Python Libraries for Data Science

Python is such a popular programming language among data scientists because of its beginner-friendly syntax and the support of libraries that we get for all data science tasks. So there are some of the Python libraries you need to learn for data science, with that being said, in this article I will introduce you to all the Python libraries you need to learn for data science.

Python Libraries for Data Science

Whenever you start working on a data science task you have to go through some of the steps starting from data collection, data processing, data visualization and then using machine learning algorithms on your data. So most of the times you get the data but sometimes you have to start from scraping the data. So here I will introduce you to all the Python libraries that you need for data science which will help you from scarping the data to implementing machine learning algorithms.

Web Scraping:

Web scraping is the process of collecting data from a webpage. If you are working on an issue and you don’t have the most appropriate data, you need to collect the data yourself. So here are the libraries that will help you scrape data while working on a data science task:

  1. Scrapy
  2. BeautifulSoup
  3. Requests

Data Processing:

After getting your data the next step is to understand what type of data you are using to solve a problem and then you have to prepare that data so that you can identify patterns from it by visualizing the data. So below are the libraries that you need to learn for data processing while working on a data science task:

  1. NumPy
  2. Pandas
  3. Scipy

Data Visualization:

Data visualization is one of the most important steps while working on a data science task. It helps you to understand the hidden patterns in your data as we humans can understand visuals better than numbers. So below are the libraries that you need to learn for data visualization while working on a data science task:

  1. Matplotlib
  2. Seaborn
  3. Plotly

Machine Learning:

Using machine learning algorithms helps a business solve the major problems it faces every day in providing services to its customers. Using machine learning helps an application or a website to improve its productivity by providing a better user experience by understanding the needs and interests of each customer. So below are the libraries that will help you to implement machine learning algorithms while working on a data science task:

  1. Scikit-learn
  2. Keras
  3. TensorFlow
  4. PyTorch

Summary

So below are all the Python libraries that you need to learn for data science:

  1. Scrapy
  2. BeautifulSoup
  3. Requests
  4. NumPy
  5. Pandas
  6. Scipy
  7. Matplotlib
  8. Seaborn
  9. Plotly
  10. Scikit-learn
  11. Keras
  12. TensorFlow
  13. PyTorch

Besides all the libraries mentioned above, there are more libraries that we can use in a data science task, but learning other libraries is just like upgrading your skillset while solving a particular problem. So these libraries are enough to become a Data Scientist. I hope you liked this article on all the Python libraries that you need for data science. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

Coder with the ❤️ of a Writer || Data Scientist | Solopreneur | Founder

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