The amount of data that are generated every day has been growing exponentially for years. On average, each of us creates at least 1.7 megabytes of data every second. A lot of this data is collected by businesses, playing a central role in their decision-making and strategic planning.
But, without the right tools, data goes unused and just takes up space. That’s where data analytics tools come in. They allow Data Scientists and Data Analysts to collect and analyze data to turn it into useful insights for a business.
There are a wide variety of data analytics tools available. Some are programming languages, which are the favorites of Data Scientists because they’re easy to use and good at parsing data. Some are libraries for these programming languages that make data analytics simpler. And some are self-contained applications that either run on the desktop or in a web browser.
Before selecting a data analytics tool, there are several questions to consider, such as:
- What kind of data are you analyzing? Is it simple numerical data stored in spreadsheets and databases or open-ended qualitative data like social media conversations that will need analysis by machine learning models to create insights?
- How much data are you analyzing? If the data you need to analyze is limited, just about any tool will do the job. But, if the dataset fits in the big data category, you’ll have to use specific tools for your analysis.
- How much technical knowledge is needed to perform the analysis? If you know a programming language, like Python, R, Java, or SQL (or are willing to learn one), then you have many options when it comes to choosing a data analytics tool. And if you’re non-technical or not interested in learning a language, you also have several options as there are no-code tools available for you to use.
- What format do you want the results in? Do you want to see your results in a table format or would you rather generate graphics to display them?
Once you’ve answered these questions, you’re ready to start learning about all the different analytics tools and eventually picking the one that’s best for your needs. Here are 15 popular data analytics tools for you to consider.
Python is one of the most widely used programming languages for data analytics. It’s an interpreted, general-purpose, high-level language that can be used for procedural, functional, and object-oriented programming.
This flexibility is one reason why Python is popular with many types of programmers. Plus, its simple syntax that’s almost like a natural language makes it a favorite language for beginning developers.
But, what makes Python a great language for data analytics is all the third-party libraries you can add to your project for free. Many of these libraries, like Matplotlib, PyTorch, and Pandas, are designed for handling data, which means you have to write less code to perform data analysis.
To get started learning Python, check out our Learn Python course. Then, once you know the basics of the language, our Analyze Data with Python Skill Path will teach you the fundamentals of using Python as a data analytics tool.
Matplotlib is a Python library that makes data visualization and graphical plotting easy. You can easily install it on any operating system that supports Python, including Mac, Windows, and Linux.
Once you have it installed, you can convert long lists of numbers into easy-to-understand pie charts, heat maps, histograms, and other types of visualizations that are ready to be used in reports or published online. Matplotlib can also generate a user interface for your graphics with a menu you can use to customize the graphic without writing extra code.
To learn both the basics of the Python programming language and how to use this powerful data visualization tool, check out our Visualize Data with Python course.
PyTorch is an open-source Python library used to build, train, and run deep machine learning models. It uses array-like tensors to encode the inputs, outputs, and parameters of models. A tensor is a container for data that can represent that data in any number of dimensions, making it a very flexible tool for data analytics.
Another advantage of PyTorch is it can run machine learning models using a computer GPU rather than the CPU. This means that a PyTorch machine learning model can provide you with insights 4-5 times faster than other data analytics tools that only use CPU processing.
pandas is another Python library, and it’s the Swiss Army knife of data manipulation. With pandas, you can turn unstructured data from multiple sources into an in-memory 2D object called a DataFrame.
Once you have your data in a DataFrame, you can filter, query, segment, and segregate it quickly. You can also merge and join two different DataFrames.
To learn how to clean, aggregate, and transform data with pandas, check out our Learn Data Analysis with pandas course.
5. Jupyter Notebook
Jupyter Notebook is an open-source web application where you can run Python, R, and other programming languages in an interactive environment. Because it’s a web application, it enables interactive collaboration between users.
It’s called a “notebook” because it not only allows Data Analysts to store and execute code in the browser, but also adds explanatory text, images, and other supporting information.
Jupyter Notebooks serve as a computation record between collaborators, and the records that are created can be stored as JSON files. These files can then be used to keep track of every step of the process.
R, also known as R statistics, was designed specifically for machine learning, data visualization, data mining, and statistical analysis. It’s not just a simple programming language with the ability to write loops, conditionals, and recursive functions.
R is a suite of tools for manipulating data, making calculations, and generating graphics. It comes with powerful data handling and storage capabilities, as well as a flexible graphical toolset for generating charts and graphs that are ready to publish in reports.
You can’t go wrong if you choose R as your data analytics tool, especially since, it was built from the ground up to do just that. Our Learn R course will teach you the basics of the language. After that, you can take Analyze Data with R to delve deeper into data analytics.
