Introduction to data science
In the age of data explosion, the face of research done in scientific fields is being changed dramatically. Nowadays, scientists are dealing with massive amount of unstructured data which contains unprecedented amount of decrypted information which in turn can facilitate the break through data-driven discoveries and predictions.
That is why the demand for the “data scientists” with the ability to read through this entangled web of knowledge is on rise.
This course provides an overview to the field of data science. Throughout the course the students get familiar with the different branches of data science and they get to know of the standard steps when dealing with the data: data collection, data cleaning and integration, exploratory data analysis, graph analytics, regression, classification, clustering. They also learn about some of the programming packages of the field.
At the end of the course, the students are able to do some basic-to-medium data analysis on large data sets. The projects to be done at the end of the course include analysis of diverse types of data sets, including data on Alzheimer patients, on European football, and on air quality.
At the end of the course, the students will be able to:
- Use Python to scrape, clean, process, and visualise data
- Apply data management techniques to prepare, parse, manipulate, and store data
- Use statistical methods to summarize data and identify relationships
- Explain how to formulate new hypotheses and draw accurate conclusions from data
- Apply statistics and computational analysis to make predictions based on data
- Effectively communicate the outcome of data analysis using descriptive statistics and visualisations
This is an online course based on OpenCourseWare materials.
More information in Åbo Akademi’s course page.
You can get a digital badge after completing this course.
datatiede tiedonkäsittely visualisointi data-analytiikka luokittelu klusterointi
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