Data Science is the art of obtaining valuable insights from data. This is the methodology and process behind the top technologies like AI and ML. Overall, Data Science is all about understanding the data process and extracting value from it. In this regard, Data Scientists are the Data Science professionals who deal with a large amount of data on daily basis.
Data Scientists perform various functions within any business entity. Among these functions include identifying the right data or information, gathering data from different sources, and organizing data. Further, they transform this data into useful insights and communicate the same with the management of the company for making better decisions.
On an overall note, we can say that Data Science is a combination of different tools and ML principles. This aims to locate hidden patterns from raw data.
Data Science tools are the most useful tools used by professionals to perform their daily tasks. There are many tools available, either open-source or premium, within Data Science that help Data Scientists well.
Let us go deeper into this blog for Data Science tools to learn in 2020 for beginners and professionals too.
Data Science tools 2020
The following are the best Data Science tools to learn in 2020 and to use them professionally to process data.
KNIME
This is an open-source, reporting, and data analytics tool to understand the data and its design pattern within DS. It helps designing Data Science workflows and also many reusable components with accessibility. Moreover, it also integrates new developments regularly. The features of the KNIME tool include the following;
Data Blending:
The tool KNIME helps to blend data of simple formats like PDF, CSV, XLS, and other kinds of unstructured data. It also links to databases and warehouses to integrate data from different platforms. Moreover, it recovers data from various sources like Google sheets, Azure, etc.
Workflow Creation:
It helps to build creative and intuitive workflows with visualization and drag-drop GUI applications. This tool doesn’t need to code anything. This tool allows users to develop, model workflow, and controls the data flow with updates. Furthermore, it also combines tools from different domains.
Data formation:
KNIME helps to derive statistical data like mean, deviation, etc. and the same into workflows. Moreover, it also gathers, sorts, and filters data on the local system, or in the Big Data ecosystem. It also cleans data through the normalization process and detects the range values with detection procedures.
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Rapid Miner
The Rapid Miner tool under the DS platform built for non-programmers for quick data analysis. This tool includes the functions of building models, data building, validating data, and its deployment. It helps in importing various ML models along with several web apps like Android, iOS, etc. Moreover, it resolves complex tasks like data mining and analysis. Rapid Miner loads data from different platforms and frameworks like Hadoop, Cloud, NoSQL, etc.
After collecting data from different sources it starts processing and prepares useful data using various industrial methods. Furthermore, it offers a user interface to link with predefined blocks of data.
The tool has four different categories such as Rapid Miner- Studio, Radoop, Server, and Cloud.
Rapid Miner Studio offers statistical modeling, visualization, and data building facility. The Radoop version offers the application of Big Data functions. Rapid Miner Server offers central repositories for data. Finally, the cloud category offers cloud-based repository services.
Apache Hadoop
Hadoop is a popular open-source Big Data framework with having a presence in DS also. It is useful to build easy to use coding models with distributed processing facility across system clusters with large data sets.
It is a highly scalable platform under data analysis that locates systems failures and handles them at the application layer. Furthermore, it comes with different modules like HDFS, Common, Map Reduce, YARN, etc.
SAS
The SAS integration software suite is designed for large org’s to use for data analysis and modeling statistic data. Moreover, this is very useful to access data in any format. It is a specially designed software for advanced analytics, BI, data management, and analytics forecast. Furthermore, it is also useful through SAS programming or GUI platforms.
MS Excel
Excel is a highly accepted and widely useful Microsoft Office product/tool for data analytics. It is very much in use across every industry. This tool offers multi-task performances like summarizing data, analyzing data, pivot tables, data filters, and many more. It also offers several formatting features to the users.
Xplenty
This is an ETL platform with a data combination facility that brings all the resources in one place. It offers a complete kit for developing data pipelines. Moreover, this is a cloud platform with elasticity and scalability for data processing, combination, and data building for analytics purposes. This DS tool offers a wide range of solutions to different departs like sales, marketing, developers, consumer support services, etc.
Its sales solution features include understanding customer behavior, data enrichment, applying sales tools to achieve metrics, using CRM organically. The customer support platform for this tool offers better support to customers. This support helps the business platforms to make good & informed decisions.
Python
Python is a high-level object-oriented scripting language and open-source tool of Data Science. Further, Python is easy to study language similar to JavaScript, PHP, etc. Moreover, it also consists of various machine learning libraries like Theano, Tensorflow, Keras, etc. This DS tool has great functionality with automatic memory management.
The tool offers free-to-use data analysis libraries and is very extensible. It also provides a good number of packages useful to DS professionals.
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R Programming:
R programming is one of the important analytics tools within DS that is extensively useful in data modeling and statistics. It helps data scientists to model and manage data easily with diverse methods. Moreover, this programming tool is compatible with different platforms such as Windows, Mac OS, and UNIX.
The major features of R programming are; cross-platform support, distributed computing, ML capability, supports other languages, etc. Moreover, it interfaces with other databases easily and runs code without the support of the compiler. Using this tool, Data science professionals can make good performance and achieve better results.
