Data Science Tools

Data science is one of the best career options of the 21st century. You need to learn many skills to become a Data Scientist like Python, SQL, data visualization and many more. In addition to these skills, there are many tools that a Data Scientist uses in the complete data science lifecycle. In this article, I’ll walk you through the most important data science tools you should learn.

What are Data Science Tools?

If you are a data science enthusiast then you must have started by learning some of the key skills you need to become a data scientist such as Python, SQL, Data Science Fundamentals, Working with Data, data visualization, understanding the life cycle of data science, communication and many more.

All the skills you learn to become a data scientist are the skills you need to become a data scientist. But to perform every task as a data scientist, you need to use professional frameworks and applications to work with big data more effectively. These apps and frameworks are nothing but the tools you need to become a data scientist. In the section below, I’ll introduce you to the most important data science tools you should learn.

Data Science Tools You Should Learn

Hope you now understand what is the difference between data science skills and tools. Simply put, data science skills are the skills that help you understand and the complete life cycle of data science, and data science tools are the apps and frameworks you need to execute your skills and work with data. Below are the most important tools you need to learn:

  1. Apache Hadoop: It is an open-source framework that is a collection of many software utilities for data processing.
  2. SAS: It is a statistical tool that is used for data management, data analytics, business intelligence, predictive analysis and many more.
  3. Tableau: It is a very powerful tool used for data visualization. It is mainly used by a Business Analyst to do reporting by using interactive dashboards.
  4. Matplotlib: It is a Python library that is used for data visualization.
  5. Scikit-Learn: It is also a Python library used for implementing machine learning algorithms.
  6. TensorFlow: It is a Python framework that is mainly used for deep learning.
  7. Apache Spark: It is an open-source analytics tool that is used for big data processing.

Summary

In this article, I introduced you to the 7 most important tools that you should learn to become a data scientist. I hope you will now never get confused about the difference between skills and tools you need to become a data scientist. I hope you liked this article, feel free to ask your valuable questions in the comments section below.

Default image
Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1126

Leave a Reply