Basic knowledge of Python
After this course you will be able to write your own programs for processing data with NumPy and optimize them.
Analysts, researchers and engineers who would like to carry out numerical calculations on their data.
NumPy is the most frequently used Python library for processing numerical data. It combines a programmer-friendly Python interface with the speed of an implementation in pure C. NumPy allows to implement calculations with large data series and matrices with few lines of code. Therefore, NumPy is a perfect fit to optimize the runtime of Python programs.
In this course you will get a hands-on introduction of NumPys essentials, featuring many practical examples. To build upon the basic functionality, the SciPy package featuring a plethora of mathematical tools that make the best use of NumPy will be covered as well.
14 hours
| Day 1 | Day 2 |
|---|---|
| Introduction to NumPy | Broadcasting |
| Functions / ufuncs | Optimization with NumPy |
| Indexing | The Scipy Library |
| Typical Applications | Related Python Libraries |
- overview of the functionality in NumPy
- arrays
- dtypes
- reshape
- creating arrays
- loading/saving data
- indexing arrays
- views
- fancy indexing
- sorting
- set operations
- built-in functions
- ufuncs
- matrix operations
- rotating coordinates
- eliminating Python loops with NumPy
- sparse matrices
- identifying bottlenecks with cProfile
- recommender systems
- Eigenvectors
- the PageRank algorithm
- implementing a neural network in NumPy
- broadcasting
- stacking
- raveling
- finding zeroes
- fitting polynomial functions
- Fourier transformation
- data visualization
- pandas
- statistics with statsmodel
- machine learning with scikit-learn
- solving linear equations with Pulp and Gurobi
- Spark
- Dask