Matplotlib
Matplotlib is a data science tool and a plotting & visualization library designed for Python and NumPy tools. Although, the tool SciPy also uses Matplotlib and its interface is similar to the data science tool MATLAB.
However, the best feature of this tool is its ability to build crucial graphs & charts by simple lines of code. Further, users can use this tool to design bar plots, scatterplots, and any other kind of graphs and charts generally. The tool comes with an object-orient API for implanting plots into various apps using general-purpose GUI toolkits. Moreover, it is the perfect tool for beginners willing to learn data visualization within Python.
Tableau
This is another Data Science/BI tool that is useful in data visualization and comes with prevailing graphics. The tool is useful for data scientists for designing the best visualizations for any data presented. Moreover, it mainly focuses on the BI functionalities that any industry uses.
The major feature of this Tableau is its interaction with databases, spreadsheets, and OLAP cubes. Moreover, it includes collaboration & sharing data, maps, ask data, high- security, easy mobile view, etc. This is mostly useful for individuals as well as for industry people also. Most of the teams within and business entity uses this tool for their daily operations like for any manufacturing unit data, IT co., etc.
There are some of the best features of Tableau are data mixing and real-time data analysis. Besides, Tableau also can present geographical data as per need. It consists of various offerings such as Tableau Prep, Tableau Desktop, Online, and Tableau Server to fulfill the different needs of users.
Splunk
Splunk is another DS tool that involves analyzing and searching system-produced data. It helps in pulling out text-based data where users can pull all sorts of data to carry out easy functions like the mathematical analysis. The tool helps to access machine data that comes from web apps, devices, or data produced by users.
Moreover, it is useful in diverse formats. This tool locates various data patterns, diagnoses issues, provides metrics, and super-intelligence to business ops. It is basically useful for app management, security, and business and web analytics purposes.
The best features of Splunk are searching, modeling, pivot charts-tables, reports, and indexing, data ingestion, sharing & exporting, etc.
Data Robot
This is an automated Machine Learning platform under Data Science mainly useful for data scientists, IT professionals, and executives. Moreover, DataRobot is an end-to-end enterprise-level AI platform that helps to test and operate a simple data model. It empowers many business analytics people to build easily deploy highly perfect ML models. Besides, Data Robot doesn’t require writing a single line of code.
The machine learning models offered by Data Robot provide deep analysis and forecasts for making better and informed decisions. Further, this tool offers an easy deployment process, allows parallel data processing. Includes Python SDK, and provides model optimization.
Jupyter
This is a Python-based open-source/free tool useful for developers for developing open-source software. The Jupyter tool supports various programming languages such as Python, R, etc. Moreover, it is most useful for composing live codes, visualizations, presentations, etc. and also the most commonly demanding Data Science tool. Besides, the tool provides an interactive environment for data scientists to complete their tasks easily.
This is the best free to use tool for data scientists with a good range of options to use in their tasks.
Scikit-learn
It is a Python library for machine learning algorithms that is commonly useful for evaluation and data science is easy for execution. It’s an ML system that supports a wide range of features including information pre-processing, clustering, regression, classification, etc. Moreover, the tool simplifies the crucial ML codes and easy to learn the optimal platforms.
TensorFlow
This is also a Python-based, end-to-end, open-source/free to use platform for Machine Learning within Data Science. It is a complete and flexible environment including tools, libraries, and community resources that eases fast and easy mathematical calculations within ML. Moreover, the TensorFlow tool enables users to easy ML models building, training, and applying ML models anywhere. It includes a clear and flexible architecture that encourages the development of the best models. The tool helps users to work much faster than other tools using for their tasks.
Alteryx
This is an end-to-end faster implementing data analysis platform that allows Data Scientists to solve issues much faster. It offers a platform to prepare discovers and analyzes the data smoothly. Furthermore, it also helps users to find a wide range of insights by deploying and distributing the analytics at scale.
The features of the Alteryx tool include;
The platform allows users to include Python, R, and Alteryx models into their various processes.
It offers several functionalities to gather and analyze the model.
The tool furnishes the features to identify the data and plan it across the organization for allocation. Moreover, this platform also enables the management of users, data assets, and workflows with central control.
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BigML
This is another data science tool that is widely used by data scientists and analytics professionals. The tool provides an interactive, cloud-based user interface ecosystem for processing machine level algorithms. Furthermore, it offers standard cloud-based software solutions for IT people. It enables businesses all over different areas of their enterprise to use ML algorithms widely. This tool is an advanced modeling expert that uses a wide range of algorithms for ML including clustering and categorizing. Users can build a free or premium account based upon their information needs with the help of the BigML web interface with Rest APIs.
Summing Up
Thus, we have gone through the various Data Science tools to learn in 2020 and apply them for many uses. Data Science being the mix of various instruments with hidden patterns of other techs and models offers the best solutions. Especially for Data scientists, it is much useful that makes their job much simple and easier. Most DS professionals use analytics tools along with data science tools as a mix. ML algorithms also play an important role in this regard.
So, I hope you got the basic and overall idea of these tools and techniques. Get more practical insights into these tools by taking up Data Science Online Training from IT Guru experts.