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klein b data analysis python numpy matplotlib pandas 2021

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Uploaded On:: Jan. 9, 2023, 9:21 a.m.
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Textbook in PDF format 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

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Klein B. Data Analysis With Python. Numpy, Matplotlib and Pandas 2021.pdf 61.3 MB

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klein b data analysis python numpy matplotlib pandas 2021

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