Introduction to Data Science

obowiązkowe

This course provides a brief introduction to Data Science. The main goal is to shine a light on all areas related to Data Science starting from description of most popular tasks like data wrangling and exploration, task automation and predictive modeling through explanation of true meaning of “Big Data” description of most popular data science software and finishing with data science applications and reality of everyday work.

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.

Readings and up-to-date online resources provides during the lecture.

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

Final exam, multiple choice test (100%)