ABL-E1300 - Big Data Analysis in Accounting, Lecture, 26.2.2024-19.4.2024

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

The course is intended to provide the student with the basics of applying data analytics in accounting. After completing the course, students will be able to:

  • Gain a managerial overview of the potential uses of big data in accounting contexts
  • Extract, cleanse, and transform heterogeneous data into machine-readable form
  • Analyze data to create insights for strategic and operational decision-making
  • Understand the potential and pitfalls of forecasting and machine learning techniques
  • Use Python programming language and implement Python modules for data analysis

Credits: 6

Schedule: 26.02.2024 - 19.04.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jukka Sihvonen

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:

    The course is an introduction to big data methods and data science with an emphasis on the concepts and techniques most relevant to accounting analytics. The secondary aim of this course is to acquaint students with Python programming language and its rich ecosystem for data analytics. The general topics of the course are:

    • Handling large unstructured datasets
    • Regression and classification (machine learning)
    • Prediction: framework, applications, and evaluation

    To achieve these learning objectives, a combination of lectures, tutorials, online training, in-class exercises, and assignments will be utilized.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    • Assignments
    • Lecture diary
    • Course exam

Workload

  • valid for whole curriculum period:

    • Lectures, tutorials, and exercises
    • Assignments and learning self-reflections
    • Exam
    • Independent work

DETAILS

Study Material

  • valid for whole curriculum period:

    • Material distributed by the instructor
    • Wes McKinney (2017). Python for Data Analysis, 2nd Ed. O Reilly Media
    • Joel Grus (2019). Data Science from Scratch, O Reilly Media
    • Online study resources defined by the instructor

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    It is recommended to bring your own laptop to the lectures. Work with your own laptop during the course if possible.

    Teaching Language : English

    Teaching Period : 2022-2023 Spring IV
    2023-2024 Spring IV

    Enrollment :

    Registration for courses: in Sisu.


    Max. 60 students. If more students have enrolled by the enrolment deadline than can be accepted on the course, priority will be given to students based on their study right: 1. Accounting MSc students 2. BIZ exchange students 3. Bachelor's students in Accounting who have completed more than 150 cr 4. other BIZ MSc students 5. BIZ Bachelor's students in other majors who have completed more than 150 cr

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof