15 Must-Know Data Science Tools for Beginners (2023)
Getting into Data Science and landing your first job can be trickier than it looks. There are many tools, skill-sets, and subareas that you can work with when starting to work with data, and if you’re not familiar with them, choosing the right one for you can be confusing.
In this article, we’ll take a look at fifteen key data science tools that will help on your data science journey. We’ll start with the most common ones, then we'll provide options that go beyond the traditional data analysis toolkit.
Python
To get started in the world of data science, you should learn and master a programming language — they are the key to various data science functions.
Python is one of the greatest options available to you — you’ll be able to manage the entire data analysis workflow with only that programming language, if that's your goal.
According to Stack Overflow, Python is currently the most popular programming language in the world, which makes it worth learning.
Python is known for its versatility and easier learning curve, compared to other languages. While the easier learning curve comes mostly from the clean and simple syntax, the versatility is in the number of open-source libraries, which enable you to do many things.
You can take advantage of the following libraries, for example:
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The power of pandas to manipulate data in any way you can imagine.
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The flexibility of matplotlib to create beautiful charts.
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The completeness of scikit-learn for machine learning.
You can also do the following:
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Build APIs to deploy a machine learning model online with FastAPI, a web framework.
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Build a simple front-end application using nothing but Python code with streamlit.
R
Similar to Python, R is a famous programming language for working with data — it’s mostly recognized for its scientific and statistical applications.
When programming in R, you can use various packages, which will provide you with great flexibility for performing data science activities.
You can take advantage of some of the following packages:
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Perform data wrangling in general with dplyr and use ggplot2 to create any kind of chart you might need.
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Create, train, and test machine learning algorithms easily and even deploy them on a web app using Shiny.
You have two powerful programming language options available to you. While some might think of them as rivals, you could master one of them and then try to get a good knowledge of the other — it will put you a few steps ahead when looking for a job in the data field.
Here is an objective comparison of the two programming languages.
Jupyter Notebook
Jupyter notebooks are web-based interfaces for running everything from simple data manipulation to complex data science projects, including creating data visualization and documentation.
Maintained by the Project Jupyter organization, Jupyter notebooks support Python, R, and the Julia programming language.
Here are its biggest advantages:
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You can run code directly in the browser
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You can run different parts of the code separately
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You can get the output of each one before moving to the next, which makes the data science workflow much simpler.
Notebooks also support displaying results as HTML, LaTeX, and SVG, and also creating text using Markdown and LaTeX to document your entire data science process.
Make sure to check this beginner's tutorial to learn Jupyter Notebook. If you already know your way around, this advanced tutorial and this list of tricks and shortcuts might be useful.
SQL
Once you start to know your way around the data analysis workflow, you’ll occasionally realize the need to interact with databases, which is where most of the data you’ll use will come from, especially in a professional environment.
Most databases consist of numerous tables containing data about multiple aspects of the business you’re dealing with that connect to each other, creating a huge data ecosystem.
The most common way to interact with these databases — called relational databases--is through Structured Query Language, or simply SQL.
SQL allows the user to insert, update, delete, and select data from databases and to create new tables.
While it’s important to know all this, understanding how to properly write queries to extract data from databases is critical for any data analyst, and it’s becoming more and more important for business analysts.
NoSQL
The most common types of databases are made of a large number of tables that interact with each other, which we call relational databases. The other type of database is called non-relational or simple NoSQL.
NoSQL is actually a generic term used to refer to all databases that don't store data in a tabular manner.
Different from SQL, NoSQL databases deal with semi-structured or unstructured data that is stored as key-value pairs, documents such as JSON, or even graphs.
This difference makes NoSQL databases ideal for working with large amounts of data without having a predetermined and rigid schema (like we have in SQL), which enables the users to change the format and fields in the data without any issue.
NoSQL databases usually have the following characteristics:
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They're faster.
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They're easily scalable.
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They have higher availability, which makes them suitable for mobile and IoT applications, as well as real-time analyses.
