Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. Companies surely need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights.
But being an inquisitive Sherlock Holmes of data is no easy task. There are a number of tools available on the market, and knowing which one to choose to increase performance can be time-consuming, and often confusing. Not to forget various areas of data scientists employed in, from academia to IT companies.
But we will not focus on their choice of industry, but we will take a closer look at the definition of data science tools, consider the techniques and software which are needed to maximize the data scientist’s role in a company, and expound on data science visualization tools that create powerful interactive dashboards by using a modern dashboard creator.
Let’s get started.
What Is A Data Science Tool?
Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning.
In the past, data scientists had to rely on powerful computers to manage large volumes of data. Thanks to modern data analysis tools, today the costs are decreased since all the data is stored on a cloud and speeds up the process to make better business decisions.
The usage of modern and sophisticated tools has the goal to make data science faster, deeper, more effective while blending together several hundreds of routines which enable the standardization and cleanup of the data for you. Therefore, there are numerous data science tools and techniques that provide scientists with an easier, more digestible workflow and powerful results.
Here, we list the most prominent ones used in the industry.
Our Top Data Science Tools
The tools for data science benefit both scientists and analysts in their data quality management and control processes. But they’re not just limited to this, therefore, we will mention the top tools that data scientists utilize to delve into data, and extract actionable insights. Let’s start with RStudio and the R programming language.
1. R (And RStudio)
We begin our list of the top data science tools with R and RStudio. As most data scientists have probably heard of, RStudio is an open-source solution that allows to clean, manipulate, but also analyze data. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. R users typically come from science, education, and various other industries that need statistical computing and design in their processes. Big companies that utilize R in their analytics operations, such as Google, Facebook, and LinkedIn, usually are finance and analytics-driven, as R has proved to be the top mechanism for data analysis, statistics, and machine learning.
R is platform-independent, meaning it can be easily applied in each operating system. The major features that this data science tool offers are the ability for extensive data exploration combined with integration with other languages such as C++, Java or Python. Many users also report its power in constructed-in capabilities and libraries, data manipulation, and reporting. Whether the company needs a comprehensive financial analytics strategy or process, R has become one of the most used data science tools to explore and manage data.
Key functions and usage:
- available in both open-source and commercial use
- provides the user with visualizations, code editor, and debugging
- perfect for statistical computing and design
We continue our list of the top data scientist tools with Python. Together with R, this programming language makes the state of the art towards data science tools and techniques. In an ideal world, learning both would be a perfect solution, but we will not concentrate much on that.
Instead, we will talk about how Python enables data scientists to begin their journey into this exciting field, but also want to explore the world of programming since Python was primarily developed as a programming language. It offers a wide range of libraries attractive for both programmers and data scientists such as seaborn or TensorFlow. But its popularity within data science is also based on the possibility to clean, manipulate, and analyze data, just like R. They do have differences, and the user has to finally decide which one fits better in their needs to work with data, but Python has emerged as one of the most prominent data scientist tools out there. In fact, there are numerous tools built with or connected with Python, such as SciPy, Dask, HPAT, and Cython, among others, which makes this programming language among the top choices of striving data scientists who would love to grow in the field.
The industry loves Python since scientists are usually looking for tools that will enable them a simple programming experience, without much hassles and potential complexities. It’s a general-purpose programming language, preferred by 48% data scientists with less than 5 years in the field.
The TIOBE index confirms that the popularity of Python is increasing. As a matter of fact, Python was declared as the most popular language in 2018, and it will surely grow in the future as well.
Key functions and usage:
- offers a wide range of libraries; connected with numerous other tools
- used for cleaning, manipulating, and analyzing data
- preferred by almost half of the data scientists with less than 5 years in the industry
3. BI Tools And Applications
Business intelligence has developed into one of the most powerful solutions for companies that look for smart data analysis, predicting the future, and utilizing BI tools for generating actionable insights. There are many differences between business intelligence and data science, but with the recent development of BI tools, both became closely interconnected and dependent on each other.
The use of machine learning, predictive analytics, and various data connectors that enable the user to work with enormous amounts of databases, flat files, marketing analytics, CRM, etc., and share them with just a few clicks while all the information is stored on a cloud, enabled data scientists to use virtualization to their advantage, and utilize their selected data science software not just as a powerful tool, but also as a working environment to work with data on a scalable solution. There are also numerous business intelligence examples that illustrate what kind of value it can bring to the business bottom line.
The example above shows us a visual of the drag and drop interface created in datapine for a 6 months forecast based on past and current data. This is extremely useful in scenarios where future predictions can provide a backbone for defining forthcoming business strategies and decisions which would, otherwise, be based on possible human errors and “hunches.”
It is a fact that companies have become more data-driven, require a deep dependence on information, but need someone who can own the process of managing their data, often also of sensible nature. Business intelligence solutions not only provide the possibility to manipulate the data but also create powerful dashboards and reports that translate the work of data scientists to real business scenarios protected with high-security levels. These tools for data science offer additional aspects in dealing with information. They might implement a MySQL report builder to relieve the IT department from carrying out SQL queries and, therefore, save enormous amounts of resources and create a cost-effective business environment.
The possibilities within business intelligence are endless, and by using modern data science visualization tools in the form of BI software, scientists can become the backbone of a successful business strategy.
Key functions and usage:
- cloud data storage; availability 24/7/365
- scalable and adjustable according to the needs of the user
- connecting data sources and predicting future outcomes
4. SQL Consoles
The tools of a data scientist list wouldn’t be complete without SQL consoles such as MySQL Workbench, which we will give our focus on – this language is used for database management, querying, and analysis. In fact, MySQL Workbench is a visual tool that provides “data modeling, SQL development, and administration tools for server configuration, backup, and much more,” according to the product listing at the MySQL website. It has numerous features such as creating and viewing databases, executing and optimizing SQL queries, viewing server status, performing backup and recovery, and much more.
This is one of the most prominent data science visualization tools offered both for open-source and commercial use. This database management tool offers a variety of possibilities to keep a data-driven application running smoothly which is critical for organizations that need their databases clean and effective.
The field requires storing data in an accessible, and uncomplicated way; therefore, this language is the most convenient method a data scientist can use.
Key functions and usage:
- database management, analysis, querying
- performing backup and recovery
- offered both as open-source and commercial use
We finish our list of the best data science software tools with MATLAB. It’s still utilized in most of the academic areas, especially in (scientific) research. Computational mathematics is in the heart of this language, typically used in algorithm development, modeling and simulation, scientific and engineering graphics, data analysis, and exploration. It offers many statistics and machine learning functionalities such as predictive models for future forecasting.
This is one of the tools of a data scientist that utilizes physical-world data by offering native support for real-time formats (sensor, image, video, binary, etc.). it also provides thousands of pre-built algorithms for financial modeling, image and video processing, control system design and much more. Learning MATLAB is a great bonus for those who want to pursue a career in (academic) research.
Key functions and usage:
- utilized mostly in academia with a strong focus on computational mathematics
- offers many statistics and machine learning abilities
- thousands of pre-built algorithms
We have listed the best data science software tools for various fields that can be used for, whether small business analytics or scientific research. This field contains so many areas that this list could go on, but, in the end, it all depends what the focus area of the data scientist is.
The success of the modern analytics strategy, whether academic, business or industrial, depends on the data. By having the right data science software and tools, is crucial to develop practical values of the projects which utilizes them while the context in which data exists is completely dependent on data scientists.
If you want to have practical experiences of data and science, learn about various ways of analyzing your data, developing practical insights, monitoring and extracting your findings, then try modern business intelligence software like datapine for a 14-day trial, completely free!