[Kurz] Program kurzu (obsah přednášky/semináře/rekvalifikace/studia) ...
Goals In this course you will learn:
- Understand techniques such as lambdas, NumPy, sklea> 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/SeaBo> by creating charts and graphs to better visualize your data.
- Use iPython Notebook to create Python code.
* In this course you will learn:
- Understand techniques such as lambdas, NumPy, sklea> 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/SeaBo> 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
Module 03: DataScience: Introduction to NumPy
- Vectors
- Matrix operations
- Sorting
- Indexing
- Broadcast
Module 04: DataScience: Introduction to sklea> 1
- Preprocess
- Model Select
- Pipeline
Module 05: DataScience: Introduction to sklea> 2
- Feature Selection
- Metrics
- One Hot Encoding
Module 06: Matplotlib visualization / SeaBo>
- 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