Data science is among the hottest and most sought-after skills in the tech industry right now. As businesses and organizations look to gain insights from data, they call on data scientists for help.
Today, the role of the data scientist is among the most lucrative jobs. Top companies are willing to shell out big bucks to hire skilled professionals with in-depth knowledge to turn raw data into actionable insights.
New data science courses and tools hit the market every day, and it can be difficult for a beginner to keep up. The advancements in data science are happening too quickly. Also, the field of data science is constantly evolving and thus any individual working in it has to stay updated.
If you are planning to start a career in data science or you are looking to update yourself, this article will be of great help to you. Here, we will be listing the top data science tools that have become increasingly popular among professionals.
List of Top 7 Data Science Tools
While there are several data science tools available today, the following ones have gained a lot of traction:
1. SAS
SAS is an analytics tool that is commonly used in the data science industry. SAS stands for Statistical Analysis System, and it allows data scientists to run statistical analysis on datasets. Because of the growth in the data science industry, data scientists are constantly on the lookout for new tools to aid them in their research.
SAS provides analytical software and services that help you turn your data into information, and harness the power of analytics to optimize business performance. SAS is a powerful tool that can be used for all phases of data analysis, from data management to data visualization. This makes SAS a great tool for anyone who wants to derive meaningful insights from raw data.
2. Apache Hadoop
Apache Hadoop is one of the most widely used tools for processing large amounts of data in a distributed computing environment; on clusters of commodity hardware.
Not only is it open-source, but it’s also free to use. It powers the biggest companies on the planet and is used by some of the biggest data warehouses.
3. Tableau
Tableau is easy to use, incredibly fast, and offers a great variety of charts and statistical graphics. You can create interactive dashboards and put your data online by sharing a link.
The best part is that you do not need to be an analytics guru to learn Tableau – it’s quite intuitive and simple to use even without having any prior knowledge of statistics.
4. BigML
BigML is one of the cutting-edge data science tools out there. It is packed with an array of algorithms that aren’t normally found elsewhere.
BigML can be used to predict future behaviors based on previous ones, optimize your supply chain, determine the best time to purchase items often used together, predict trends based on what is currently trending, etc.
5. KNIME
KNIME is one of the most comprehensive, easy-to-use data science tools available. Its flexibility makes it the preferred tool of many professionals. It allows you to quickly retrieve valuable information from raw data using its unique data visualization workflow.
KNIME Analytics Platform makes it easy for users to create and run workflows directly from business applications. It sets data free by connecting to all your data sources, providing intuitive visual analytics for fast exploration, easy modeling, and automated workflows that give you the answers you need to do your job faster—with less effort!
6. RapidMiner
Rapidminer is a data mining software that can be used to perform exploratory data analysis, followed by creating predictive models. It provides excellent visualization capabilities for the data being analyzed and the predictions being generated.
It is used to uncover hidden insights from data quickly and easily. It is used in many top universities, labs, hospitals for machine learning, market research, and drug discovery. Also, it is useful in the aerospace industry for computer vision research and the automobile industry for automated driving algorithms development.
7. Apache Flink
Apache Flink is an open-source stream data processing framework. It enables easy and fast development of continuous applications that process streams of data, such as micro-services for IoT, scalability in low-latency trading applications, etc.
Flink is written in Java and uses a modern dataflow programming model. Control flow abstractions such as filters, state stores, and windowing functions provide a simple but expressive way to build data processing pipelines. With its modern programming model and high-level APIs, Flink makes it easy to deploy sophisticated streaming applications in a large variety of domains.
Conclusion
So, we have reviewed the top tools for data science that you should start learning in 2023 to land a well-paying job. You can pick one or more data science tools as per your requirements and start learning them.
People Who Read This Article Also Read:
I am an experienced content writer and digital marketing expert, with a proven track record of writing engaging, informative content. My diverse expertise creates powerful marketing campaigns that generate ROI across a wide range of industries.