A data scientist is responsible for extracting, pre-procession, manipulating and generating predictions out of data. But to do so, a data scientist requires a variety of statistical tools and programming languages. So, to make the job of every data scientist easy, today we are going to share with you the 10 most used data science tools.
These tools are the best to be used by the data scientist to carry out their data operations. You will understand the key features of the tool, benefits these tools provide you with as well as their comparisons.
So, let's don't waste much time and move further to take a look at all the 10 most used data science tools. So just scroll down and take a look at them itself.
10 most used data science tools
1. Weka
Waikato Environment for Knowledge Analysis also known as Weka is basically a machine learning software that is written in java. The Weka is a collection of various machine learning algorithms for data mining. Weka comprises of various machine learning tools such as classification, regression, clustering, data preparation, and visualization.
The Weka is an open-source GUI software which is the best to help you out with the easier implementation of machine learning algorithms and that too through an interactable platform.
Let me tell you that the best part about Weka is that you can easily understand the functioning of the machine learning on the data without even having to write a single line of code. No doubt, it is the ideal tool for a data scientist who is a beginner in data science.
2. Scikit-Learn
So, the Scikit-Learning is simply based on the python. It is used to implement machine learning algorithms. Let me tell you, folks, it is quite simple and easy to implement a tool that is widely used for the purpose of analysis as well as data science.
The Scikit-Learn is a good option to support a variety of features in machine learning including the data preprocessing, regression, classification, dimensionality reduction, clustering and a lot more.
3. Matplotlib
So, the Matplotlib is basically a plotting and visualization library developed and based on the python. Matplotlib is one of the most popular tools to generate graphs and that too with the analyzed data. This tool is majorly used for plotting all the complex graphs using all the simple lines of code.
Using this tool, one can generate histograms, bar plots, scatterplots, etc. It has several essential modules. And one of the most widely used modules of the Matplotlib is the Pyplot. It offers a MATLAB like an interface. But let me tell you that the Pyplot is also an alternative to MATLAB's graphic module.
4. SAS
The SAS is one of those data science tools which is specifically designed for the statistical operations. So, the SAS is a closed source proprietary software. It is also used by all large organizations to help them analyze the data. This tool uses base SAS programming language which is the best for performing statistical modeling.
This is the most used software by the professionals and companies that are working on reliable commercial software. SAS has numerous statistical libraries to offer, and you as a data scientist can simply use for modeling and organizing their data.
5. BigML
It is another widely used and one of the most popular data science tools. The BigML provides a fully interactable, cloud-based GUI environment that is the best one for you to process machine learning algorithms.
This tool also provides standardized software by simply using cloud computing for industry requirements. With the help of it, companies tend to use machine learning algorithms across various parts of their company.
6. MATLAB
The MATLAB is basically a multi-paradigm numerical computing environment for simply procession all the mathematical information. The MATLAB is termed as a closed source software that also facilitates matrix functions, algorithmic implementation, as well as the statistical modeling of data.
In several Data Science Certification,this tool is one of the most widely used. You can also use the MATLAB for image and signal processing. This makes it a really very versatile tool for data scientists as they can tackle all the problems, from data cleaning as well as analysis to more advance and deep learning algorithms.
7. Apache Spark
Apache Spark also is known as the Spark is an all-powerful analytics engine and it is one of the most used data science tools. The spark is designed to handle the batch processing and stream processing in the best manner.
It also includes several APIs that also facilitates data scientists to help you make repeated access to the data for machine learning, storage in SQL, and a lot more. The Spark is also considered as the improvement over the Hadoop and it can even perform around 100 times faster than the MapReduce.
Sparks also includes many machine learning APIs that can even help data scientists to make powerful predictions with the given data.
8. D3.js
Javascript is mainly used as the client-side scripting language. It is a complete javascript library that simply allows you to make interactive visualization on your web browsers. With the use of several APIs, you can also use several functions to create a dynamic visualization as well as analysis of data in your browser.
But let me tell you that the most powerful as well as the most impressive feature of the D3.js is the usage of all the animated transitions. It also makes the documents a lot more dynamic by simply allowing the updates on the client-side.
9. Excel
Well, when we are talking about the 10 most widely used data science tools how can we forget about Microsoft developed Excel. It is mostly used for spreadsheet calculations. Also, nowadays, it is widely used for data processing, visualization, as well as complex calculations.
No doubt, Excel is termed as one of the most powerful analytics tools for data science. While Excel is termed as the traditional tool for data analysis. Excel still packs a punch. Excel comes with a variety of formulas, filters, tables, slicers and a lot more.
10. Jupyter
The Jupyter is also an open-source tool completely based on IPython for helping all the developers in making the open-source software as well as for experiencing interactive computing.
Not only the IPython, but Jupyter supports multiple languages like R, Julia, and Python. The Jupyter is basically a web application tool used for writing live code, presentation, and visualization. Jupyter is one of the most widely popular tools which is specially designed to address the requirements for Data Science.
The Final Thoughts On 10 most used data science tools
So, these are the 10 most used data science tools. It is quite common that data science requires a vast array of tools. The tools are needed for analyzing data, creating aesthetic and visualization as well as for creating powerful predictive models for using machine learning algorithms.
Also, let me tell you that most of the data science tools deliver complex data science operations and that too in one place. Also, there are several other tools that cater simply to the application domains of data science.
