{"id":3360,"date":"2022-12-16T20:29:04","date_gmt":"2022-12-16T15:29:04","guid":{"rendered":"https:\/\/dicecamp.com\/insights\/?p=3360"},"modified":"2022-12-19T12:08:55","modified_gmt":"2022-12-19T07:08:55","slug":"no-code-data-science","status":"publish","type":"post","link":"https:\/\/dicecamp.com\/insights\/no-code-data-science\/","title":{"rendered":"Data Science is for everyone! (Build AI in minutes with no-code)"},"content":{"rendered":"\n

Image by <\/em>Fauxels<\/em><\/a>\/Pexels<\/em><\/p>\n\n\n\n

While it’s an irrefutable fact that data science<\/a> brings immense benefits for businesses in the form of remarkable efficiency and accuracy in decision making, it\u2019s also true that it brings with itself the toil of crafting a recondite pipeline. And to build a working analytics system, data scientists master the inter-disciplinary knowledge that extracts the crux of otherwise out-of-context data.<\/p>\n\n\n\n

Now, as we\u2019ll make it clear to you, organizations want to leverage a simple and intuitive way towards data science that ideally empowers anyone to harness its powers. For this, technology has created a non-technical paradigm of data science: the no-code Data Science<\/em>.<\/p>\n\n\n\n

With no-code Data Science<\/strong>, it literally becomes possible for anyone to apply data science principles and process data into legitimate insights as accurately as with code. Combined with data literacy, users can drag and drop data nodes on the project screen and build an AI within minutes. Just that\u2019s how simple it is.<\/p>\n\n\n\n

This article disseminates everything about no-code Data Science- covering the context of no-code technology and its working, best no-code AI tools, citizen data scientists and how to become one, and a note on the future of Data Science with no-code platforms. <\/p>\n\n\n\n

Stick by to discover how aspiring professionals can learn no-code Data Science at the comfort of their homes with live-sessions by certified experts. <\/em><\/p>\n\n\n\n

Jump to your Intended Topic in no-code Data Science<\/h2>\n\n\n\n

1- What exactly is Data Science?, Skills, and Supply Gap.<\/a><\/p>\n\n\n\n

2- The no-code Movement and Data Science.<\/a><\/p>\n\n\n\n

3- List of best no-code AI platforms, and details.<\/a><\/p>\n\n\n\n

4- Can I become a citizen data scientist? Pretty Easily!<\/a><\/p>\n\n\n\n

5- How no-code data science tools help organizations?<\/a><\/p>\n\n\n\n

6- No-code Data Science a threat to the job security of Data Scientists?<\/a><\/p>\n\n\n\n

What\u2019s Data Science?<\/strong><\/h1>\n\n\n\n

Data Science<\/a> is the application of machine intelligence to make business decisions in the shortest time and with greater wisdom. This machine intelligence is created by Data Scientists who blend statistics, artificial intelligence, and domain knowledge together over vast volumes of data. <\/p>\n\n\n\n

In simple terms, a data scientist rounds up relevant business data, cleanses and structures the data, and builds statistical models to look for unseen patterns. These patterns (as validated through historical data) act as knowledge for business managers and help them make business decisions<\/a>. <\/p>\n\n\n\n

What are the Skills of a Data Scientist?<\/strong><\/h2>\n\n\n\n

Primarily, a data scientist<\/a> requires hands-on expertise in: mathematics and statistics, programming languages (Python<\/a>, R and SQL<\/a>), and application domain knowledge (visit our dedicated career guide<\/a> if you wish to understand more about skills of data scientist). <\/p>\n\n\n\n

Scarcity in Supply of Data Scientists<\/strong><\/h2>\n\n\n\n

The toil aspiring individuals take to become professional data scientists, as well as the effortful work in constructing an analytics pipeline<\/a>, results in scarcity of Data Scientists. On the other hand, the demand for data scientists is more than ever, with companies intrigued to leverage the competitive advantage data science provides.<\/p>\n\n\n\n

This supply-demand gap<\/strong> becomes an inspiration for engineers to create tools that offer intuitive data science capabilities. They envision easier accessibility to data and analytics with non-specialists generating and creating complex statistical models with as little effort as possible. This gives rise to non-traditional data science roles that require abstract knowledge of data and analytics compared to the profound expertise in statistics and programming required by professional data scientists.<\/p>\n\n\n\n

Gartner termed these emerging data science roles as Citizen data scientists<\/a> and extrapolated them as dominant players who can maximize an organization’s D&A strategy<\/a>. Building on Gartner\u2019s view, no-code tools and automation<\/a> are two key drivers that can empower citizen data scientists in successfully conducting analytics operations. <\/p>\n\n\n\n

<\/p>\n\n\n\n

Read further as we deep-dive into what\u2019s no-code Data Science.<\/p>\n\n\n\n

What\u2019s no-code Data Science? It all started with the no-code Movement<\/strong><\/h1>\n\n\n\n

The no-code movement <\/strong>emphasizes technology usage for everyone, offering non-technical persons to build things such as websites and data analytics applications<\/a>. The movement solely exists to empower every individual to implement their unique ideas and use the power of technology in easiest ways. Note: to gain first-hand account, visit the no-code platforms such as MarkerPad<\/a> and Nocode.tech<\/a>.<\/p>\n\n\n\n

As the no-code movement expanded across technology disciplines, data science gurus also saw greater enablement, a platform idea that makes it possible for anyone to build data science models without having to code. Using these platforms, implementing data science workflows has become extremely easy and quick thanks to the drag-and-drop layout. There\u2019s literally no coding<\/strong> involved that was earlier required to write and execute each step along the data science hierarchy (including feature engineering and model building).<\/p>\n\n\n\n

So how does no-code Data Science work?<\/strong><\/p>\n\n\n\n

No-code data science platforms leverage the power of programming (at the backend) to create intuitive drag-and-drop functionalities at the user interface. The four core characteristics<\/strong> of a no-code data science platform are:<\/p>\n\n\n\n

  1. Visual programming<\/strong> that provides graphical layout with drag-and-drop capabilities. Users pick a component (or node) and drop it on the project window before building their logical data hierarchy.<\/li><\/ol>\n\n\n\n
    1. Additional flexibility of embedded code editor as in Data Query<\/a>, for customization of processes<\/strong>.<\/li><\/ol>\n\n\n\n
      1. Provision of APIs<\/strong> that lets users import their no-code prediction data to their applications, or visualization tools<\/a> such as Power BI and Looker etc.,<\/li><\/ol>\n\n\n\n
        1. Hundreds of pre-built analytics components<\/strong> such as data readers, data joiners, and complex statistical models etc., save hours of time and effort for data science teams.<\/li><\/ol>\n\n\n\n

          AI is now possible without coding! The Best no-code AI platforms for all business users<\/strong><\/strong><\/h1>\n\n\n\n

          Following is a list of top no-code AI platforms that cover all applications of artificial intelligence including Data Science (Learn Data Science vs. AI<\/a>). As you\u2019ll witness below, with these tools, data science is possible without python.<\/p>\n\n\n\n

          Microsoft Lobe<\/strong><\/h2>\n\n\n\n

          Microsoft takes the no-code machine learning experience for non-specialists to an ultimate level with its Lobe application. It comes as a downloadable, free application that has an incredibly creative yet super intuitive graphical layout that makes sense of ML to any layman.<\/p>\n\n\n\n

          Users start with uploading their data onto Lobe. This is example data (also known as historical data) using which users make an ML model learn and discriminate between a variety of examples. Once imported, Lobe automatically trains a ML model (also selected by Lobe) and displays a model performance report. This is where users can see and verify if the ML model actually predicts in the way they expect it to. For example, users can upload new data if the model displays wrong predictions. Once fixed, use Lobe to export the model onto any application. <\/p>\n\n\n\n

          Download and train your ML model on the go using <\/em>Lobe<\/em><\/a>. Watch how Lobe trains an ML model in minutes time with its super intuitive layout .<\/em><\/p>\n\n\n\n