SQL, which stands for Structured Query Language, is a programming language that was created to interact with relational databases. Because of this, and the fact that businesses store the majority of their data in databases, SQL is a core tool that Data Scientists and Data Analysts use to gather insights.
SQL is also a simple language to learn. The queries you write in it are almost like English sentences. And just about every other programming language has SQL libraries you can use to interact with databases, which makes the language really powerful for data analytics.
To get started with SQL, check out our Learn SQL course, and to learn how to use it as a data analytics tool, check out Analyze Data with SQL. For the ultimate data analytics course that combines both SQL and Python, look into our Data Analyst Career Path.
While Python and R are both known as top tools for data analytics, when it comes to big data, Java is king. Many of the tools used for processing and analyzing massive datasets are written in Java, including Spark, Hadoop, Cassandra, Knime, Storm, Talend, and Elasticsearch.
Also, Java is an object-oriented programming language that’s been around for over 30 years. One of its primary benefits is its portability, which means the Java code you write for one operating system can run on any other platform that supports the Java Virtual Machine. Plus, as a general-purpose language, you can develop software that integrates data analysis with other features of the language.
To learn more about Java, check out our Learn Java course.
9. Apache Spark
If you’ll be working with big data, Apache Spark could be the best option for you. It’s an open-source data processing and analytics engine that can handle petabytes (1,000 terabytes) of data. Spark’s ability to quickly process large amounts of data is one of the main reasons it’s become of the most popular big data analytics tools.
Spark is also used in continuous machine learning applications where it can process data with almost real-time speed. Plus, it’s used as a general-purpose distributed processing engine where it can extract and transform data in SQL batch jobs. One of Spark’s claims to fame is that it can process data faster than Hadoop’s MapReduce, which held the title for fastest data processing for years.
D3 lets developers bind data to HTML documents using the Document Object Model (DOM), and then transform the document based on the data it uses. D3 also supports interaction, animation, annotation, and quantitative analysis. While the technology D3 uses is easy to learn, it comes with over 30 modules and 1,000 visualization methods, which may take time to master.
Our Learn D3 course will teach you the basics and start you on the path to becoming a data analytics and visualization expert.
MATLAB uses a high-level programming language for mathematical modeling, numerical computing, and data visualization. Its name is an abbreviation for “matrix laboratory” because it’s a matrix-based language. Mathematical matrices are data structures that can solve many technical computing problems more efficiently than other scalar-based programming languages.
You can use MATLAB for a variety of different tasks, like plotting data, developing algorithms, creating machine learning models, interacting with programs written in different languages, and analyzing datasets. Because of its advanced mathematical features, there are many technical roles that use MATLAB, including Software Engineers and Data Analysts.
TensorFlow can train and run machine learning models for image recognition, handwritten digit classification, word embeddings, recurrent neural networks, language translation, natural language processing, and simulation. And instead of having to deal with the details of creating custom algorithms, you can build your own models with TensorFlow.
To learn more about TensorFlow, check out our Build Deep Learning Models with TensorFlow Skill Path.
Tableau is a data analytics tool used to create quality data visualizations for business intelligence. It can extract data from many sources, including Microsoft Excel, PDF files, various types of databases, or even files stored on Amazon Web Services.
Once the data is extracted, it can be accessed by the Tableau Desktop application. Then, you can use it to generate dashboards and visualizations in the software. These dashboards can be shared with team members and the visualizations can be exported for reports. The data can also be published to Tableau Server where it can be accessed from any location, including mobile devices. And you can do all this without writing any code.
Excel has been used widely in the industry for years now. While it’s commonly used for simple tabular data, it’s actually a powerful data analytics tool.
Excel provides built-in pivot tables that allow you to summarize data in creative ways and produce valuable insights from diverse data. Also, Excel formulas can aggregate data into meaningful reports. The combination of pivot charts and slicers can be useful for visualizing data and creating interactive dashboards.
You can also enable and configure the powerful Data Analysis Toolpak that comes with Excel to develop complex statistical analysis. Excel may be ubiquitous, but there’s a lot of power hidden in the tool.
Data analytics tools will help you draw insights from data and discover trends and patterns that will help you make better decisions. There’s a wide variety of tools available, from complex programming languages to applications that require very little technical knowledge.
We offer several courses to get you started in data analytics. Here are courses on the programming languages mentioned in this article:
Once you know the basics, you can learn how to analyze data with these courses:
You can also use our Data Analyst Career Path to learn not only Python and SQL but also top data analysis tools including Pandas, NumPy, Matplotlib, Seaborn, and SciPy. You’ll even earn a certificate when you finish the course, which you can add to your resume.