Can you imagine a plumber without a toolbox? No, right? The same goes for data practitioners. Without their data analytics tools, data practitioners couldn’t analyze data, visualize data, extract value from data, and do the many cool things that data practitioners do in their daily work.
Depending on the data science career you’re most interested in –whether data analyst, data scientist, data engineer, etc–, your data science toolbox will be slightly different.
In this article, we will provide a list of some of the must-have data analytics tools every mature data analyst should have. In essence, a data analyst focuses on:
- Collecting, processing, and performing statistical data analyses to spot important patterns.
- Transforming and manipulating large data sets into usable forms, such as reports or presentations.
- Communicating data insights with data visualizations and data storytelling techniques.
Now that you know what a data analyst does, let’s get into their toolbox!
Criteria for Selecting a Data Analytics Tool
Data science is a rapidly evolving industry. New software and tools, including data analytics tools, are launching daily. With such a vibrant ecosystem, choosing the right tools can be challenging, especially if you are a newcomer in data science.
Like any toolbox, there is no tool that is best in absolute terms to address all the problems you will face as a data analyst. Instead, the key is to have a complete and balanced set of tools that will help you succeed in every scenario.
Every case is different, and you or the company you work for will have to consider several factors to find the best fit for your needs. Below, you can find a list of factors you should assess before choosing your data analytics tools:
Identifying business needs
This is probably the most important factor. While it’s important to ask yourself what tools you want to learn and what tools you like the most, you should never forget to ask what your company –or the company you would love to work in– needs. This makes sense: if the rest of the team uses Python, learning R first may not be the smartest decision.
At the same time, you should always keep an eye on recent trends and cutting-edge data technologies so you can learn them and increase your value in the team.
Scalability and flexibility
The big data landscape has changed dramatically in the last few years. Today, data-driven companies deal with massive amounts of data, with varying formats, that are often processed in near real-time.
Against this backdrop, scalability and flexibility are crucial for success. The data science technology landscape is rapidly growing, with many tools and applications specifically designed to address the challenges associated with increasing data volume, velocity, and variability. Hence, when choosing your tools, always keep in mind how much flexibility and scalability you or your team need.
Ease of use and learning curve
The ease of use and complexity of data analytics tools varies considerably. In general, the more features and advanced capabilities, the steeper the learning curve. By contrast, simpler data analytics tools are better suited for non-technical users, who will be able to get started quickly.
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The Top 9 Top Data Analytics Tools
With the above factors in mind, here is the list of the top 9 data analytics tools for data analysts.
Ranked first in several programming languages' popularity indices, Python is a must-have tool for data analysts. Python is an open-source and extremely versatile programming language with broad applicability in the data science industry and other disciplines, like web development and video game development. You can read more about what Python is used for in a separate post.
Any data science tasks you can think of can be done with Python. This is mainly thanks to its rich ecosystem of libraries. With thousands of powerful packages backed by its huge community of users, Python can perform all kinds of operations, from data preprocessing, visualization, and statistical analysis to the deployment of machine learning and deep learning models.
Due to its simple and readable syntax, Python is often referred to as one of the easiest programming languages to learn for beginners.
However, when it comes to processing massive amounts of data, other programming languages and solutions, such as Julia or Spark, can offer better performance and efficiency than Python.
If you want to be a Python expert, DataCamp is here to help. In our course catalog, you will find a wide array of Python courses for all levels. Check out our Data Analyst with Python Career Track and get ready for your Python learning journey.
2. R Programming
Though not as trending as Python, R is still a top option for data analysts. Frequently portrayed as the main competitor of Python, learning one (or both) of these two languages is a critical step to becoming a data analyst. Check out our Python vs R comparison for more details.
R is an open-source, domain-specific language explicitly designed for data science and advanced computing. Very popular in finance and academia, R is a perfect language for data manipulation, processing, and visualization, as well as statistical computing and machine learning.
R has a large community of users and a vast collection of specialized libraries for data analysis and machine learning. Some of the most notable ones belong to the Tidyverse family, a collection of data science packages. It includes dplyr for data manipulation and the powerful ggplot2, the standard library for data visualization in R.
