Top 8 Data Analytics Tools To Leverage During 2020 And Beyond

With a fast-changing and continuously evolving data landscape, the birth of new technological advances is helping organizations better understand its impact. Today, data science stands as a sought-after career path, bringing people from various backgrounds to explore and leverage it to derive logical solutions. With new tools and programming languages being regularly pushed out, aspirants need to keep up with the latest technologies to stay ahead in the ever-competitive field. To keep you in the know, we have put together the tools you need to get a grip on. Top 8 Data Analytics tools to Leverage in the year 2020 SAS SAS is an integrated software suite that offers features like business intelligence, advanced analytics, predictive analytics, multivariate analysis and data management. A closed-source software it can be leveraged through a graphical interface, the SAS programming language, or Base SAS. Large organizations rely on SAS for both data analysis and statistical modeling. With its ease of use, it can access data in almost any format. Its array of dynamic statistical libraries and tools serves the purpose of data modeling and organization. Tensor Flow Tensor Flow is a Python-friendly, end-to-end open-source platform for machine learning. With a comprehensive, flexible ecosystem of tools, community resources and libraries, it lets researchers and developers quickly build & deploy powerful machine-learning applications. Keras For data scientists making the transition to Artificial Intelligence and deep learning, the Keras framework is an efficient tool. It is easy to use and a powerful Python library designed to evaluate helps with the easy development of deep learning models. And while there are multiple deep frameworks at your disposal, the Keras framework has an intuitive high-level API. New data scientists find it highly attractive as they can quickly prototype and develop new models. MATLAB MATLAB finds numerous applications in data science, allowing image and signal processing, along with simulating neural networks. A high-performance, multi-faceted digital computing environment, it is designed for processing mathematical information. The closed-source environment also allows matrix functions, statistical modeling and algorithmic implementation. Additionally, what makes it ideal for a range of data science applications is its easy integration for enterprise apps and embedded systems. Tableau Tableau is a secure, flexible and powerful end-to-end analytics and data visualization platform. What makes it sought-after is that it doesn’t demand any programming or technical expertise. Its ease of use and power-packed graphics have helped it claim its place in the business intelligence industry as a widely-used tool. The features that truly stand out are data collaboration, data blending, real-time data analysis and geographical data visualization. Depending on your needs, it has various offerings like Tableau Desktop, Tableau Prep, Tableau Online, and Tableau Server. Apache Spark Apache Spark sports a robust analytics engine uniquely designed to handle stream and batch processing. As opposed to other big platforms, Spark can process real-time data since it dominates cluster management: •With numerous machine learning APIs, it assists data scientists to make accurate decisionsrn•It includes APIs that are programmable in Python, Java, Scala and R. BigML BigML is a cloud-based GUI environment that processes ML algorithms. With the use and implementation of different ML algorithms, it finds its application in a variety of business functions and processes. For example, BigML can be leveraged for sales forecasting, product innovation and risk analytics. With the use of REST APIs, BigML can create user-friendly interfaces and enables interactive data visualization. It also comes with a whole host of automation techniques that accelerates workflows and the tuning of hyperparameter models. DataRobot DataRobot serves as a machine learning platform for aspiring and experienced data scientists that help build & implement accurate predictive models in the shortest time possible. Built on the prowess of some of the world’s leading data scientists, DataRobot Cloud requires an effortless approach to assemble cutting-edge prediction models. DataRobot Enterprise expands to new horizons with business features like flexible deployment, training, governance and world-class support. Are you ready to explore potential career opportunities with data analytics? If you are still not sure about its prospects, you can explore our data analyst training course offered by LearnAtRise. It will help you apply theoretical concepts to real-time projects under the guidance of an experienced team.