Popular Libraries Every Data Scientist Aspirant Must know

According to a survey and research, Data Science and Machine Learning are the most popular jobs posted in today’s era. There are many subfields and specialization in both domains. But, for a beginner sometimes these words sound like a vast ocean and for them, it might be difficult to sail the ship. Furthermore, the more you dig initially at different levels the more you feel confused about the tools. Basically, this blog is intended to put all the list of libraries that are necessary for one checklist. After reading this blog, beginners will have a sigh of relief with their marked checklist and this will also boost their confidence.

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Source: Unsplash

To be a Data Scientist or Machine Leaning expert, we all know the benefits of Python programming language. Python lets you work easily and implement programs more efficiently. Also, it is a general-purpose language, which means that you can create a broad variety of applications, from web creation using Django or Flask, to data analysis using cool libraries like Scipy, Scikit-Learn, Tensor-flow, and more. The following list gives you an idea of different libraries that are widely popular and needed to be learned:

  1. Pandas
  2. Keras
  3. PyTorch
  4. TensorFlow
  5. Scikit Learn
  6. Theano
  7. Matplotlib
  8. Numpy
  9. Scipy

Let’s have a look at each of them in detail:

1. Pandas

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Source: Google

No, I am not talking about this panda. Don’t get confused. There’s a different panda for Machine Learning experts :). Let’s talk about that.

Pandas is a powerful toolkit for Python data analysis that offers high-performance, easy-to-use applications, flexible and descriptive data structures designed to make it both easy and intuitive to interact with “like” or “labeled” data. This primarily seeks to be a high-level building block in a practical language such as Python for the pragmatic analysis of real-world data.

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Source: Google

Characteristics of the Pandas:

  • Simple management of incomplete data.
  • Columns can be quickly added and removed from the data set.
  • Intuitive combining and overlapping of datasets.
  • Power to interpret tables in SQL.
  • Flexible server reshaping and pivoting.
  • Fast translation of data structures in Python and Numpy data to data frame objects

2. Keras

Keras is a Python-written, high-level neural network API capable of running on top of Tensorflow, CNTK, or Theano. It was designed to allow deep neural networks to be rapidly explored, to be able to move from the idea to the outcome with the least possible delay.

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Source: Keras

Characteristics of the Keras:

  1. It’s user-friendly, so it’s perfect for deep learning beginners. It literally offers a clear and reliable design tailored for growing use cases.
  2. This is flexible and composable.
  3. You will compose unique building blocks to convey new design concepts, such as constructing new structures, missing features, and designing state-of-the-art models.

In TensorFlow, Keras is now part of TensorFlow, so you can actually use Keras inside TensorFlow, so you don’t need to update it, you can import Python code as follows:

from tensorflow.keras.layers import Dense

3. PyTorch

PyTorch is a framework of open-source machine learning that accelerates the journey from prototyping to the deployment of output. It’s a package of Python that has two high-level functions: Computation of tensors (like Numpy) with GPU acceleration. A tape-based auto-grade structure is founded upon deep neural networks.

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Source: Pytorch

Characteristics of the PyTorch:

  1. In eager mode, PyTorch offers ease-of-use and flexibility, while seamlessly switching to graph mode in C++ runtime environments for speed, optimization, and functionality.
  2. It supports features such as multi-model serving, logging, metrics, and the creation of RESTful endpoints for application integration.
  3. It also supports distributed training.
  4. PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android.
  5. PyTorch supporting development in areas extending from computer vision to reinforcement learning.
  6. On major cloud platforms, PyTorch is well supported, providing frictionless growth and easy scaling through prebuilt images, large-scale GPU training, the ability to run models in a production-scale setting, and more.

4. TensorFlow

TensorFlow is an open-source software framework using data flow graphs for numerical processing. Graph nodes represent mathematical processes, while multidimensional data arrays called the Tensors flowing through them are represented by the edges. This modular design allows you to assign data to one or more (distributed) CPUs or GPUs.

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Source: Wikipedia

Characteristics of the TensorFlow:

  1. It provides a simple simulation (using Tensorboard) of each section of the graph that is not a choice in Numpy or Scikit-Learn.
    2. Easily training both on CPU and GPU for distributed computing.
    3. It has been developed by Google, making it quite popular among machine / deep learning engineers.

5. Scikit-Learn

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Source: ScikitLearn

Scikit-learn is a free computer learning software supporting the Python framework designed on top of Scipy. It was developed with a mind-set in software development. The main API architecture revolves around being simple to use, efficient, and scalable. This robustness makes it perfect for use in any machine learning project, particularly for beginners in Python.

Characteristics of the ScikitLearn:

  1. Clear and efficient methods for data processing, deep learning, and data review.
  2. Accessible and affordable for all.

6. Theano

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Source: theano

Theano is a Python library that helps you to easily describe, refine, and test mathematical expressions involving multi-dimensional arrays. The Deep Learning Library is a main fundamental resource.

Characteristics of the theano:

1. Optimization of tempo and stability.
2. Transparent usage of the GPU.
3. A near integration with Numpy.
4. Generating complex C application

7. Matplotlib

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Source: Google

Matplotlib is a Python plotting software that generates data across platforms in a range of hardcopy formats and virtual environments. Matplotlib can be found in a number of environments, python files, IPython containers, online application servers, jupyter notebooks, and other interactive user interface toolkits. For basic plotting, the pyplot module provides a MATLAB-like GUI, particularly when paired with IPython.

Characteristics of the matplotlib:

  1. It provides a wide variety of plots that can be generated with matplotlib library. For eg: Line plot, multiple subplots, images, Contouring and pseudocolor, histogram, path, 3-dimensional plotting, and many more.
  2. Matplotlib has simple GUI widgets that allow you to write cross-GUI figures and widgets, irrespective of the graphical user interface you are using.

8. Numpy

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Source: Google

Numpy is regarded to be one of Python’s most popular scientific computing libraries. It offers a powerful N-dimensional entity sequence. It’s simple to navigate. Moreover, complicated mathematical applications are very simple. It can also be used as an effective multi-dimensional container for generic data, in addition to its scientific uses.

Characteristics of Numpy:

  1. Many advantages are based on providing high-performance manipulation of homogenous data item sequences over Python lists.
  2. It also offers contiguous memory allocation which has the advantages of ensuring that all elements of an array are immediately accessible from the beginning of the array at a fixed offset.

9. Scipy

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Source: Google

Scipy is an open-source platform for mathematics, science, and technologies. This contains modules for statistics, modeling, convergence, linear algebra, signal and image processing, and more. Scipy is based on Numpy, which offers convenient and quick N-dimensional array manipulation.

Characteristics of Scipy:

  1. It includes syntax highlighting.
  2. It also has the ability to execute code.
  3. It provides debugging tools, autocompletion, and project management options.

Summary

To sum up, you need to begin with Scikit-Learn as a machine learning library for you as a newcomer, and then get to know the SciPy, Numpy, Pandas, and Matplotlib building blocks.
Nonetheless, you should probably start with Keras if you are a Deep Learning enthusiast, as it provides an effective simple, easy-to-use starter framework and an official high-level TensorFlow API. Theano and PyTorch are often a great choice for you, and they are widely used in also: scientific research and

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Research aspirant in Machine learning and Data Science

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