Compared to Python, beginners may find R more difficult and less versatile. Yet, if you are new to data science or want to add new languages to your arsenal, learning R is a perfect choice. Check out our rich catalog of R courses, including our popular Introduction to R Course, to start sharpening your skills today. You can also follow our Data Analyst with R career track.
Much of the world's data is stored in databases. SQL (Structured Query Language) is a domain-specific language that allows programmers to communicate with, edit, and extract data from databases. Having a solid knowledge of databases and SQL is a must if you want to become a data analyst.
Knowing SQL will enable you to work with different relational databases, including popular systems like SQLite, MySQL, and PostgreSQL.
Whether you choose Python or R to start your data analyst journey, you should also consider learning SQL. Due to its declarative, simple syntax, SQL is very easy to learn compared to other languages, and it will help you a lot along the way.
Yet, while the syntax of SQL seems simple and rudimentary, writing advanced SQL queries can be tricky, especially when dealing with big and complex databases.
Want to get started in SQL? Have a look at our Data Analyst in SQL career track to become a query wizard.
Despite being a relatively young language –it was only released in 2011–Julia has already impressed the world of numerical computing.
Julia is a new and exciting programming language conceived from its foundations to be the ideal language for scientific computing, machine learning, and data mining. Julia achieves this goal by encompassing the speed of C, the general programming ability of Python, and the statistical prowess of R.
In recent years, Julia has steadily gained momentum, with more and more data practitioners opting for Julia instead of Python, as a result of its astonishing performance. You can read more about what Julia is used for in our separate guide.
However, Julia is not yet as widely adopted as languages such as Python and R. It has a smaller community and doesn't have as many libraries as its main competitors.
DataCamp has been working hard to update its catalog with new Julia courses. Have a look at our Julia Fundamentals skill track to get started on this data science rising star.
Microsoft Excel doesn’t need much of an introduction because everyone knows it. A classic tool in data analysis, its death has been predicted by many gurus in the last decades. However, the predictions couldn’t be more mistaken. Despite the rise of more flexible and powerful tools, Excel is still the go-to tool for many day-to-day data analysis tasks.
Excel combines powerful capabilities with a user-friendly interface, which makes it a perfect tool for both mature data analysts and less technical users. It’s also a great tool thanks to its smooth integration with the Microsoft BI stack, including PowerBI.
Excel is specifically designed to be an accessible and easy-to-use tool. However, this comes with a price: Excel is not as flexible as other tools like Python or R, and some complex operations may not be as straightforward.
Yet, learning Excel is a smart choice, as it’s very likely that part of your job as a data analyst will involve dealing with Excel spreadsheets. Check out our catalog, where you can find courses on the topic, such as Data Analysis in Excel, Data Preparation in Excel, and Data Visualization in Excel.
When it comes to dealing with huge datasets, conventional data analytics tools, like Python or R, may fall short. To process datasets with millions of rows at high speed, a different set of tools is required, which incorporate advanced techniques to provide high performance.
Among these solutions, Apache Spark is probably the most popular. Spark is a platform for cluster computing. Spark lets you spread data and computations over clusters with multiple nodes (think of each node as a separate computer). Splitting up your data makes it easier to work with very large datasets because each node only works with a small amount of data.
One of the keys of Spark is its integration with other data analytics tools, including Python. PySpark is the Python package that makes the magic happen. These integrations make it easier for data analysts to get started with Spark.
Willing to learn Spark? Our Introduction to PySpark Course is a great place to get started,
Power BI is a cloud-based business analytics solution that allows you to bring together different data sources, analyze them, and present data analysis through visualizations, reports, and dashboards.
According to the Gartner Magic Quadrant, Microsoft’s PowerBI is the leader in BI solutions in the industry. Power BI makes it easy to access data on almost any device inside and outside the organization.
Among its essential features, Power BI includes:
- Direct connectivity to Excel and easy integration with other Microsoft products
- Ability to compress data and extract insight from large datasets
- Customizability using R and Python
- Using Power Query to import, filter and translate data
- An adaptive and friendly user experience.
