NumPy

Python is a powerful and versatile programming language that has become increasingly popular in the field of data science. One of the main reasons for its popularity is the vast array of libraries and packages available for data manipulation, analysis, and visualization.

10 Python packages for data science and machine learning

In this article, we will highlight some of the top Python packages for data science that aspiring and practicing data scientists should consider adding to their toolbox. 

1. NumPy 

NumPy is a fundamental package for scientific computing in Python. It supports large, multi-dimensional arrays and matrices of numerical data, as well as a large library of mathematical functions to operate on these arrays. The package is particularly useful for performing mathematical operations on large datasets and is widely used in machine learning, data analysis, and scientific computing. 

2. Pandas 

Pandas is a powerful data manipulation library for Python that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data easy and intuitive. The package is particularly well-suited for working with tabular data, such as spreadsheets or SQL tables, and provides powerful data cleaning, transformation, and wrangling capabilities. 

3. Matplotlib 

Matplotlib is a plotting library for Python that provides an extensive API for creating static, animated, and interactive visualizations. The library is highly customizable, and users can create a wide range of plots, including line plots, scatter plots, bar plots, histograms, and heat maps. Matplotlib is a great tool for data visualization and is widely used in data analysis, scientific computing, and machine learning. 

4. Seaborn 

Seaborn is a library for creating attractive and informative statistical graphics in Python. The library is built on top of Matplotlib and provides a high-level interface for creating complex visualizations, such as heat maps, violin plots, and scatter plots. Seaborn is particularly well-suited for visualizing complex datasets and is often used in data exploration and analysis. 

5. Scikit-learn 

Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines. The library is built on top of NumPy and Pandas and is designed to be easy to use and highly extensible. Scikit-learn is a go-to tool for data scientists and machine learning practitioners. 

6. TensorFlow 

TensorFlow is an open-source software library for dataflow and differentiable programming across various tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. TensorFlow was developed by the Google Brain team and is used in many of Google’s products and services. 

7. SQLAlchemy

SQLAlchemy is a Python package that serves as both a SQL toolkit and an Object-Relational Mapping (ORM) library. It is designed to simplify the process of working with databases by providing a consistent and high-level interface. It offers a set of utilities and abstractions that make it easier to interact with relational databases using SQL queries. It provides a flexible and expressive syntax for constructing SQL statements, allowing you to perform various database operations such as querying, inserting, updating, and deleting data.

8. OpenCV

OpenCV (CV2) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage and is now maintained by Itseez. OpenCV is available for C++, Python, and Java. 

9. urllib 

urllib is a module in the Python standard library that provides a set of simple, high-level functions for working with URLs and web protocols. It includes functions for opening and closing network connections, sending and receiving data, and parsing URLs. 

10. BeautifulSoup 

BeautifulSoup is a Python library for parsing HTML and XML documents. It creates parse trees from the documents that can be used to extract data from HTML and XML files with a simple and intuitive API. BeautifulSoup is commonly used for web scraping and data extraction. 

Wrapping up 

In conclusion, these Python packages are some of the most popular and widely-used libraries in the Python data science ecosystem. They provide powerful and flexible tools for data manipulation, analysis, and visualization, and are essential for aspiring and practicing data scientists. With the help of these Python packages, data scientists can easily perform complex data analysis and machine learning tasks, and create beautiful and informative visualizations. 

If you want to learn more about data science and how to use these Python packages, we recommend checking out Data Science Dojo’s Python for Data Science course, which provides a comprehensive introduction to Python and its data science ecosystem. 

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  

What is GitHub?

GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more. It is an excellent resource for beginners who are just starting with data science, analytics, and engineering. There are thousands of open-source repositories available on GitHub that provide code examples, datasets, and tutorials to help you get started with your projects.   

This blog lists some useful GitHub repositories that will not only help you learn new concepts but also save you time by providing pre-built code and tools that you can customize to fit your needs. 

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Best GitHub repositories to stay ahead of the tech Curve

With GitHub, you can easily collaborate with others, share your code, and build a portfolio of projects that showcase your skills.  

1. Scikit-learn: A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.  

Link to the repository: https://github.com/scikit-learn/scikit-learn  

2. TensorFlow: An open-source machine learning library developed by Google Brain Team. TensorFlow is used for numerical computation using data flow graphs.  

Link to the repository: https://github.com/tensorflow/tensorflow  

3. Keras: A deep learning library for Python that provides a user-friendly interface for building neural networks. It can run on top of TensorFlow, Theano, or CNTK.  

Link to the repository: https://github.com/keras-team/keras  

4. Pandas: A Python library for data manipulation and analysis. It provides a range of data structures for efficient data handling and analysis.  

Link to the repository: https://github.com/pandas-dev/pandas  

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5. PyTorch: An open-source machine learning library developed by Facebook’s AI research group. PyTorch provides tensor computation and deep neural networks on a GPU.  

Link to the repository: https://github.com/pytorch/pytorch  

6. Apache Spark: An open-source distributed computing system used for big data processing. It can be used with a range of programming languages such as Python, R, and Java.  

Link to the repository: https://github.com/apache/spark  

7. FastAPI: A modern web framework for building APIs with Python. It is designed for high performance, asynchronous programming, and easy integration with other libraries.  

Link to the repository: https://github.com/tiangolo/fastapi  

8. Dask: A flexible parallel computing library for analytic computing in Python. It provides dynamic task scheduling and efficient memory management.  

