In data science, you can be sure to use a range of common tools, regardless of the particularities of your role. While some tools rise and others fall into oblivion, it’s important to keep up with the trends and always stay on top of them. By learning these new tools, you get to build a strong competitive advantage in the job market and simply become better at your job.
In this article, we take a closer look at the key tools that every data scientist should know to become more effective at their job. We hope this article helps you to polish your skillset, improve your knowledge, and find your dream job.
This tool was specifically designed for statistical operations. In its essence, SAS is a closed-source proprietary software used by large organizations for data analytics. It uses the base SAS programming language for performing statistical modeling. Today, SAS is widely used by companies that focus on software development.
SAS comes with many statistical libraries and tools that data scientists can use for modeling and organizing data. It’s a highly reliable language with strong support from the company behind it. However, it’s also very expensive and used only in larger industries.
Moreover, if you compare SAS to open-source tools, you’re more likely to go with one of the free alternatives. Are you planning to grow your career in the enterprise environment? Then the knowledge of SAS can boost your career.
2. Apache Spark
This is a powerful analytics engine and one of the most used data science tools today. It was designed to handle batch and stream processing. It offers data scientists many APIs that help to make repeated access to data for machine learning purposes or storage in SQL and others. It’s definitely is a huge improvement over Hadoop, and it can perform 100 times faster than MapReduce.
As you may expect, Spark offers many machine learning APIs that allow making powerful predictions on the basis of data. This tool is a must-have is you’re interested in machine learning. Contrary to other analytical tools that can only process historical data in batches, Spark can also process real-time data. Moreover, it comes with various APIs programmable in Java, R, and Python. According to experts, Spark works best with the Scala programming language, which is based on the Java Virtual Machine and is cross-platform.
3. Apache Hadoop
This is classic open-source software for scalable and distributed computing. Built by the Apache Software Foundation, Hadoop uses parallel processing across clusters of nodes to solve complex computational problems and enable data-intensive tasks.
How does Hadoop do that? By splitting large files into smaller chunks and sending them over to nodes with instructions. The tool takes advantage of different components that help it to achieve such efficiency and smooth data processing: Hadoop Common, Hadoop Distributed File System, Hadoop YARN, Hadoop MapReduce, and more.
Another valuable data science tool is BigML. It provides a cloud-based GUI environment a data scientist can use for processing machine learning algorithms. It provides standardized software that uses cloud computing to match modern industry requirements. Thanks to BigML, businesses can use machine learning algorithms across different parts of their operations – for example, risk analytics, product innovation, or sales forecasting.
The core of BigML is predictive modeling, as it uses a wide range of machine learning algorithms such as classification, time-series forecasting, or clustering. They come in an easy-to-use web interface with REST APIs. Depending on your data needs, you can create a free or premium account.
BigML also comes in handy for interactive data visualizations – it offers data scientists the ability to export the visual chart to mobile or IoT devices. You can probably tell why it’s such an important tool in a data scientist’s toolkit, especially for professionals interested in machine learning.
Recently acquired by Salesforce, Tableau is an excellent data visualization tool and one of the leading enterprise Customer Relationship Management (CRM) systems in the world. It focuses on offering a clear representation of data in a short period of time to assist decision-making across organizations.
That’s why many data scientists need to know it – especially the one who directly supports the decision-making processes in their companies. Tableau makes use of online analytical processing cubes, spreadsheets, cloud databases, and relational databases.
The great thing about Tableau is that it allows you to stay focused on statistics instead of worrying about setting it all up. Getting started is really easy – you can drag and drop a data set into the application, set up filters, and customize your data set effortlessly.
This handy data science tool is an enterprise-grade solution that addresses every potential need for artificial intelligence (AI). Its goal is to automate the end-to-end process of building, deploying, and to maintain AI applications.
DataRobot allows data scientists to get started with just a few clicks and support their companies with features such as automated machine learning or time-series, machine learning operations, and many others.
The tool can be used individually or in combination with other deployment options such as on-premise or cloud-based solutions. Moreover, DataRobot helps data scientists to focus more on the problem at hand rather than worrying about the setup.
This tool is an essential addition to your toolkit if you’re interested in artificial intelligence, deep learning, and machine learning. Built by Google, TensorFlow is basically a library that helps data scientists to build and train models, deploy them on diverse platforms such as smartphones, servers, computers, or achieve the best potential using their resources.
By using TensorFlow, you can easily create statistical models, build data visualizations, and access the best-in-class features for both machine learning and deep learning. There’s no denying that TensorFlow works best with the Python programming language, so you better get a good grasp of Python before using it.
This free and open-source data analytics, reporting, and integration platform is a multipurpose tool that all data scientists should know. It allows us to integrate elements such as machine learning or data mining into data sets.
The intuitive GUI enables data scientists to perform the extraction, transformation, and loading of data easily. They don’t even need to have a lot of programming knowledge – which is often the case for beginner data scientists who are trained in statistics and not software development.
The tool also allows creating visual data pipelines, models, and interactive views. The great thing about Knime is that it helps you to work on large data volumes easily, offering extensive integration capabilities with database management languages such as MySQL, SQL Server, PostgreSQL, Oracle, and many more.
This data science platform is just perfect for teams of data scientists – it helps them to unite data preparation and predictive model deployment.
You can use it to prepare your models from the initial preparation of data to the very last steps, such as analyzing the deployed model. It’s an end-to-end data science package that offers massive help in areas such as text mining, deep learning, machine learning, and predictive analytics.
Data scientists use it in data preparation, result visualization, model validation, real-time data tracking and analytics, comprehensive reporting, scalability for use by any team, and excellent security features—a must-have in your toolkit.
This one is a classic open-source graph plotting library for every data scientist, especially if you know the Python programming language. It offers extensive customization options without complicating any parts of the process.
Python is arguably the most powerful programming language for data science today thanks to its vast collection of libraries and integration with other languages. Matplotlib is an excellent example of such a library. Its simple GUI allows you to create attractive data visualizations. Thanks to multiple export options, you can take your custom graph to the platform of your choice easily.
Another interesting feature of this library is the use of animated transitions. They help to make documents more dynamic by allowing updates on the client-side and using the changes in data to reflect the visualizations in the browser.
By combining D3.js with CSS, you can create beautiful transitory visualizations that help to implement customized graphs on web pages. This is especially important for data scientists to work in web application development or are involved in projects building IoT devices that require client-side interactions for both data processing and visualization.
This tool is a multi-paradigm numerical computing environment that helps to process mathematical information. It’s a closed-source software that makes it easier to carry out tasks such as algorithmic implementation and statistical modeling of data or matrix functions.
MATLAB has found wide use across various scientific disciplines and is a must-have for data scientists that are looking to work on more scientific applications. MATLAB is mainly used for simulating fuzzy logic and neural networks. By using the graphics library, you can also create powerful data visualizations.
Data scientists also use MATLAB for image and signal processing. As you can see, this versatile tool helps data scientists to tackle a variety of different problems – from data cleaning and analysis to creating sophisticated deep learning algorithms.
This is a real classic that just had to become part of our list. Probably the most widely used data analysis tool, Microsoft Excel comes in handy for spreadsheet calculations but also data processing, visualization, and carrying out complex calculations. Excel is easily one of the most powerful analytical tools for data scientists.
It comes with bar formulas, tables, slicers, filters, and many more features. Moreover, you can create custom functions or formulas. Sure, Excel won’t help in calculating a massive amount of data, but it’s still a great choice for creating spreadsheets and data visualizations.
You can connect SQL with Excel and then use it to manipulate or analyze data. Many data scientists also use Excel for data cleaning because it offers an interactive GUI environment that helps to preprocess information easily.
We hope that this list of tools helps you to expand your toolkit and think about how to invest your time in learning new tools that make a greater impact on your career in data science. If you would like to learn more about how to launch and build a successful career in data science or learn about the most recent trends in the tech industry, keep a close eye on our blog!