnlinessa, tarkammat tiedot opintojaksosta löydät toteutuksen kurssikortilta
https://campusonline.fi/course/matematiikan-ja-kemian-laskennalliset-perusteet-laboratoriotyohon/
TH00CO66-3004
Tämä opintojakso on suoritettava hyväksytysti ennen ensimmäistä työelämäharjoittelua.
Tämä opintojakso on suoritettava hyväksytysti ennen kuin kliininen näytteenotto- ja vierianalytiikka 2 -opintojakson suoritus voidaan hyväksyä.
7073110-3011
- Opintojakso tulee olla hyväksytysti suoritettu ennen ensimmäistä työelämäharjoittelua
- Opintojakson arvosana on EKG- ja spirometriaosioiden sekä itsenäisistä tehtävistä muodostuvien arviointien keskiarvo
- Yhden osion (EKG tai spirometria) tentin voi yrittää uusia kaksi kertaa.
- Mikäli opiskelija saa hylätyn jommasta kummasta tai molemmista osioista, tulee hänen osallistua opintojakson seuraavalle toteutukselle uudestaan.
TH00CO60-3004
Viestintä joko sähköpostilla tai Itslearning-oppimisalustalla, sovitaan ryhmän kanssa opintojakson alussa. Tämä opintojakso on oltava hyväksytysti suoritettu, jotta pääsee osallistumaan Näytteenotto ja asiakaspalveluosaaminen -opintojaksolle sekä Kliininen mikrobiologia 2-opintojaksolle.
5051270-3008
ITS learning
Teams will be used for storage of the project related material (datasets, software, documentation, etc.)
5051249-3007
Administrative information: Peppi platform
Additional course information: itslearning workspace
Main contact channels: MS Teams and email
The use of AI: Allowed in learning tasks and can be used, but the use must be reported. Artificial intelligence can be used in the creation of outputs, but the student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the assessment.
Available for max. 5 open TUAS students.
5051257-3007
All course materials and instructions can be found in It'sLearning workspace.
The use of AI inlearning tasks
Allowed, can be used, must be reported
Artificial intelligence can be used in the creation of outputs, but the student must clearly report its
use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the
assessment.
Available for max. 5 open TUAS students.
TT00CN71-3005
ITS and Teams.
USE OF ARTIFICIAL INTELLIGENCE REPORTED
Allowed, can be used, must be reported. Artificial intelligence can be used in the creation of outputs, but the student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the assessment.
TT00CN68-3005
Use of AI in assignments and final project: USE OF AI REPORTED.
AI can be used in the creation of outputs, but student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect to assessment.
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Qualifications and Prerequisites:
Before taking an "Introduction to Data Engineering with Python" course, students typically need a foundational understanding of several key areas. Here are the mandatory and recommended prerequisite courses and topics.
1. Mandatory Prerequisites:
1.1. Programming:
1.1.1. Introduction to Programming: Knowledge of programming fundamentals,
including concepts like variables, loops, conditionals, and functions.
1.1.2. Python Programming: Familiarity with Python, including basic syntax, data
types, control structures, and function and modules
1.1.3. Error Handling
1.1.4. Object-oriented programming (OOP)
1.1.5. Data Manipulation: Skills in using Pandas library including DataFrames and
Series, reading, writing, filtering, and transforming data
1.2. Databases: Knowledge of how databases work, including concepts like tables, keys, normalization, and indexing.
2. Recommended Topics:
2.1. Algorithms and Data Structures: Basic understanding of algorithms and data
structures such as arrays, lists, trees, and graphs, which are crucial for data
processing
2.2. Having the fundamental knowledge of cloud services or passing the Cloud Services
Course in TUAS (Lecturer: Ali Khan)
2.3. Version Control Systems: Basic understanding of tools like Git for version control.
2.4. Basic Algebra and Calculus: Fundamental math skills to handle data transformations
and calculations.
2.5. Statistics: Understanding of basic statistical concepts like mean, median, standard
deviation, and probability distributions.
2.6. Being familiar with VirtualBox and Ubuntu