Link to the repository: https://github.com/dask/dask  

9. Matplotlib: A Python plotting library that provides a range of 2D plotting features. It can be used for creating interactive visualizations, animations, and more.  

Link to the repository: https://github.com/matplotlib/matplotlib 

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10. Seaborn: A Python data visualization library based on matplotlib. It provides a range of statistical graphics and visualization tools.  

Link to the repository: https://github.com/mwaskom/seaborn 

11. NumPy: A Python library for numerical computing that provides a range of array and matrix operations. It is used extensively in scientific computing and data analysis.  

Link to the repository: https://github.com/numpy/numpy  

12. Tidyverse: A collection of R packages for data manipulation, visualization, and analysis. It includes popular packages such as ggplot2, dplyr, and tidyr.  

Link to the repository: https://github.com/tidyverse/tidyverse  

In a nutshell

In conclusion, GitHub is a valuable resource for developers, data scientists, and engineers who are looking to stay ahead of the technology curve. With the vast number of repositories available, it can be overwhelming to find the ones that are most useful and relevant to your interests. The repositories we have highlighted in this blog cover a range of topics, from machine learning and deep learning to data visualization and programming languages. By exploring these repositories, you can gain new skills, learn best practices, and stay up-to-date with the latest developments in the field.

Do you happen to have any others in mind? Please feel free to share them in the comments section below!  

 This blog covers the 6 famous Python libraries for data science that are easy to use, have extensive documentation, and can perform computations faster.

Data scientist is the sexiest job of the 21st century, but what is a data scientist without data? Harvard Business Review labels data as the new oil. There is a massive dearth of people qualified for data-related jobs. As a beginner, you can be tempted to wet your feet in the ever-evolving field of data science.

However, Python is a programming language that can be easily learned. Sometimes, your pseudocode can directly be converted into Python code.

Python is increasingly used in data science-related tasks and is becoming the de-facto standard because it is easy to learn, easy to debug, has a rich userbase, is object-oriented, and is easy to interpret. However, you can get lost in the intricacies and subtleties of the many available specialized packages.

Fret not, because we have you covered!

You might be tempted to learn about many of these libraries, but there are some libraries that are frequently used in the domain of data science given their versatility and ease of use.

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In this blog, we will be going over the 6 most commonly used python libraries for data science:

NumPy

Be it the creation of vectors and arrays, performing some matrix multiplication, or performing singular value decomposition, NumPy is a linear algebra-based library that provides a vast repertoire of mathematical routines at your disposal. NumPy is a library that deals with vectors, and matrices and offers fast operations. It provides various functions such as array indexing and broadcasting, consumes less memory, and is convenient.

Behind the hood, it uses multiple optimization algorithms to accelerate typically slow operations such as matrix multiplication. The automatic broadcasting takes care of different array sizes and makes life very convenient ultimately making it one of the most famous Python libraries for data science.

Pandas

Handling complex data, indexing into the data, cleaning and handling null values, merging and joining datasets, Pandas is a python library that is both easy and intuitive. Since it is built on top of NumPy, it can perform tasks that would otherwise take a lot of time. Usually, by using native Python functionality, it becomes tough to iterate over thousands of tuples to perform some pre-processing, but by using Pandas’ wrappers, these tasks can be done in significantly less time.

Moreover, Pandas is widely used for data analysis and looking into the summary statistics, and inferring some patterns from data, which can help answer or validate our assumptions and hypothesis.

SciKit-Learn

If you want to train complex machine learning models or have an ensemble of different machine learning models with an intuitive and easy-to-use interface, Scikit-learn is your friend. The beauty of Scikit-learn is that it provides a similar interface for every machine learning algorithm, which makes the library very intuitive to use and can easily extend the current learning algorithms by using custom cost functions and optimization algorithms.

The library also offers various optimization algorithms to tune the model’s hyperparameters. Therefore, Scikit-learn stays one of the most popular machine learning libraries for Python.

Keras

Machine learning and deep learning have become immensely popular in recent days due to ever-increasing computing power and that is why you see complicated models being developed, and Keras is a Python library for data science to do that. Keras is a static graph-based machine learning library. One of the distinguishing features is that the computational graph of a network, once formed, will be fixed, and will not be changed on the run-time, which means that the variables will be locked at the run time, making the models very efficient.

Moreover, the Keras application programming interface is highly abstracted, which makes Keras very easy to use once you have a good grasp of Python. It is used to build custom machine learning models and is widely used in the machine learning community for research and deployment purposes.

SciPy

Testing whether your assumption is valid or not to make a fundamental decision about a product’s life cycle is an important task. As SciPy is written in various low-level languages such as C, C++, and Fortran, the speed gains are tremendous compared to a library written in a high-level language. Moreover, Scipy extends the functionality of NumPy by providing access to structures that can be used to store sparse data in a highly optimized fashion and perform computations on it.

The open-source nature of Scipy allows anyone to look at the source code, find bugs or optimize the numerical algorithms further. Hence, SciPy remains one of the most popular libraries for statistical tasks.

PyTorch

PyTorch is a dynamic graph-based machine learning library developed by Facebook to aid in their model development and deployment purposes. The variables, including layers, can be changed during the iterations, making the neural networks easier to debug and providing more flexibility. Moreover, for people having access to GPUs, this library offers a remarkably simple flag to switch between GPU and CPU, which makes the life of programmers extremely easy by making the code portable.