Top 7 Data Science Tools for 2023
Data Science is the hottest field of the 21st century. It helps businesses to gain insights into their customers and the market, allowing them to pitch their products better. Data Scientists play an important role in a company’s decision-making. The work of a Data Scientist is to extract, manipulate, pre-process, and generate predictions from data. Data scientists will need various data science tools and programming languages to accomplish this.
In this article, we will discuss some of the most popular data science tools and their benefits and key features.
SAS is a data science tool designed specifically for statistical operations. Large organizations use SAS to analyze data. It performs statistical modeling using a base SAS programming language and is widely used by companies and professionals working on reliable commercial software. SAS provides various tools and statistical libraries for data modeling and organization.
Its pricing is as high as the features it offers, which is why large organizations mostly use it.
BigML is yet another widely used data science tool. It provides a fully interactive cloud-based GUI environment that can be used to process Machine Learning Algorithms. BigML is a standardized software that enables businesses to apply Machine Learning algorithms across multiple departments.
BigML is also an expert in predictive modeling. It employs a wide range of Machine Learning algorithms such as classification, clustering, time-series analysis, etc. It lets data scientists visualize interactive datasets and lets you send visual charts to other devices.
Furthermore, BigML has automation methods that can be extremely useful in automating the tuning of hyperparameter models and the workflow of reusable scripts.
BigML has an easy-to-use interface and offers free signups for those who want to try the tool.
MATLAB is a multi-paradigm numerical computing platform that allows you to process mathematical data. It is extremely useful in statistical data modeling, algorithmic implementation, and matrix functions.
Data scientists can create powerful visualizations using the MATLAB graphics library. Because it is also used in signal and image processing, MATLAB is a very versatile tool for Data Scientists.
Tableau is a well-known tool for visualizing data. It has powerful graphics and can be used to make visualizations that can be interacted with. Tableau is specifically designed for industries involved in business intelligence.
The best feature of Tableau is its ability to connect to databases, Online Analytical Processing cubes, spreadsheets, and other data sources. Tableau can also visualize geographical data to plot latitudes and longitudes on maps.
It can also be used to analyze data as an analytics tool. You can share your findings/observations with Tableau’s active online community.
Jupyter is an IPython-based tool that helps developers make open-source software, combine software code, and try out interactive computing. Jupyter supports various programming languages, including Python, Julia, and R. It is primarily used for writing live code and visualizations and presentations.
Jupyter also has some fantastic presentation features that can be extremely useful when telling stories. Data scientists can use Jupyter to perform data cleansing, visualization, statistical computation, and even predictive machine learning models.
Matplotlib is a visualization and plotting library written in Python. It is, without a doubt, the best tool for creating graphs from analyzed data. Data scientists can use Matplotlib to create bar plots, scatterplots, histograms, and other graphs.
Matplotlib is also a popular data visualization tool.
Natural Language Processing (NLP) is a hot topic in the Data Science community. The development of statistical models that assist machines in understanding human language is called NLP. These models are components of Machine Learning that help computers understand natural language. When it comes to the most popular programming language, a.k.a. “Python,” comes with a set of libraries called Natural Language Toolkit (NLTK) that was created specifically for this purpose.
Data scientists commonly use NLTK in language processing techniques such as stemming, tokenization, tagging, machine learning, and parsing. It has a wide variety of applications like Word Segmentation, Machine Translation, Parts of Speech Tagging, and Text to Speech Speech Recognition.
Data Science tools can make a data scientist’s life much easier and less stressful because they come with pre-built features that can be extremely useful to a data scientist.
So, that was the list, guys. I hope you enjoyed it and learned something new. Though there are numerous tools on the market, and I understand how confusing it can be, you need to filter out tools based on your needs and requirements.