AI News, Why is Python a language of choice for data scientists?

Why is Python a language of choice for data scientists?

Python has a solid claim to being the fastest-growing major programming language but remember that it’s all up to you to choose the best programming language.

Mlpy provides a wide range of machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise between modularity, maintainability, reproducibility, usability and efficiency. NumPy is the fundamental package for scientific computing with Python, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.

It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. TensorFlow is an open source software library for machine learning across a range of tasks, developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use.

Top 15 Python Libraries for Data Science in 2017

As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience.

When starting to deal with the scientific task in Python, one inevitably comes for help to Python’s SciPy Stack, which is a collection of software specifically designed for scientific computing in Python (do not confuse with SciPy library, which is part of this stack, and the community around this stack).

However, the stack is pretty vast, there is more than a dozen of libraries in it, and we want to put a focal point on the core packages (particularly the most essential ones).

There are two main data structures in the library: “Series” — one-dimensional “Data Frames”, two-dimensional For example, when you want to receive a new Dataframe from these two types of structures, as a result you will receive such DF by appending a single row to a DataFrame by passing a Series: Here is just a small list of things that you can do with Pandas: Another SciPy Stack core package and another Python Library that is tailored for the generation of simple and powerful visualizations with ease is Matplotlib.

However, the library is pretty low-level, meaning that you will need to write more code to reach the advanced levels of visualizations and you will generally put more effort, than if using more high-level tools, but the overall effort is worth a shot.

Efficiency and stability tweaks allow for much more precise results with even very small values, for example, computation of log(1+x) will give cognizant results for even smallest values of x.

The functionality of NLTK allows a lot of operations such as text tagging, classification, and tokenizing, name entities identification, building corpus tree that reveals inter and intra-sentence dependencies, stemming, semantic reasoning.

Gensim implements algorithms such as hierarchical Dirichlet processes (HDP), latent semantic analysis (LSA) and latent Dirichlet allocation (LDA), as well as tf-idf, random projections, word2vec and document2vec facilitate examination of texts for recurring patterns of words in the set of documents (often referred as a corpus).

It was originally designed strictly for scraping, as its name indicate, but it has evolved in the full-fledged framework with the ability to gather data from APIs and act as general-purpose crawlers.

The library follows famous Don’t Repeat Yourself in the interface design — it prompts its users to write the general, universal code that is going to be reusable, thus making building and scaling large crawlers.

As you have probably guessed from the name, statsmodels is a library for Python that enables its users to conduct data exploration via the use of various methods of estimation of statistical models and performing statistical assertions and analysis.

Among many useful features are descriptive and result statistics via the use of linear regression models, generalized linear models, discrete choice models, robust linear models, time series analysis models, various estimators.

The library also provides extensive plotting functions that are designed specifically for the use in statistical analysis and tweaked for good performance with big data sets of statistical data.

And here are the detailed stats of Github activities for each of those libraries: Source: Google Spreadsheet Of course, this is not the fully exhaustive list and there are many other libraries and frameworks that are also worthy and deserve proper attention for particular tasks.

Julia vs. Python: Julia language rises for data science

But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”—Python isn’t fast or convenient enough.

Created in 2009 by a four-person team and unveiled to the public in 2012, Julia is meant to address the shortcomings in Python and other languages and applications used for scientific computing and data processing.

We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell.

A gallery of interesting Jupyter Notebooks

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file.

These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages): The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language.

The interactive plotting library Nyaplot has some case studies using IRuby: This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication.

7 Steps to Mastering Machine Learning With Python

This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way.

Fortunately, due to its widespread popularity as a general purpose programming language, as well as its adoption in both scientific computing and machine learning, coming across beginner's tutorials is not very difficult.

If you have no knowledge of programming, my suggestion is to start with the following free online book, then move on to the subsequent materials: If you have experience in programming but not with Python in particular, or if your Python is elementary, I would suggest one or both of the following: And for those looking for a 30 minute crash course in Python, here you go: Of course, if you are an experienced Python programmer you will be able to skip this step.

Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least.

The good news is that you don't need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.

For example, when you come across an exercise implementing a regression model below, read the appropriate regression section of Ng's notes and/or view Mitchell's regression videos at that time.

good approach to learning these is to cover this material: This pandas tutorial is good, and to the point: You will see some other packages in the tutorials below, including, for example, Seaborn, which is a data visualization library based on matplotlib.

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