MLI26C744 - Tools for Data Analysis, Lecture, 29.7.2024-16.8.2024

Inträde för gästanvändare
Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Learning outcomes for this course, upon successful completion, include the ability to: 1) understand principles of programming using the Python programming language, 2) use Python to collect data from various sources for analysis, 3) employ Python for data cleaning, 4) implement statistical and predictive models in Python using business data, 5) understand how to choose the correct statistical or predictive model based on the available data and business context, and 6) understand how the information resulting from data analysis leads to improved business decision-making.

Credits: 6

Schedule: 29.07.2024 - 16.08.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Dusty White

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    This course is intended to introduce the student to programming languages as tools for conducting data analysis, focusing on Python in particular. The course will cover basic principles of programming languages, as well as libraries useful in collecting, cleaning and analyzing data in order to answer research questions. Students will learn to use Python to apply forecasting tools and predictive models to business settings. The course will be divided between lecture and lab time, and labs will be focused on teaching students how to implement the programming techniques and statistical models discussed in lectures.

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Module 15
    2023-2024 Module 15

    Enrollment :

    The course is targeted only for the Mikkeli Campus students.

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