Geo-ICT Training Center, The Netherlands
200 courses, 20 online supports,
60 moocs, 30 trainees
Python and Datascience
- Planning and Registration
This Python and Datascience course lasts 4 days and costs € 1999. The course is intended for data analysts who want to use Python and the Python libraries in Data Science projects. To participate in this course knowledge and experience with any programming language or package such as SPSS, Matlab or VBA is desirable. The Python basics are covered on the first day. If you already have this knowledge, you can skip the first day of the course for a lower price.
During the Python and Datascience course, you will learn how to use the Python language and Python libraries in Data Science projects. First, the Python syntax aspects are discussed. These are important in Data Science projects. Variables, data types, functions, flow control, comprehensions, classes, modules and packages are discussed. The functioning of the Jupyter notebooks, the IPython shell and installing Python packages in Anaconda are also discussed.
Next, the NumPy package is discussed, with which large data sets can be processed very efficiently. NumPy's ndarray object and its methods are discussed. Attention is paid to the different array manipulation techniques and special routines for organizing, searching and comparing data in matrices. Also discussed is the MatPlotlib library, which is tightly integrated with NumPy and is a very powerful tool for creating and plotting complex data relationships.
Then it is the turn of pandas to use for data analysis. The pandas library introduces two new data structures in Python, which use Numpy and are therefore fast. The data structures are DataFrame and Series and it is extensively discussed that you can use them for data analysis when inspecting, selecting, filtering, combining and grouping data. Finally, attention is paid to the essentials of the SciPy library.
The course uses many practical examples and shows how one-, two- and three-dimensional data sets can be visualized.
The theory is discussed on the basis of presentations. Illustrative demos clarify the concepts. The theory is alternated with exercises. The development environment is the Anaconda distribution with Jupyter notebooks.
Learning objectives of this course:
- The student is able to use different data science libraries to perform complex analyzes
- You can independently perform analyzes in python with the different available libraries