The Command Line
When talking about data analysis and data science skills, the command line is never the first one to come to mind. However, it’s a very important data science tool and a good skill to add to your resumé.
The command line (also known as the terminal or the shell) enables you to navigate through and edit files and directories more efficiently than using a graphical interface.
This is the kind of skill that may not be at the top of your list when starting in the data field. However, you should keep an eye out for it, as it will be useful when progressing in your data learning journey.
If you want to know more about why you should learn it, here are eleven reasons to learn to work with the command line and twelve essential command line tools for data scientists. If you want to learn by practicing, you can learn with the Command Line for Data Science course.
Cloud
Cloud computing keeps getting stronger and stronger year after year, which means it's an even more important skill to master.
Just like the command line, this is not a skill you'll need at first, but as you start working as a data practitioner, you'll probably see yourself dealing with cloud computing at some level.
Currently, the three biggest cloud platforms are as follows:
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AWS
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Google Cloud Platform — GCP
All have online applications for creating machine learning, ETLs (Extracting, Transforming, and Loading data), and dashboards. Here's a list of the benefits of such platforms for data professionals.
If you’re interested in getting into the cloud world, you can do the following:
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Get familiar with the concepts and start practicing with the introductory course on Microsoft Azure.
Git
Git is the standard tool for version control. Once you start to work with a team, you’ll understand how important version control is.
Git allows a team to have multiple branches of the same project, so each person can make their own changes, implementations, and developments, then the branches can be safely merged together.
Learning Git is more important for those who choose to work with programming languages for data analysis and data science, as those will probably need to share their code with multiple people and also to have access to other people’s code.
Most of the use of Git takes place in the command line, so having an understanding of both is certainly a good combination.
If you want to take your first steps with Git and version control, this is the course for you.
GitHub Actions
Still on the cloud and versioning subjects, GitHub Actions allows you to create a continuous integration and continuous delivery—CI/CD pipeline to automatically test and deploy machine learning applications, as well as run automated processes, create alerts, and more.
The pipeline runs when a specific event happens in your repository (among other possibilities), which means you can deploy a new version of your application just by committing this new version, for instance.
It’s possible to configure multiple pipelines to run at different triggers and perform different tasks, depending on your needs.
This is not a tool for analyzing data or training models. Its biggest pro is in enabling data scientists to deploy their machine learning models using best DevOps practices without setting up an entire cloud infrastructure, which takes much more effort and money.
Visual Studio Code
As a data professional, you’ll probably spend a lot of time writing code in a Jupyter notebook. As you evolve, you’ll eventually need to have your code in a .py file instead of a notebook, so you can deploy it directly to production. For this task, there are more suitable IDEs (Integrated Development Environments) than notebooks. Visual Studio Code (or just VSCode) is one of them.
Developed by Microsoft, VSCode is an amazing tool for writing, editing and debugging code.
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It supports numerous languages.
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It comes with built-in keyboard shortcuts and code-highlighting patterns that will make you more productive.
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There are hundreds of extensions available to install, which can increase the power of this tool.
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It has a built-in terminal where you’ll be able to put your command line and Git skills to work.
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You can expect easy integration with the entire Microsoft environment, as it's a Microsoft tool.
There are other great code editors that are great data science tools, but VSCode is surely an excellent choice. If you choose to use it, here’s how to set it up in an easy way.
Spark
Apache Spark is a powerful tool used to stream and process data at very large scales within short periods of time, through parallel processing on computer clusters.
Originally developed in Scale, Spark supports many programming languages, such as Python, R, and Java. When using Python, for instance, you can take advantage of the PySpark framework to connect to Spark’s API and write Spark applications directly from Python.
Not only does it support many languages, it’s also scalable and has multiple libraries that allow you to go from general data manipulation to machine learning.
If you intend to get into big data, you’ll have to learn Spark sooner or later. Here’s an easy introduction to Spark and more robust content for you to get started.
