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Data science tools are handy when it comes to analyzing complex and large datasets.
Tools like R, Matplotlib, and SAS are best suited for statistical analysis.
Advanced tools like TensorFlow and libraries like NLTK libraries and Scikit-learn can be used in machine learning projects.
The increasing complexity of the data collected is driving organizations to invest in advanced data science tools to analyze this data and derive insights. These tools help handle large datasets, build models, create visualizations, and transform raw data into actionable insights.
Here is the top 10 data science tools list that most researchers, data scientists, analysts, and other data professionals prefer:
Python is one of the leading data science tools worldwide. Its syntax is easier to understand, especially for the new data scientists. The platform has a large and active community that supports information exchange. Google and IBM mostly rely on this language as it helps develop and manage complex projects.
Matplotlib is widely used to create high-quality charts, plots, and data visualizations. It is a famous Python library that offers a wide range of customization options. Similar to Python, Matplotlib offers versatility and flexibility without being complex.
TensorFlow is a machine learning library used to build and deploy ML models. TensorFlow’s data flow graph, which represents nodes and edges, enables faster and simpler execution of complex computations on CPUs and GPUs.
NLTK is a popular library used in Python for Natural Language Processing (NLP). It is especially suitable for working with human language data, like translation and classification. NLTK is perfect for data scientists who work on both language and textual data.
Also read: Conversational AI: Transforming Natural Language Data Analytics
Due to its user-friendliness and flexibility, Scikit-learn, an open-source library of Python, has become the most used library in the entire field of data science. It can also be used to run several machine learning algorithms, like classification, preprocessing, and clustering.
KNIME is a data analytics platform that offers a graphical interface for data scientists. It is an open-source tool used to design and execute data workflows without the help of coding. KNIME is specially used for team collaboration and data analysis.
D3 is a JavaScript library used to create data visualizations on the web. Data scientists use it to easily bind data to HTML, CSS, and SVG elements. This easy-to-execute binding allows them to efficiently create web-based visual representations.
R is also a leading open-source data science tool known for its statistical computing capabilities. It is suitable for complex statistical data analysis. However, R has a comparatively steeper learning curve than Python.
WEKA, also known as “Waikato Environment for Knowledge Analysis,” is an open-source data mining software. It is written in Java and is particularly famous for its graphical user interface (GUI). Without the need for substantial coding, it enables experimenting with multiple machine learning techniques.
SAS (Statistical Analysis System) is a programming language with strong statistical analysis capabilities and data management features. It also has a comprehensive suite of tools for different tasks in data science.
Also Read: Platforms to Learn SAS for Data Science
With the increasing complexity of data, the need for advanced analysis tools is also increasing. Data science tools can be used for in-depth data analysis, machine learning projects, data visualization, and data modeling. These libraries and platforms make it easier for data professionals to make sense of the numbers and draw insights that align with the company’s business goals.
