Top 10 Data Science Tools for Non-Programmers to Consider in 2021

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by samhitha


November 21, 2021

Check Out These Data Science Tools: No More Programming Worries

Data Science emerged as an advantageous choice for those who want to suppress, control and produce experiments from huge volumes of data. There is a monstrous demand for data scientists in all industries, which has drawn many non-IT and non-programmer experts into this field. If you want to become a data scientist without being a coding ninja, get your hands on some data science tools.

You don’t need any programming or coding skills to work with these tools. These data science tools provide a constructive way to characterize the entire data science workflow and execute it with almost no bugs or coding errors.

RapidMiner

RapidMiner is a data science tool that provides a coordinated environment for different cycles of innovation. This incorporates machine learning, deep learning, data preparation, predictive analytics, and data mining. It lets you tidy up your data and run it through a wide range of statistical algorithms. Suppose you need to use AI rather than conventional data science. All things considered, the automatic model runs through a classification algorithm and searches through various boundaries until it finds the best fit. The aim of the tool is to provide many models and then distinguish the best one.

data crawler

DataRobot engages data scientists at all levels and fulfills an AI stage to help them build and deliver accurate prescient models in a short time. This step prepares and evaluates 1000 models in R, Python, Spark MLlib, H2O and other open source libraries. It uses many mixtures of algorithms, preprocessing steps, elements, modifications, and tuning limits to convey the best models for your datasets.

Chart

Tableau is one of the best data visualization tool which allows you to break down raw data into a usable and understandable format. It has some brilliant features including a drag and drop interface. It facilitates tasks such as sorting, comparing and analyzing, efficiently.

Tableau is also compatible with multiple sources, including MS Excel, SQL Server, and cloud-based data repositories, making it a popular data science tool for non-programmers.

Minitab

Minitab is a software package used in data analysis. It helps to capture statistical data, manipulate that data, identify trends and patterns, and extrapolate answers to existing problems. It is among the most popular software used by businesses of all sizes.

Minitab has a wizard for choosing the most appropriate statistical tests. It is an intuitive tool.

  • Simplifies data entry for statistical analysis
  • Manipulate the dataset
  • Identifies trends and patterns
  • Extrapolates the answers to the existing problem with the products/services.

Trifacta

Trifacta is considered the secret weapon of data scientists. It has an intelligent data preparation stage, powered by AI, which speeds up the overall data preparation process by about 90%. Trifacta is free independent programming offering an intuitive graphical interface for cleaning and manipulating data.

In addition, its visual interface shows errors, exceptions or missing information without additional work. Trifacta accepts input data and evaluates a plan with different statistics per section. For each section, it automatically suggests some changes.

Data Wrapper

Datawrapper is an open source web tool that enables the creation of essential interactive diagrams. Datawrapper expects you to stack your CSV dataset to create pie charts, line graphs, bar outlines (levels and verticals) and guides that can be effortlessly installed on a site.

  • Datawrapper requires no design or programming knowledge
  • To work with Datawrapper, you only need your data and that’s it
  • Datawrapper takes care of choosing an inclusive color palette
  • Select multiple types of charts and maps and insert annotations.

KNIME

Knime or Konstanz Information Miner is a monstrous information processing device. It is mainly used for authoritative big data study. It’s based on the Eclipse scene and it’s incredibly adaptable and amazing.

Characteristics

  • Create visual workflows: intuitive drag-and-drop GUI
  • Combines tools from different domains with native KNIME nodes in a single workflow, including writing in R and Python, machine learning, or connectors to Apache Spark.
  • Many very intuitive and easy to implement data manipulation tools
  • Well-documented, step-by-step workflow
  • Application optimized enough to comfortably handle large volumes of data

IBM Watson Studio

Watson is an artificial intelligence platform from IBM that will allow you to fuse AI tools into your data, regardless of where it is hosted, whether on IBM Cloud, Azure or AWS. It is an integrated data governance platform that serves to find, plan, understand and use data effortlessly. You can get and order important data, such as keywords, sentiments, sentiments, and semantic tasks from messages, chats, and conversations.

Characteristics

  • AutoAI for faster experimentation
  • Advanced data refinement
  • Support for open source laptops
  • Built-in visual tools
  • Model training and development
  • Extensive open source frameworks

Google Cloud AutoML

Google Cloud AutoML is a platform for preparing excellent custom AI models with negligible effort and limited machine learning expertise. It makes it possible to build premonitory models capable of surpassing all the usual calculation models. It uses a basic graphical interface to prepare, evaluate, improve and submit models based on the accessible data creating excellent training data. It therefore builds and sends the best AI models on structured data.

BigML

BigML facilitates the most common way to build machine learning and data science models by providing quickly accessible constructs. These constructs help with ranking, relapse, and grouping issues. BigML joins a wide range of machine learning algorithms. It helps to assemble a robust model without much human intervention, allowing you to focus on fundamental tasks, for example, improving decision-making.

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