A Data Scientist performs analysis by identifying relevant questions, collecting data from relevant sources, data organization, transforming data to the solution, and communicating the findings for better business decisions. Apart from having appropriate qualifications and education, an aspiring data scientist must be skilled at a certain set of tools.

Data science is a broad field so different data scientists will need to have knowledge of different tools and technologies. However, there are definitely some tools which you at least need a baseline knowledge of.

Below we’ll discuss a list of tools for data science that you should be aware of.

Relational Database

A relational database is a collection of data structured in tables with attributes. The tables can be linked to each other, defining relations and restrictions, and creating what is called a data model.

This is where SQL comes in for data science. To work with relational databases, you commonly use a language called SQL. You should have knowledge of at least one relational database tool such as MySQL and Postgre.

MS Excel

Microsoft Excel is probably the most well-known tool for data science. Excel is a powerful tool for data science and it is widely used in the industry, but it has its limitations. Although for a beginner, it’s one of the best tools out there to get started.

We recommend you to really explore and learn Excel. You will be impressed by the things that you can do as a data scientist, simply with Excel.

NoSQL Database

Also known as non-relational databases, this type of data repository provides faster access to non-tabular data structures. Some examples of these structures are graphs, documents, wide columns, key values, among many others.

You should learn at least one NoSQL database such as MongoDB, Neo4j, Redis, etc.

Programming Language

SQL is an important language that you should learn for data science, but there are also a few languages that are truly data science languages. You might have guessed it, these programming languages are Python & R.

So, what makes these programming languages so different from SQL?

Python & R were specifically created with a clear focus on data science. These languages allow the developers to write programs that deal with massive data analysis, such as statistics and machine learning.

Big Data Frameworks

Big Data frameworks were created to provide some of the most popular tools used to carry out common Big Data-related tasks. There are two of the frameworks that lead the market: Hadoop and Spark.

To manipulate huge amounts of data in an effective way, you need an appropriate framework capable of computing statistics over a distributed architecture. They help to store, analyze and process the data.

Visualization Tools

There are hundreds of tools that fall into this category. The most commonly used one is the one that we’ve mentioned above (MS Excel). This is probably the best visualization tool for beginners.  

But if you’re well-versed in data science, you need something that has more capabilities, more specific tools, specially tailored for business intelligence (BI) and data analysis. This is where tools like Tableau or QlikView come in for data science. These tools offer a clean and straightforward user interface. They help analysts discover new insights from existing data through visual elements.

Scraping Tools

A data scientist needs scraping tools to extract data from various sources (especially from the web). Doing this manually would take a lot of time that could be spent on doing something more productive. This is why data scientists use web scraping tools to do web scraping.

They use automated processes, or bots, that jump from one webpage to another, extracting data from them and exporting it to different formats or inserting it in databases for further analysis. The most popular ones are ParseHub & Content Grabber.

IDEs

An ideal IDE should put together all the tools you need in your every day work as a coder: a text editor with syntax highlighting and auto-completion, a powerful debugger, an object browser, and easy access to external tools.

Besides, it must be compatible with the language of your preference, so it is a good idea to choose your IDE after knowing which language you will use. The most popular ones are Spyder, Pycharm & RStudio.


There is no hard and fast rule that the tools that are mentioned above are the only ones that you should be using. As you move into a career in data science, you will gain skills with a variety of tools and will choose ones that are best for you. Until then, develop knowledge of the methods and the domains.

If you want to learn more about Data Science tools & ones that are actually being used by Industry Experts, then you should check out our Data Science Learning Path.

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