Data Analytics (5 cr)

Materials

Joel Grus: Data Science from Scratch: First Principles with Python

Evaluation scale

0-5

Further information

Avoin amk 5

Employer connections

It is advisable that the students use their own work environment as an object of study and as a data source.

Possible guest lecturers from companies.

Virtual portion

4

Assessment criteria, satisfactory (1)

**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.

Assessment criteria, good (3)

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.

Content scheduling

There will be one or few contact sessions on saturdays at 9am - 3pm. The exact times are announced by the end of the year 2021. The place will be JAMK / Lutakko campus, Piippukatu, Jyväskylä.

5 ECTS credits equals about 135 hours of study work.
Contact instruction: 36 hours
Exercises/Study project: 99 hours

Exam schedules

The course does not have an exam. The assessment is based on the evaluation assignments.

Teaching language

en

Teaching methods

5 ECTS credits equals about 135 hours of study work.
Contact instruction: 36 hours
Exercises/Study project: 99 hours

Number of ECTS credits allocated

5

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

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