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This tutorial can be used as an online course on Numerical Python as it is needed by Data Scientists and Data Analysts. Data science is an interdisciplinary subject which includes for example statistics and computer science, especially programming and problem solving skills. Data Science includes everything which is necessary to create and prepare data, to manipulate, filter and clense data and to analyse data. Data can be both structured and unstructured. We could also say Data Science includes all the techniques needed to extract and gain information and insight from data.
Data Science is an umpbrella term which incorporates data analysis, statistics, machine learning and other related scientific fields in order to understand and analyze data. Another term occuring quite often in this context is "Big Data". Big Data is for sure one of the most often used buzzwords in the software-related marketing world. Marketing managers have found out that using this term can boost the sales of their products, regardless of the fact if they are really dealing with big data or not. The term is often used in fuzzy ways.
Python is a general-purpose language and as such it can and it is widely used by system administrators for operating system administration, by web developpers as a tool to create dynamic websites and by linguists for natural language processing tasks. Being a truely general-purpose language, Python can of course - without using any special numerical modules - be used to solve numerical problems as well. So far so good, but the crux of the matter is the execution speed. Pure Python without any numerical modules couldn't be used for numerical tasks Matlab, R and other languages are designed for. If it comes to computational problem solving, it is of greatest importance to consider the performance of algorithms, both concerning speed and data usage.
If we use Python in combination with its modules NumPy, SciPy, Matplotlib and Pandas, it belongs to the top numerical programming languages. It is as efficient - if not even more efficient - than Matlab or R.
Numpy Tutorial
Numpy Tutorial: Creating Arrays
Data Type Objects, dtype
Numerical Operations on Numpy Arrays
Numpy Arrays: Concatenating, Flattening and Adding Dimensions
Python, Random Numbers and Probability
Weighted Probabilities
Synthetical Test Data With Python
Numpy: Boolean Indexing
Matrix Multiplicaion, Dot and Cross Product
Reading and Writing Data Files
Overview of Matplotlib
Format Plots
Matplotlib Tutorial
Shading Regions with fill_between
Matplotlib Tutorial: Spines and Ticks
Matplotlib Tutorial, Adding Legends and Annotations
Matplotlib Tutorial: Subplots
Exercise
Exercise
Matplotlib Tutorial: Gridspec
GridSpec using SubplotSpec
Matplotlib Tutorial: Histograms and Bar Plots
Matplotlib Tutorial: Contour Plots
Introduction into Pandas
Data Structures
Accessing and Changing values of DataFrames
Pandas: groupby
Reading and Writing Data
Dealing with NaN