As data science gains popularity and momentum with the evolution of technology, so too does Python, a high-level programming language that's also used for things like scripting and web development. Python's been around since the late 1980s, and it's becoming increasingly popular for use in data science, including fields like data analysis, data visualization, and machine learning. As Python continues taking the lead in data science, those working with it have found having certain tools on hand makes it easier to achieve a desired outcome. If you're an aspiring data scientist , these are some of the best Python data science tools to help you achieve your goals.
This open-source extension module makes it easier to work with matrices and sizable multidimensional arrays. It has a large collection of mathematical functions and methods that enables users to perform a variety of operations. For those who perform scientific computing, this may be one of the most useful tools to have.
Because this core Python library is based on NumPy, it's ideal for those who want to extend NumPy's capabilities. Like NumPy, it has a multidimensional array data structure that includes tools for solving complex tasks like integral calculus, probability theory, and linear algebra.
Yet another tool built on NumPy, Pandas offers high-level data structures and features designed to make fast, easy work of data analysis and data munging. Users love that they can use just one or two commands to translate complex operations. Other benefits include its integrated methods of combining, sorting, grouping, and filtering data.
Scikit-Learn is based on SciPy, and it's designed specifically for machine learning. It comes with many of the most popular machine learning algorithms, making it particularly useful for those just starting machine learning projects. Its algorithms also make easy work of data mining tasks like clustering, dimensionality reduction, and regression.
Matplotlib and Seaborn
Matplotlib's ideal for building chats, histograms, and scatter plots. It's a low-level library designed to help Python users create two-dimensional graphs and diagrams. For those looking for a richer visualization gallery and better settings for processing charts, Seaborn, which is based on Matplotlib, may be a better option.
Having a strong educational foundation is another great Python data science tool. From free courses to Nanodegree programs designed to immerse you in the field and get you (and your portfolio) career ready, Udacity is here to help you take your data science career to the next level. Check out a Udacity Nanodegree program today.