9 Most Used Data Science Tools for 2022

Data Science Tools

In this blog we’ll discuss about the Data Science Tools that every data scientist wish for. Data is playing a pivotal role in redefining the future. Data Scientists are the professionals who can organize and analyze the huge amount of data. For this they need tools for data analysis.

Organizations are adopting all the measures to unfold the information lurking in the scattered data. It is expected that by 2025, 163 zettabytes of data will be created. The information hidden in this ballooning data repository can help in forming a flawless model of success for every organization. Hence, companies must have an arsenal of the best data scientist who can leverage their data science skills to unfold the hidden meaning in this data.

Essential Data Science Ingredients

When you start exploring the different set of data science tools, then you will come across a number of options. You have innumerable options, but a wise data scientist should know which is the right data science tool to help them effectively decipher the information hidden behind available data. Following is a set of tools that you must know:

  1. SAS– Statistical Analysis System or SAS is one of the most powerful statistical and analytical data science tools. With SAS, you can work on a large volume of data and analyze it. It makes use of the base SAS programming language. The data scientist can use several statistical libraries to model and organize the data.
    Key features:
    • Easy to learn
    • It comes with tutorials that make it easy to learn
    • It has a well-managed suite of tools that helps in data mining, statistical analysis, and BI application
    • It has a simple Graphic User Interface that helps in deriving powerful reports
    • It can also analyse textual content
  2. Apache Spark– It is used to work on batch and stream processing. It has a better ability to handle streaming data. And it can process real-time data, making this tool more popular than the others used to analyze historical data. It has several APIs. The highly official cluster management system present in spark makes it process applications at a faster speed.
    Key features:
    • It has faster processing
    • It supports complex analytics
    • It offers real-time stream processing
    • It is easy-to-use
  3. BigML– This data science tool is useful for companies. They can use it to forecast sales, and also work on creating their products. One of the prominent features of this tool is that it is completely automated.
    Key features:
    • It is used for processing machine learning algorithm
    • It uses regression methods like linear regression, trees and others
    • It uses cluster analyses, anomaly detection
  4. TensorFlow– The next data science tool that we have is the TensorFlow. It is an advanced machine learning algorithm. It has powerful libraries that helps in creating training models which can be executed across the different platforms like smartphones, and computers. It is an open-source tool kit that also offers efficient computational abilities. You can use this tool on both CPUs and GPUs.
    Key features:
    • It provides the framework which is used for computation on different platforms
    • It is a flexible tool used for machine learning processes
  5. Matlab– It is a high-level programming language. This tool is used by data scientists for analysis of data, designing the algorithm, and developing embedded systems for wireless communication. Besides, it also has add-on toolboxes and several build functions, which makes it easy to visualize data under 2D and 3D dimensions.
    Key features:
    • This tool is used in developing algorithms and models
    • It provides an interactive app interface, which you can use to test different algorithms
    • It also automates the work, thus enhancing the efficacy of the process
  6. Python– It is one of the most widely used programming languages for data science that finds applications across different technological domains. It comes with built-in data structures and dynamic typing and binding capabilities. However, Python is easier to learn.Learning Python will help you become a proficient data scientist, but you can also use it for data analysis, data visualization, NLP or Natural Language Processing, Artificial Intelligence, and Robotic process automation.
    Key features:
    • Python is used for data manipulation, mining and data visualization
    • Python is used by beginners, data scientists and even experienced professionals
    • It is used for creating web and mobile applications
  7. Weka– Data mining is an integral part of data analysis. Fetching out the set of useful data from volumes of info can be challenging. With Weka it becomes easier. It is an open-source workbench. You can also find several machine learning algorithms for data mining. It can be used directly on data sets without any programming language. It can also be implemented via a JAVA API.Weka can be used for clustering, classification, regression and association rules. It also supports Python, R, Spark and other libraries.
    Key features:
    • It is open-source software
    • It is used for data classification, data visualization, and data regression
  8. Tableau – It is one of the best data science visualization tools. It is used to create interactive graphs and charts. This makes understanding the data effectively. It also lets you connect to different data sources and create a visualization in less time. Besides, you can also share your work with others.

