Top Tools For Data Science Professionals

Data science consists of leveraging large datasets to extract valuable information that can be transformed into actionable business decisions. Data scientists are the talented minds that accumulate process, manipulate, clean up and analyze data to extract valuable insights.

Knowing how to use Data Science tools can help you build a successful and rewarding career in Data Science. Read on to learn about some of the key data science tools out there!

List of Data Science Tools


Data science tools and technologies are not just about databases and frameworks. Choosing the correct programming language for Data Science is very important. You can effectively perform different mathematics,  statistics and science calculations with Python.

Python is used by many data scientists for scraping; various libraries used in data science are Numpy, Pandas, Scikit-Learn etc.


R provides an extensible software environment for statistical analysis and is one of several popular programming languages used in Data Science. You can perform data cleansing and efficient visualization through R.  Clustering  and classification of the data can be carried out in less time using R.

R represents the data visually in a simple way for everyone to understand.


Pandas is fast and powerful open source, easily used data structures and analysis tools for the Python programming language.


NumPy is a Python library used to work on arrays. It also has functions to work in the area of linear algebra, fourier transform and matrix.


Scikit-learn is perhaps the most useful tool for machine learning in Python, Simple and efficient tools for predictive data analysis. Built on NumPy, SciPy, and matplotlib.


Matplotlib is also a python library for the creation of interactive visualizations. Matplotlib lets you do easy and difficult things.

  • Create graphic of publication quality.
  • Make interactive pictures which can zoom, pan, update.
  • Personalize visual style and page layout.
  • Integration into JupyterLab & GUI.


Seaborn is a data visualization library in Python. It provides a high-level interface to draw interesting and informative statistical graphs.

SQL(Structured Query Language):

SQL is a database language designed for retrieving and managing data in a relational database. Data Partitioning helps to increase efficiency to work with the normalized tables while analyzing the data flow and retrieving data by SQL statements.


Tableau provides collaborative data visualization software for business analytics.

Tableau empowers people and organizations to make the most of their data by transforming the way they use it to solve problems.


Power BI provides software services, apps and connectors to turn unrelated data sources into interactive and visual information. Data can be in an Excel spreadsheet or in a hybrid data warehouse that is cloud-based and on-premises.

The PowerBI solution allows you to easily connect to your data sources, visualize, discover, and share what’s important.

MS Excel:

MS Excel is a powerful and useful program for analyzing and documenting data. It is used to store and analyze numerical data.


TensorFlow is an open-source software library designed for artificial intelligence and machine learning. Deep neural networks can be trained and inferred using it across a range of tasks.


Keras is an open-source software library that serves as a Python interface to artificial neural networks. Keras serves as an interface to the TensorFlow library.


NLTK language toolkit is a leading platform for using human language data in Python programs.

NLTK is described as “a good tool to teach and work with computational linguistics,” and as “an incredible library to play with the natural language.


Hadoop is an open-source framework for storing and running applications over commodity hardware clusters. It provides storage of any kind of data, enormous processing power, and the ability to manage virtually unlimited concurrent tasks.

Data Science is a complex field that requires many tools for processing, analyzing, cleaning, organizing, manipulating, and interpreting data.

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