Data Analysis (4 cr)

Objective

The student understands the significance of data analytics in the digitalizating operational environment. The student knows the most commonly used methods of data analytics as well as how to apply them in practice to existing data and interpret the results of the methods.

Content

• Python data analytics libraries: NumPy, Pandas ja Matplotlib
• Data visualization: creation and analysis of descriptors ?//kuvaajien
• Processing of missing values
• Outliers
• Termin: Average, standard deviation, correlation coeficient and their interpretation
• The concept of probability distribution (particularly normal deviation), confidence interval and hypothesis testing.
• Linear/logistic regression

Assessment criteria, satisfactory (1)

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. 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.

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. 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.

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. 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.

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 analyzing data and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation superficially.

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 for analyzing data. Additionally, the student is able to assess his/her implementation briefly.

Fail 0: The student does not meet the minimum criteria set for the course.

Qualifications

Basics in computing, programming, knowledge and know-how of Python programming language.