    Although learning the Tableau may take some time, once you gain expertise in this data visualization tool, it will be helpful in accurate data interpretation.

    Key features:
    • It offers real-time analytics
    • You can connect it to different data sources
    • You can access it via mobile
  9. Excel– Data visualization tool that a data scientist profoundly uses is Excel. Excel find multiple application and is not just limited to data science. It has an easy interface and is much simpler when it comes to application. For example, you can use Excel to create charts and scatter plots that display resource associations between two data sets.Many data scientists use scatter plots to analyze statistical, medical, economic and scientific data. This helps make the research more accurate and helps in formulating right strategic actions for a strong and flawless financial planning.
    Key features:
    • It is easy to use
    • It can be used to create a visual interpretation
    • It can be used to create charts and scatter plots

The purpose of each of the above-mentioned data science tools is to ensure that data analysis and interpretation are accurate. When it comes to the analysis of data, then there is no scope for mistakes or error. With the help of these data science tools, a proficient data scientist can make the right assessment of the information.

Growing prospects of growth for data scientists

Data science is also one of the most lucrative career opportunities. Its central role in decision-making and strategic planning drives organizations to invest in the people, processes and technologies that will help in gaining valuable business insights from their data assets. The growing volume of enterprise data and its significant role in helping the organization make strategic decisions and planning motivate the organizations to invest.

Why expertise in using data science tools is paramount?

The increasing volume and complexity of enterprise data, makes it important to have expertise in using the data science tools. Assessment involves using data science tools like deep learning algorithms, statistical tools like linear regression, programming languages like Python, machine learning and others.

Comprehending the information lurking behind a scattered pool of information can be challenging. Hence it is important to master the tools used in data science. Several techniques, tools for data science and software can make your data analysis precise and accurate. This blog takes you through a detailed overview of the different data science tools, its key features and how you can become a data scientist.

Is data science hard to learn?

The above technical information may make you think about whether learning data science is hard. This is a common question that ponders in the mind of every data science aspirant. However, you can master all these best data science tools with the data science course.

How to learn data science?

When it comes to learning data science, then the first step is to develop a data mindset. Have a technical bent of mind. With your keenness and passion for learning about data science, it will be easier for you to understand the tools and their application.

With the wider applicability of data science, data science has emerged as a popular career opportunity. If you plan to become a data scientist, the first step is to enroll for the best data science certification course. Several institutes and online platforms are offering this course. Hence if you are keen to join a data science program, you should always take the data science course overview to understand whether it covers all the essential data science tools or not.

How to Become a Data Analyst with No Experience?

It is expected that by 2025 there will be around 11.6 million new job opportunities for analytics professionals. Having a data science certification makes you eligible to be a part of this growing community. However, a common question is whether one can become a data analyst without experience. Data analyst and data scientist are entry-level position and an individual who is willing to be a part of this domain can join the data science course for beginners.

Data science courses for beginners are dedicated to nurturing the data skill sets of freshers. College graduates or individuals who want to become data scientists can enroll on a data science program for beginners. This course entails all the core concepts of data science and practical application via projects.

Wrapping it up !!!

In the times to come, we can expect a much wider application of data science. Its role is not just limited to the IT sector; data science is finding applications in finance, healthcare, education, retail, marketing and other fields.

This growth is also catalyzing the demand for data science certification courses. The certification courses are designed to provide theoretical and practical knowledge that makes an individual job ready. So, if you are looking forward to making a career as a professional data scientist, this is the time to enrol on the data science certification program today.


  • Neha Singh

    Written by:

    I’m a full-time freelance writer and editor who enjoys wordsmithing. The 8 years long journey as a content writer and editor has made me relaize the significance and power of choosing the right words. Prior to my writing journey, I was a trainer and human resource manager. WIth more than a decade long professional journey, I find myself more powerful as a wordsmith. As an avid writer, everything around me inspires me and pushes me to string words and ideas to create unique content; and when I’m not writing and editing, I enjoy experimenting with my culinary skills, reading, gardening, and spending time with my adorable little mutt Neel.