Introduction to Data Science Tools
The Data analytics tools can aid an organization to deliver a special value and bring the data back to life. A great deal of hard work goes in the process of the extraction and transformation of the data into a format that is usable. On the accomplishment of this stage, the data analytics courses can offer to the users a greater level of insight into their business, customers, and industry. There can be three major groups of data analytics which offer different types of insight:
The Traditional Business Intelligence (BI) offers the traditional, recurring data analysis and reports.
The Self-Service Analytics enable the end users to design their own analysis within the framework of the IT offered data and tools.
The Embedded Analytics offers the company business intelligence within the limits of a traditional company application, such as the HR system, CRM, or ERP. Such analytics tools offer context-sensitive decision support system within the users’ normal workflow.
It has been said that if an individual is not using any of the analytics tools, they should start using one for data science applications. It has been predicted that by the year 2019, the self-service analytics, as well as the BI users, will be able to generate more analysis of data as compared to the data scientists. Below mentioned are the top 15 data science tools to work with n 2019
Different Data Science Tools
Tableau possesses a robust functionality along with a great speed of insight. Having the connectivity to a large number of different local as well as cloud-based data sources, the Tableau’s intuitive software combines the data sourcing, its preparation, exploration, analysis, and the presentation in a streamlined format.
The Looker makes an attempt to give a unified data environment as well as a centralized data governance with significant weight on the reusable components for the data-savvy users. Using the extract/load/transform (ELT) technique, Looker provides its users with the ability to model and to transform the data the way in which they require it.
This data science tool provides with a modern and dynamic reporting with the out-of-the-box integrations to a large number of the world’s most famous on-premise and cloud-based ERP systems. This is an easy-to-use report writer which provides Excel, web, as well as mobile interfaces, and offers the finance professionals with the powerful financial dwell as operational reporting abilities in a variety of formats and presentation layouts.
The Dataiku Decision Support System blends most of the data analysis life cycle, all in a single tool. This enables the analysts to source and prepare the data, create predictive models, amalgamation with the data mining tools, build visualizations for the end users and to set up the ongoing data flow so as to keep the visualizations appear to be fresh. The Decision Support Systems’ collaborative environment enables the different users to work as a team and to share the knowledge. All of this happens within the DSS platform.
It is an open-source, enterprise-wide analytics platform. The KNIME is made by keeping the data scientist in mind. The KNIME’s visual interface has nodes for every task. Right from the extraction to the presentation of the data, with more weight being done on the statistical models. KNIME has the ability to integrate with many of the other data science tools such as R, Hadoop, python, and H2O, along with many of the other structured and unstructured data types.
RapidMiner puts specific emphasis on the speeding of the insight for a complex data science. The visual interface includes a pre-built data connectivity, workflow, as well as machine learning components. With the integration of R and Python, the RapidMiner automates the data preparation, the model selection, the predictive modeling, as well as the what-if gaming. This platform even helps to accelerate the “behind-the-scenes” work with the help of a combined development as well as a collaborative environment and the integration with big data platforms like Hadoop and Spark.
This data science tool emphasizes on the data collection as well as blending it with the other data sources like ERP and CRM systems, as well as big data tools, for instance, Hadoop and NoSQL. It has built-in integration with the endpoints and a unique metadata injection functionality that speeds up the data collection process from a variety of sources. The Pentaho’s visualization capabilities go from the basic reports to the complex predictive models.
The Talend’s toolset is used to accelerate the data integration projects and the speed time in order to value. It is an open-source tool. It comes equipped with wizards that help to connect to the big data platforms such as Hadoop and Spark. It has integrated toolset as well as a unique data fabric functionality to enable self-service data preparation by the business users. By making the process of data preparation easier for the users who have an understanding of the business context for the given data, Talend helps to remove the IT bottleneck on the usable data. This helps to reduce the time that is needed to merge the data sources.
The Domo aims at the speed of insight for the less technical users. It has 500+ built-in data connectors as well as a visual data preparation interface which allows the user to accelerate the process of data sourcing and transformation. It has robust business intelligence capabilities that enable visualization and social commenting in order to facilitate the collaboration. The Domo also has a native mobile device support with the similar analysis, annotation, as well as collaboration experience as a desktop.
The Sisense provides an end-to-end analytics platform along with a strong governance as a component. It provides the visual data sourcing as well as preparation environment, along with the alerts which notify the users the moment when a given metric falls out of a configurable threshold. It runs with private-cloud, on-premises, or the Sisense-managed environments, and it enables the governance at the user role, object, & the data levels.
The Qlik emphasizes the speed of insight by automating the data discovery process and the relationships that exist between the multiple data sources during the data acquisition and preparation process.
The Microstrategy is considered to be one of the oldest data analytics platforms. It has the robustness which one can expect from such a mature software. The Microstrategy connects to a number of enterprise assets such as the ERPs and the cloud data vendors. It integrates with multiple common user clients such as iOS, Android, and Windows.
The Thoughtspot has a search engine-like interface along with an AI to enable the users to adopt a conversational approach to the data exploration and analytics.
The Birst aims to solve one of the very vexing challenges in the field of data analytics which is that of establishing trust in the data from a number of different sources existing within the organization.
The SQL Server Reporting Services (SSRS) is considered to be a business intelligence and reporting tool which tightly amalgamates with the SQL Server Management Services, the Microsoft data management stack, and the SQL Server Integration Services. The toolset enables the users to get a smooth transition from the database to the business intelligence environment.
The popular vendors stated above support multiple users across a large range of industries. But, the volume of the data which is generated by the traditional business activity, the social media, and the IoT technology continues to increase every year. In such a case the data analytics options continue to develop and change.
The key to making an informed decision is to understand the specific analytics needs of the organization and the industry. Understanding where the seeds fall on the path to the analytics spectrum will aid in productively engaging with the vendors and also make the most out of the analytics that is produced.