Are you an aspiring data scientist? Are you inquisitive, exploring, asking questions, and always trying to learn new things? If yes online tutorials and videos can help you quench your thirst for knowledge. Online search for data is one of the best ways to get ready as a data scientist and to make sure that you have all the tools necessary for learning Python. If you are planning to learn Python here I present to you the tools that can easily galvanize your mind to become a successful and leader in Python data science.
IPython is basically a command shell in multiple programming languages. It helps users with interactive computing. IPython was developed for Python programming. It offers features like rich media, tab completion, rich history, enhanced introspection, and additional shell syntax. There are some peculiar features that will help you understand why does IPython tool considered one of the best. It has powerful interactive shell that is Qt-based and terminal. Also, it offers browser based notebook that has mathematical expressions, support for code, inline plots, and text. IPython tool also supports interactive data visualization. It also uses GUI toolkit. The flexible and embedded interpreters help users to load their projects easily. This tool offers high performance and is very easy to use.
It is the Python library which is backed by a C++ engine. GraphLab Create ensures high performance and helps build large scale products. It can analyze data of a huge size at a very good speed even when you are using it on your desktop. With this tool, you get a single platform where you can find a graphs, tabular data, images, and texts. At one place state-of-art machine learning algorithms can be found such as factorization, boosted trees, and deep learning. With flexible Ape this tools focuses on machine learning. It has a unique function of running the same code in a distributed system on your laptop with the help of Hadoop Yarn. It also offers exploration as well as production monitoring and we can visualize data easily. You can also deploy data products with the help of predictive services.
Panda is a BSD-licensed and open source library. It provides data structures and data analysis meant for Python programming language in an easy-to-use way. If you want to use a tool for data preparation and munging, it’s the best. But it is not a very favorites tool for data modelling and data analysis. It can fill gaps easily so that you can carry out your data analysis in Python without having to switch to programming languages like R. You can combine Pandas with other libraries and Python toolkit. It offers excellent performance as it is high in productivity and can collaborate with modelling functionalities like linear and panel regression.
If you’re talking about linear programming PuLP is the best Python Tool.You can use it to maximize objective functions by optimizing some constraints. PuLP is basically a linear programming modular. It helps the user to generate LP files. PuLP also helps to call optimized solvers such as CPLEX, GLPK, and COIN to solve complex linear problems.
You can never go wrong with Python 2D plotting library named Matplotlib. The ability to produce publication quality figures is one of the bestselling features of Matplotlib. With Matplotlib, you can generate figure in a variety of format and in different interactive environment across several platforms. You can use it in Python, IPython shell and Python script. Toolkit makes things easier for the users. With the help of this we can generate power spectra, bar chart, slot error chart, and scatter Plot. With just few simple lines of code, you can create plots. It also offers user full control over font properties line styles and axes properties.
Scikit Learn is a simple tool for data analysis and data mining. The very fact that everybody can easily access it makes it number one. You can create it on SciPy, matplotlib, as well as NumPy. Moreover, As it is open source, so you can use it commercially. It has a BSD license. Scikit Learn offers classification and identification of categories. It also offers regression, clustering, model selection, preprocessing, and dimensionality reduction.
Spark is basically a driver program that runs the main functions of the user. It also executes operations on a cluster in parallel. The main attraction is that it provides RDD or resilient distributed dataset. Moreover, It is a collection of elements across several nodes of a cluster. With Spark, you can operate the elements in parallel. It is the main abstraction. The second abstraction is the shared variables. Further, you can also use them in parallel operations.
These are the popular Python toolkits for aspiring data scientists.