Introduction to Data Science 2400-DS1DS
The lecture will cover the following topics:
• What is Data Science? What data scientists do?
• Every-day reality of data science jobs - data wrangling.
• Understanding Big Data
• Data exploration and description.
• Task automation.
• Predictive and descriptive modeling modeling.
• Machine learning and econometrics. What is the difference?
• Data world is not always flat - working with different data structures.
• Importance of soft skills.
• Data scientists toolbox. What software to use?
• Putting Data Science to work: business, science and everything in between.
Type of course
Course coordinators
Term 2024Z: | Term 2023Z: |
Learning outcomes
Knowledge:
Participants knows the fundamentals of Data Science, what is the true meaning of data science, what are the most common tasks performed by data scientists.
Additionally students know what is currently most popular software for multiple applications (data exploration and visualization, task automation, predictive modeling, etc.)
Skills:
Student is able to identify the essence of data science problem and chose the right software to solve it efficiently.
Social competence:
Participants understands the role of soft skills in the job of data scientists.
K_W01, K_U01, K_U02, K_U03, K_U04, K_U05, KS_01, K_U06
Assessment criteria
Final exam, multiple choice test (100%)
Bibliography
Readings and up-to-date online resources provides during the lecture.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: