Best Data Science Tools in 2020

In Data Science, researchers use different scientific methods, algorithms, and tools to…
Best Data Science Tools in 2020


In Data Science, researchers use different scientific methods, algorithms, and tools to extract useful information from structural and unstructured data.

Since the business has become obsessed with digital transformation, there is a constant need for data science providers. To integrate digital technologies and maximize their effectiveness, enterprises require data science consulting from experienced specialists. But both companies and scientists also need reliable software instruments. So, here are the top 10 data science tools for companies and software developers.

Amazon Redshift

Redshift is a part of the enormous ecosystem of Amazon Web Services. It is a cost-effective and extremely efficient instrument for building a data warehouse. The structured information uploaded to the warehouse clusters can be easily accessed and analyzed, which makes it invaluable for business intelligence. Redshift has seamless integration with other tools in Amazon’s ecosystem and offers excellent scalability to cover the needs of large, medium, and small enterprises. It also pays special attention to information security and supports data encryption and SSL certificates.


Data science is all about extracting, processing, and storing information, so it is impossible without a good database management system. MySQL is perhaps the most popular relational database used in numerous web applications and servers. It is very convenient, supports many source formats of data, and offers effective means to import, clean up, modify, and output information in a clear visual form.


One of the main processes of data science is the analysis of collected information. SAS is one of the top platforms for data analysis and offers an impressive set of features. It integrates with other beneficial digital technologies, such as Artificial Intelligence, Machine Learning, Internet of Things, and cloud solutions, to maximize its effectiveness. This way, SAS can obtain data from various sources, from cloud databases to smart devices, and perform deep analysis. 

As an additional benefit for data scientists, is has excellent compatibility with several popular software development instruments. With the help of application programming interfaces, SAS can be combined with Python, Java, R, REST, and some other development tools and environments.


When you go deep into business analytics and statistics, you need a reliable instrument dedicated to handling mathematical data. MATLAB is a perfect solution for this purpose. Its capabilities extend far beyond business applications, and it is actively implemented for scientific purposes. MATLAB presents a computing environment that is especially beneficial for training machine learning models, among other uses. Data scientists use it to perform AI-powered analysis, automate routine tasks, visualize the analysis results, etc.

Domino Data Science Platform

Domino is a versatile solution that takes control of all data science processes within an enterprise. It is packed with many tools for managers, business analysts, system administrators, and software developers. As with most data science tools, its goal is to maximize the effectiveness of a company by developing and implementing custom models based on processed data. To also allows scientists to run experiments in a safe virtual environment to test a model and predict its outcome via complex computing.


RapidMiner is a collection of automated solutions that use the power of AI and ML to optimize and promote businesses. Due to its flexibility and scalability, it can be easily applied to an enterprise in any industry. Despite its name, RapidMiner is not limited to data mining processes but offers a wide range of data science activities. It is effectively used for risk management and fraud detection, price optimization, and market forecasting, as well as other activities.

Talend Data Fabric

This platform is often used when a company decides to set up a data warehouse. Talend is perfect for collecting information from various sources and transforming it in a form that enables convenient analysis, fast searching, and improved overall usability. It supports both cloud warehouses and local (on-premises) options. Talend also supports Big Data, Machine Learning, and other advanced digital technologies that help companies to make the most out of their data.

Apache Spark

Since its appearance in 2014, this framework for distributed computation has become one of the most powerful tools for data analytics. What makes it even more effective is the large number of components that can be added to the main part – Spark Core. For example, Spark MLlib is the component that adds the machine learning functionality that benefits from the speed of distributed computations. Additionally, the high speed allows Apache Spark to process information in real-time, during its streaming. 

TensorFlow and Scikit-learn

These are the two most popular libraries for Machine Learning. Just like AI and ML are the heart and soul of modern data science, TensorFlow and Scikit-learn and the essential instruments for software developers that design and integrate learning algorithms. Another common feature between these two libraries is that you need one programming language – Python – to use both of them. 

Microsoft Excel

Don’t be fooled by the seeming simplicity of Microsoft Excel. It is the core tool for handling structured information in many companies and can be effectively used for data analysis. Thanks to its overwhelming popularity, Excel often becomes the first step in digitizing the business processes in order to effectively monitor and evaluate various aspects of operation within an enterprise.

It has all the basic features required to input/import, transform, analyze, and visualize information. As a bonus, any company or even an individual can afford it. Excel is hilariously cheap in comparison with other analytics software used in data science.

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