Python is a popular programming language used in data science, IoT, AI and more. Data scientists around the world use Pyhton.
What is Data Science?
Data science is all about finding and exploring data in the real world and using that knowledge to solve business problems.
Python as a programming language has been used in data science, IoT, AI, and other technologies. Python is used as a programming language for data science because it contains costly tools from a mathematical or statistical perspective. It is one of the significant reasons why data scientists around the world use Python.
Python Libraries for Data Analysis
Python is a simple programming language to learn, and there is some basic stuff that you can do with it, like adding, printing statements, and so on. However, if you want to perform data analysis, you need to import specific libraries. Some examples include:
- Pandas - Used for structured data operations
- NumPy - A powerful library that helps you create n-dimensional arrays
- SciPy - Provides scientific capabilities, like linear algebra and Fourier transform
- Matplotlib - Primarily used for visualization purposes
- Scikit-learn - Used to perform all machine learning activities
Questions about Python? Contact:
Stavanger University Library
Research and Study Support
Before the class you must install Anaconda. Please, follow the installation link. It is available for all Windows user, Mac, and Linux users.
Workshop in Python
The library offers worshops in Python. The class is an introduction to data mining, python, NumPy and Pandas, Data Preprocessing as well as plotting and visulization. This class is only available in English.
In this class, the basics of the python programming environment will be introduced that includes fundamental python programming techniques such as reading and manipulating csv files. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on data representation, platting and charting. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Target audience: Students and staff at UiS Equipment: Your own laptop with Anaconda installed Prior knowledge needed: None Teaching method: Workshop Language: English
This workshop will cover the following areas:
- Introduction to Data Mining
- Introduction to Python (Installing Python 3.5 – Running Jupyter Notebook IDE)
- Introduction to NumPy and Pandas
- Introduction to Data Preprocessing
- Introduction to Plotting and visualization