Programs

Data Science

Introduction to Data Science

  • Class 15
  • Practice 30
  • Independent work 45
Total 90

Course title

Introduction to Data Science

Lecture type

Obligatory

Course code

12438

Semester

1

ECTS

3

Lecturers and associates

Course objectives

Introduce students with broad perspectives of conceptual models, knowledge-based skills, process and culture of data science and its application in the business environment. With basic knowledge of data science, introduce students with the most used current programming languages and prepare them for efficient use of programming languages throughout the rest of the study and in practice.

Content

Introduction to Data Science. Definition of terms. Application of data science. Examples of business contribution of data science. Business process flow. Exits from individual phases. Data scientist knowledge and skills. Description of applicable techniques for data project phases. Methods of evaluation of applied techniques. Trends and Future.

Required reading

Course handbook prepared and printed by Algebra University College

Additional reading

Thomas W. Miller: Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science

Minimal learning outcomes

  • Explain the purpose of data science
  • Explain the process flow in data science (data collection, data preparation, analysis)
  • Explain the application of method to slove specific problem
  • Understand the application of different procedures in addressing business challenges
  • Create a simple software solution that solves a given part of the data problem

Preferred learning outcomes

  • Evaluate the contribution of data science.
  • Directly apply and define the steps to be taken in the observed business case
  • Optimally define the application of data science method
  • Assess the acceptability of individual procedures in solving business challenges
  • Create a software solution that solves a given part of the data problem
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