As a data scientist, you have access to a vast array of tools and technologies that can help you analyze and understand data. From programming languages and libraries to data visualization and machine learning tools, there are many options to choose from.

In this post, we'll take a look at the top 10 data science tools that every data scientist should know. These tools are widely used in the industry and can help you get started in data science or take your skills to the next level.

Python

Python is a powerful programming language that is widely used in data science. It has a large and active community, and there are many libraries and frameworks available for data analysis, machine learning, and data visualization. Some of the most popular Python libraries for data science include NumPy, pandas, and scikit-learn.

R

R is another popular programming language for data science. It has a large number of packages and libraries specifically designed for data analysis and visualization. R is particularly useful for statistical analysis and is often used in academia.

SQL

Structured Query Language (SQL) is a programming language used to manage and manipulate data stored in relational databases. As a data scientist, you’ll likely be working with large amounts of data stored in databases, so it’s important to know SQL to be able to retrieve and manipulate the data you need.

Excel

Excel is a spreadsheet program that is widely used for data analysis. It may seem basic, but it has many powerful features that can be useful for data manipulation and visualization. Excel is also a good tool for quick prototyping and experimentation.

Jupyter

Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. With its two variations JupyterLab and Jupyter Notebook, it’s a great tool for data exploration and prototyping and is often used in data science projects.

Machine learning platforms

There are many machine learning platforms available that allow you to build, deploy, and manage machine learning models. Some popular options include Azure Machine Learning, Google Cloud ML Engine, and Amazon SageMaker.

These days, there is a rise of Low-Code and No-Code tools noticeable that might change the daily work of data scientists completely. More and more tools are used by professionals and non-professionals to build machine learning models to solve tasks. The consequences of increased data science tool usage are described in my other article on Medium.

Tableau

Tableau is a powerful data visualization tool that allows you to create interactive charts, graphs, and maps. It’s user-friendly and can be used to quickly create compelling visualizations for presentations and reports.

Power BI

Power BI is a business intelligence platform that allows you to create and share data-driven insights. It has a range of tools for data visualization, data modeling, and report creation, and is particularly useful for creating interactive dashboards.

Collaboration and version control tools

As a data scientist, you’ll often be working on projects as part of a team. Collaboration and version control tools like Git and GitHub can help you work together effectively and keep track of changes to your code and data.

To ensure efficient information providence by writing meaningful commit messages, read my blog article on Medium about my recommendations.

Google Analytics

Google Analytics is a web analytics service that provides insights into website traffic and user behavior. As a data scientist, you may be asked to analyze website data to understand user behavior and improve website performance.

Conclusion

These are just a few of the many data science tools available, but they are some of the most essential. Whether you're just starting in data science or you're an experienced professional, it's important to have a strong understanding of these tools and how to use them effectively.

Additionally, don’t see the different tools as separate pieces but as a connected data science set. Gather experience in each of them and focus on how to use them together as a tool stack, afterward. It’s not about tool specialization but choosing the right tools that work best with each other to solve the specific data science task.

Which tool do you miss in this list?