Overview of Data Science Libraries
This is a list of the basic of the libraries to start off with data science. It's a starter page for me figuring out how to start doing data research and machine learning.
Anaconda
Anaconda Program that allows a user to download and manage data science software and libraries. That includes all manner of data science learning. Things like machine learning, dta visualization, and natural language processing.
More than 250 of the most commonly used open source data science and machine learning packages are installed when a user installs Anaconda.
This can be a time saver by downloading everything at once.
Pandas
Pandas A python library used for data wrangling and data manipulation. It allows a user to read data in, change it, look for missing values, read data out. The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.
There's built in data plotting with Matplotlib.
NumPy
NumPy A core package for scientific computing with Python.
NumPy is great for fast data computation. Pandas actually uses NumPy under the hood for some of its calculations. NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.
It can be used for indexing and sorting, linear algebra, as well as other types of data calcuations.
Matplotlib
For data visualizations. The most common ones will both be able to help you produce some nice visuals. This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.
Seaborn
Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions, and other graphs.
Statsmodels
This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.
SciPy
SciPy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks. The SciPy library consists of a specific set of fundamental scientific and numerical tools for Python.
There's a bunch of fancy math things that it's capable of that are called efficient numerical routines. Some listed numerical routines are numerical integration, interpolation, optimization, linear algebra, and statistics.
Requests
This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.
Machine Learning Libraries
Scikit-learn
Scikit-learn This popular machine learning library is a one-stop-shop for all of your machine learning needs with support for both supervised and unsupervised tasks. Some machine learning algorithms available are logistic regression, k-nearest neighbors, support vector machine, random forest, gradient boosting, k-means, DBSCAN, and principal component analysis.
For machine learning because it has all the algorithms you'll want to use for regression, classification, and unsupervised learning. When you’re deep in the Immersive Data Science Bootcamp, you’ll be leveraging Scikit-Learn pretty heavily.
Tensorflow
JavaScript
Tensorflow is a high-level library for building neural networks. Since it was mostly written in C++, this library provides us with the simplicity of Python without sacrificing power and performance. However, working with raw Tensorflow is not suited for beginners.
Keras
JavaScript
Keras is a popular high-level API that acts as an interface for the Tensorflow library. It’s a tool for building neural networks using a Tensorflow backend that’s extremely user friendly and easy to get started with.
Pytorch
Python
Pytorch is another framework for deep learning created by Facebook’s AI research group. It provides more flexibility and speed than Keras, but since it has a low-level API, it is more complex and may be less beginner-friendly than Keras.