Docker
Docker is an open-source platform used to create and manage isolated environments that we call containers. By isolating itself from the systems, a container allows you to configure and run applications totally independent from the rest of your operating system.
Let’s say you’re using a Linux virtual machine in a cloud provider, and you want to use this VM to deploy your new machine learning model. You can use Docker to build a container with only what’s necessary for your application to run and expose an API endpoint that calls your model.
Using this same approach, you can deploy multiple applications in the same operating system without any conflicts between them.
Here’s a video tutorial of a deep learning API with Docker and Azure that’s worth checking out.
Another use case is to set up a Jupyter server inside a container to develop your data science applications. This allows the environment to be isolated from your original operating system.
Docker is also commonly integrated with cloud providers and used within DevOps environments. Here’s an example of using Docker and a cloud provider together.
Airflow
The Airflow is an open-source tool developed by the Apache Foundation, used to create, manage and monitor workflows that coordinate when determined tasks are executed.
Commonly used to orchestrate ETL pipelines by data engineering teams, Airflow is also a good tool for data scientists for scheduling and monitoring the execution of tasks.
For instance, let’s say we have an application running inside a container that’s accessed by an API. We know that this application only needs access on predetermined days, so we can use Airflow to schedule when the container should be stopped and when it needs to run again to expose the API endpoint. We’ll also schedule a script to call this endpoint once the container is running using Airflow.
Finally, during the entire process, Airflow produces logs, alerts, and warnings that allow users to keep track of multiple, diversified tasks they manage with Airflow.
MLFlow
MLFlow is an open-source tool used to manage the entire lifecycle of a machine learning model, from the first experiments to tests and deployments.
Here are some of the key advantages of MLFlow:
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It’s possible to automate and keep track of the training and testing, hyperparameter tuning, variable selection, deployment, and versioning of your models with a few lines of code.
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It provides a user-friendly interface that allows the user to visually analyze the entire process and compare different models and outputs.
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It smoothly integrates with the most used machine learning frameworks, such as scikit-learn, TensorFlow, Keras, and XGBoost, with programming languages such as Python, R, and Java, and cloud machine learning platforms, such as AWS Sagemaker and Azure Machine Learning.
If you want to take your machine learning skills to the next level, MLFlow will very likely be required.
Databricks
Databricks is a platform that unifies the entire data workflow in one place, not only for data scientists, but also data engineers, data analysts, and business analysts.
For data professionals, Databricks provides a notebook-like collaborative environment in which you can perform data science and analytics tasks with multi-language support--which means you can use different languages in the same notebook with flexibility and scalability.
When it comes to machine learning, it’s important to point out that Databricks is the developer of MLFlow, which means that these tools were made to work together and make the lives of data scientists easier.
Finally, Databricks easily integrates with Spark and the most famous IDEs and cloud providers. For instance, here’s an introduction to its use in Azure.
All this puts Databricks at the cutting edge of modern data science tools, and you’ll definitely run into it as you advance in your career.
Conclusion
Throughout this article, we covered several important skills so you know how to take the first steps in your data science career.
We've also seen a few advanced skills to keep on your list while you advance in your learning process that will make you a more complete professional.
The data field is constantly evolving, as new technologies show up all the time. Therefore, you’ll not only need to learn how to use new tools to land your first job, but you’ll need to keep learning new tools so you can stay relevant.
A programming language might be the core tool at first, but as we saw, there are adjacent tools that should not be taken for granted.
That’s why in Dataquest’s Data Science Career Path, you’ll not only learn how to program, you’ll take courses and learn how to use SQL, the command line, Git and version control, Jupyter notebooks, Spark, and you'll even take your first steps in the cloud.
You’ll also learn with a hands-on approach in which you are always writing code and building your own projects. This will also help you build your data science portfolio.
Dataquest believes this approach is the best method for creating a complete data science professional, able to keep up with the pace of data science's evolution.
If you’re interested, click here to know more about Dataquest’s Data Science Career Path!
