Data Analytics (5 cr)
Code: YTIP2200-3005
General information
Enrollment
01.08.2024 - 31.08.2024
Timing
02.09.2024 - 18.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Face-to-face
Unit
School of Technology
Teaching languages
- English
Seats
0 - 35
Degree programmes
- Master's Degree Programme in Artificial Intelligence and Data Analytics
Teachers
- Harri Varpanen
Groups
-
YTI24S1Master's Degree Programme in Artificial Intelligence and Data-analytics
- 25.10.2024 15:00 - 20:00, Data Analytics YTIP2200-3005
- 26.10.2024 09:00 - 15:00, Data Analytics YTIP2200-3005
Objective
The student understands the significance of data analytics in the digitalizing operational environment. The student knows the most commonly used methods and theories of data analytics as well as how to apply them in practice to existing data and interpret the results of the methods.
Course competences
EUYEN EUR-ACE: Engineering Analysis, Master's Degree
EUYEE EUR-ACE: Engineering Design, Master's Degree
EUYIV EUR-ACE: Investigations, Master's Degree
Content
- Python data analytics libraries: NumPy, Pandas, Matplotlib, Seaborn, Scipy
- Data visualization
- Processing of missing values and outliers
- Statistical terms: Average, standard deviation, correlation coefficient and their interpretations
- The concept of probability distribution, confidence interval and hypothesis testing.
- Bernoulli and Poisson processes
- Linear/logistic regression, decision trees
Oppimateriaali ja suositeltava kirjallisuus
Online material.
Teaching methods
Online lectures (recorded), bi-weekly exercises with peer review.
One mandatory weekend campus meeting face-to-face: Fri Oct 25 - Sat Oct 26, 2024.
Exam schedules
No exams.
Vaihtoehtoiset suoritustavat
Uni. Helsinki: Data Analysis with Python (MOOC), course code CSM90004
Content scheduling
Seven sets of theory / exercises:
1. Orientation (basics, numpy, matrices)
2. Data manipulation (pandas)
3. Data visualization (matplotlib & seaborn)
4. Time series (pmdarima)
5. Linear regression (scipy / sklearn)
6. Logistic regression (scipy / sklearn)
7. Dimension reduction (scipy / sklearn).
Further information
The grade is determined by the number of completed exercises.
Basic knowledge of python is assumed.
Evaluation scale
0-5
Arviointikriteerit, tyydyttävä (1-2)
**Assessment criteria, sufficient 1, satisfactory 2
Sufficient 1:
The student knows about the most commonly used techniques in data analytics in data analysis tasks. He/she is able to apply the most common techniques to analysing data and has sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to assess his/her implementation briefly.
Satisfactory 2:
The student knows the most commonly used techniques in data analytics in data analysis tasks. He/she is able to select the techniques for analysing data and apply his/her technical know-how in practice. Student understands the mathematics behind the techniques at a satisfying level. Additionally, the student is able to assess his/her implementation superficially.
Arviointikriteerit, hyvä (3-4)
Good 3:
The student is aware of the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics in various data analysis tasks. Student understands well the mathematics behind the techniques at a good level. He/she is able to validate and select the techniques in data analysis and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation and validate its development.
Very good 4:
The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics and is able to extensively validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques at a very good level. He/she is able to versatilely validate and select the correct techniques for the analysis of data and apply his/her technical know-how to practice. Additionally, the student is able to assess his/her implementation profoundly and validate its development.
Assessment criteria, excellent (5)
Excellent 5:
The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques in data analytics and is able to critically validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques in excellent level. He/she is able to critically validate and select the correct techniques in data analysis regardless of the data to be analyzed and apply the technical know-how to practice. Additionally, the student is able to critically assess his/her implementation and validate its development.