However, as with Excel, Power BI sometimes sacrifices flexibility in the name of accessibility. Some operations may be difficult to perform and functionalities in certain areas can be limited. Equally, despite its intuitive interface, PowerBI can be complex, especially when using it along with other interrelated tools.
Every aspiring data analyst should be fluent with at least one BI tool. Power BI is no doubt one of the best candidates. DataCamp has plenty of materials about the tool that ranges from our Introduction to Power BI Course to more advanced topics, such as our Reports in Power BI Course.
Founded in 2003 at Stanford University, Tableau is a powerful and popular data visualization tool that allows you to analyze data from multiple sources simultaneously.
Tableau is one of the best-in-class BI tools out there. It is used by top companies to extract insights from tons of raw data. Thanks to its intuitive and powerful platform, both technical and non-technical users can create visual dashboards and worksheets.
In many aspects, Tableau offers the same capabilities as Power BI. However, it’s considered a more complex tool for beginners. Hence, if you are just interested in building simple charts, you should go for less complex options. Also, an important downside of Tableau is that there is no free version of it, so you will have to pay a quite costly subscription to use it.
Still, many organizations are choosing Tableau for its unique and powerful capabilities. Check out our Introduction to Tableau Course and our Creating Dashboard in Tableau Course to discover the this wonderful tool.
9. DataCamp Workspace
The work of a data analyst wouldn’t be possible without IDEs (Integrated Development Environments). These coding tools provide powerful, intuitive interfaces that make writing, debugging, and testing your code easier.
There are many popular IDEs out there, such as JupyterLab or RStudio, but we don’t want to miss this opportunity to tell you about DataCamp Workspace.
Designed with data democratization in mind, Workspace is a valuable tool for team collaboration and organizational learning across various industries.
With the new AI Assistant, Workspace aims to make data science even more accessible and productive for its users. Key features of the AI Assistant include the "Fix Error" button, which not only corrects code errors but also explains them, allowing users to learn and avoid repeating mistakes.
The “Generate Code” feature allows you to generate code based on natural language queries and answer key questions about a dataset. Additionally, the AI Assistant provides intelligent suggestions based on existing code and context, making code writing smarter and more efficient.
Available on both free and paid Workspace plans, the AI Assistant promises a more seamless integration into the tooling stack of modern data scientists, empowering anyone working with data to make informed decisions. You can get started with Workspace for free.
Future Trends in Data Analytics Tools
Everything evolves rapidly in the data science industry. Any little innovation breakthrough can completely change the technology landscape. The tools and solutions that seem mainstream today can turn obsolete tomorrow, as emerging technologies with new and powerful capabilities reach the market.
This is clearly illustrated by the AI revolution that has resulted following the launch of ChatGPT. With their impressive capabilities, ChatGPT, and the many other generative AI technologies powered by large language models, AI is rapidly changing the way data science and software development is conceived.
These tools will soon become an essential part of the data analyst toolbox. Hence, learning them is essential. Luckily, at DataCamp we have you covered. We’re working hard to provide you with best-in-class courses and materials to navigate the AI revolution, including our popular Generative AI Concepts Course, and our AI Essentials Skill Track.
Becoming a data analyst is one of the best decisions anyone interested in data science can make. Learning the right data analytics tools will be a central part of your journey. We can only wish you safe travels and hope that our courses, tutorials, cheat sheets, and other materials can help you along the way.
Here is a list of our learning recommendations to get started:
This track starts with the basics of the Python programming language for data analysis. After covering the basics, you’ll dive deeper into data wrangling and data visualization techniques.
You’ll also learn to source data from different sources such as the web, databases, and JSON files. Finally, this track provides an overview of exploratory data analysis techniques and upskills the learner on concepts of relational databases and SQL.
This track focuses on the primary skills required by data analysts working with the R programming language. It offers comprehensive content covering the most commonly used R packages like tidyverse, ggplot, and dplyr.
Similar to the Python track, the course helps build an intermediate understanding of data analyst skills such as data sourcing, manipulation, and visualization.
This career track helps the learner develop an in-depth understanding of SQL Server, the concepts of relational databases, their design, and performance optimization.
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Data Sets and Where to Find Them: Navigating the Landscape of Information
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