
Kurzy a certifikace Dev & Test
Python for DataScience
Cena (bez DPH)
This course focuses on Python specifically for data science, and will introduce data manipulation and cleaning techniques using the popular python pandas data science library, will continue with NumPy adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
Audience
- Software engineers
- Business analysts
- STEM background
Goals
In this course you will learn:
- Understand techniques such as lambdas, NumPy, sklearn and manipulating files
- Describe common Python functionality and features used for data science
- Query DataFrame structures for cleaning and processing
- Understand the basics of visualization using Mathplotlib/SeaBorn by creating charts and graphs to better visualize your data.
- Use iPython Notebook to create Python code.
Outline
Module 01: DataScience: Introduction to pandas 1
- DataFrames
- Insert
- Delete
- Select
Module 02: DataScience: Introduction to pandas 2
- Merging
- Conditionals
Module 03: DataScience: Introduction to NumPy
- Vectors
- Matrix operations
- Sorting
- Indexing
- Broadcast
Module 04: DataScience: Introduction to sklearn 1
- Preprocess
- Model Select
- Pipeline
Module 05: DataScience: Introduction to sklearn 2
- Feature Selection
- Metrics
- One Hot Encoding
Module 06: Matplotlib visualization / SeaBorn
- 2D plotting
- Histograms
- HeatMap
Module 07: IPython Notebook
Prerequisite
- There is no prerequisite for this course.
Technical requirements
To attend this course, you need to have:
- PC/Laptop with internet access
- Updated web browser