The 15 best tools every data scientist should put to work
September 13, 2021
Here are the tools that will help every data scientist to show his sharp mind and innovation in this competitive world.
The data science and data scientist job market is constantly evolving. Every year there are so many new things to learn. As some tools emerge and others fall into oblivion, it becomes essential for a data scientist to follow trends and have the knowledge and skills to use all the tools that make their work easier.
Here are the 15 best tools every data scientist should use to become more efficient at their job.
Knowledge of problem solving
For a data scientist, his mind is one of the best tools to stay ahead of the competition. Because data science is the area where you face different obstacles, bugs and unexpected issues every day. Therefore, if you do not have problem solving skills, it will be difficult for you to continue your work.
Programming languages allow data scientists to easily communicate with computers and machines. They don’t need to be the best developers ever, but data scientists need to be strong. Python, R, Julia, and SQL, and more, are the programming languages widely used by data scientists.
This handy data science tool is an enterprise-grade arrangement that meets every expected requirement for AI and machine learning. With DataRobot, data scientists launch everything with a few clicks and support their organizations with components, for example, robotic AI or time series, AI tasks, etc.
TensorFlow is crucial if you are interested in artificial intelligence, deep learning, and machine learning. Built by Google, TensorFlow is basically a library that helps data scientists to assemble and prepare models, etc.
With the help of Knime, data scientists can integrate things like machine learning or data mining into datasets and create visual data pipelines, models, and interactive views. They can also perform data extraction, transformation and loading with the intuitive graphical interface.
In data science, statistics and probabilities are crucial. This tool helps data analysts understand what they are working with and guides their exploration in the right direction. Understanding the details further ensures that the analysis is valid and that there are no logical errors.
AI and machine learning
Companies always prioritize data scientists who know machine learning. AI and machine learning empower data scientists to analyze large volumes of data using data-driven, automation-aided models and algorithms.
Data science involves a lot of precise communication, so having the ability to tell a detailed story with data becomes very important. In this case, data visualization can be essential to your job, as analysts rely on charts and tables to make their theories or conclusions easier to understand.
RapidMiner is used to prepare models from the initial data preparation to the very last stages, for example, the analysis of the deployed model. As an end-to-end data science package, RapidMiner offers massive help in areas such as text mining, predictive analytics, deep learning, and machine learning.
Python is one of the most powerful programming languages for data science due to its large collection of libraries like Matplotlib and its integration with other languages. Matplotlib’s simple graphical interface enables data scientists to create compelling data visualizations. With multiple export options, the data scientist can easily transfer their personalized graph to the platform of their choice.
D3.js allows data scientists to use features to create dynamic data analyzes and visualizations in browsers and it also uses animated transitions. By combining D3.js with CSS, a data scientist can create beautiful transient visualizations that help implement custom graphics on web pages.
To simulate fuzzy logic and neural networks, every data scientist uses MATLAB. It is a multi-paradigm digital computing environment that assists in the processing of mathematical information. MATLAB is a closed-source program that makes it easy to perform tasks such as algorithmic implementation and statistical modeling of data or matrix functions.
Excel is probably the most widely used data analysis tool because MS Excel is not only useful for spreadsheet calculations, but also for processing data, visualizing and performing complex calculations. For data scientists, Excel is one of the most powerful analysis tools.
Nowadays, organizations that focus on software development make extensive use of SAS. It comes with many libraries and statistical tools that can be used to model and organize data. SAS is a very reliable language with strong support from developers.
Apache Spark is one of the most used data science tools today. It was designed to handle block and stream processing. It provides data scientists with numerous APIs that help make information re-accessible for AI or capacity purposes in SQL and others. It’s definitely a huge improvement over Hadoop, and it can run several times faster than MapReduce.
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