Top 10 data science tools that every data scientist should know

Data science is a relatively new field that is constantly evolving. As data scientists, we are always on the lookout for new and better ways to do things. In this article, we will discuss the top 10 data science tools that every data scientist should know.

These tools are essential for anyone who wants to be able to effectively work with data. They will help you to clean, transform, and visualize your data. We will also discuss some of the specific benefits that each tool can offer.

So without further ado, let’s get started!

1. Introduction

2. Python

3. R

4. SQL

5. SAS

6. Excel

7. Tableau

8. Jupyter Notebooks

9. Spark

10. AWS

Python:

Python is a versatile language that can be used for both data science and web development. It is a popular choice for data scientists because it is easy to learn and has many powerful libraries.

Some of the most popular Python libraries for data science include NumPy, pandas, and scikit-learn. These libraries provide a wide range of functions that can be used to clean, transform, and analyze data.

Python is also a great choice if you want to build data-driven web applications. The Django web framework is written in Python and is a popular choice for many developers.

R:

R is another popular language for data science. It is a statistical language that is widely used for statistical analysis and data visualization.

RStudio is a popular integrated development environment (IDE) for R. It provides a console, syntax-highlighting editor, and tools for plotting, history, and debugging.

There are also many powerful R packages that can be used for data science tasks. Some of the most popular packages include dplyr, ggplot2, and tidyverse.

SQL:

SQL is a standard database query language that can be used to manipulate and query data stored in relational databases such as MySQL, PostgreSQL, and Microsoft SQL Server.

SQL can be used to perform all kinds of tasks such as insert, update, delete, and select data. It can also be used to create new tables and indexes.

If you want to become a data scientist, it is essential to learn SQL. It is a powerful tool that can be used to wrangle data and make it easier to work with.

SAS:

SAS is a commercial software package that is widely used in the business world for statistical analysis and data management.

SAS includes many features that make it easier to work with data. It can be used to clean, transform, and visualize data. SAS also has a wide range of statistical procedures that can be used for analyzing data.

If you are looking for a tool that will give you all the features you need to manage and analyze data, SAS is a good choice. However, it is important to note that SAS is a commercial software package and therefore it can be quite expensive. 

Excel:

Excel is a spreadsheet application that is part of the Microsoft Office suite of products. Excel is a powerful tool that can be used for storing, manipulating, and analyzing data.

Excel offers many features such as formulas, graphing, pivot tables, and macros. These features make Excel a versatile tool that can be used for many different purposes. 

Tableau: 

Tableau is a powerful data visualization tool that can be used to create interactive charts, graphs, and maps.

Tableau allows you to easily connect to different data sources and then create visualizations from the data. Tableau also offers many features for analyzing and exploring data.  Jupyter Notebooks: Jupyter Notebooks are web-based applications that allow you to create and share documents that contain live code, equations, visualizations, and explanatory text. 

Jupyter:

 Notebooks are often used by scientists and engineers to share their work with others. They are also a useful tool for teaching and learning code.

 Jupyter:

 Notebooks are available on many different platforms such as IBM Watson Studio, Amazon SageMaker, Azure Notebooks, Google Colaboratory, and more. 

Spark: 

Apache Spark is an open source platform for big data processing. Spark can be used to process large amounts of data in parallel with minimal effort.