          Akkio<\/strong><\/h2>\n\n\n\n

          Akkio markets itself as one of the best no-code data science tools in that it offers fastest model training in a work environment that\u2019s highly intuitive. It\u2019s with these features that professionals from sales, marketing, and finance find Akkio as a promising no-code platform for predictions such as customer churn, subscription score and customer acquisition. <\/p>\n\n\n\n

          Akkio lets users upload .csv files or integrate data from sources such as Salesforce, Google BigQuery, and Google Sheets. Once uploaded, Akkio lists all variables from historical data and allows multi-select variables intended for prediction. It takes almost 30s for Akkio to train an ML model based on the historical data and automatically selecting the best model without needing users to select on their own. Because it\u2019s critical for users to learn how an ML model reaches a decision or in other words identify if the model is biased on a feature or a group of features, Akkio brings an intuitive model performance report. This insight report shows model performance metrics, percentage of individual features contributing in the prediction, and even creates prediction cases. Users can directly connect Akkio\u2019s no-code ML model with data sources such as Web Application to predict data in real time. That\u2019s how simple ML is with Akkio.<\/p>\n\n\n\n

          \"Akkio
          A screengrab from Akkio\u2019s introductory video. Users can create a complete, end-to-end machine learning model in just four steps. Watch the full video: <\/em>Getting Started with Akkio\/<\/em><\/a>You Tube.<\/em><\/figcaption><\/figure>\n\n\n\n

          Visit Akkio to get a free trial of <\/em>Akkio Application<\/em><\/a>.<\/em><\/p>\n\n\n\n

          Google Auto ML<\/strong><\/h2>\n\n\n\n

          Another no-code platform in the list of best no-code AL tools is Google Auto ML. Auto ML covers the whole gamut of ML (ranging from data science to pure ML tasks), providing a simple, no-code solution for custom business needs. <\/p>\n\n\n\n

          Within just four simple steps, people with limited machine learning expertise can generate predictions on their custom data. Within Auto ML\u2019s User Interface, users begin with uploading their \u2018labeled\u2019 data set, then selecting \u2018train\u2019 on a custom ML model, \u2018evaluate\u2019 model-quality analysis and statistics, and select \u2018predict\u2019 to generate results on their data.<\/p>\n\n\n\n

          \"With
          A screengrab from Google\u2019s AutoML introductory video shows how users can create and deploy ML model in just six simple steps. Watch how <\/em>Google Auto ML<\/em><\/a> creates data science workflow within minutes\/Youtube<\/em><\/figcaption><\/figure>\n\n\n\n

          Google AutoML<\/a> is a paid application.<\/em><\/p>\n\n\n\n

          Amazon SageMaker<\/strong><\/h2>\n\n\n\n

          Amazon provides the best no-code machine learning experience for developers and data scientists alike with its incredible no-code AI tool called SageMaker. With its intensely rich machine learning capabilities, non-specialists, data scientists and developers save time and effort in data preparation, building, training, and deployment of models. <\/p>\n\n\n\n

          Starting with feature engineering, SageMaker Data Wrangler automatically builds features on users\u2019 selected data. Users can save different versions of features in the SageMaker Feature Store and select and use them for model training. Now, trusting an ML is another problem that SageMaker gracefully handles with its SageMaker Clarify. It suggests a balanced feature set by checking for issues such as: biases towards a single feature. An individual feature\u2019s role can also be inspected using Clarify. Once a model is trained, the SageMaker Debugger identifies improvements in an ML model by measuring for example CPU memory, and number of violations on training data. Finally, SageMager pipelines helps data scientists and developers to automate the whole ML development process in a single click.<\/p>\n\n\n\n

          See how <\/em>Amazon SageMaker<\/em><\/a> expedites a data science workflow within minutes.<\/em><\/p>\n\n\n\n

          Users can try Amazon SageMaker for free for 2-months after they sign-up to AWS Cloud. <\/em><\/p>\n\n\n\n

          KNIME<\/strong><\/h2>\n\n\n\n

          Competing with top no-code AI platforms, KNIME Analytics platform successfully delivers the simplicity and ease of building machine learning algorithms for data science applications (check out KNIME Reviews<\/a> at Gartner Peer Reviews). It\u2019s a visual, no code analytics platform that lets all business users create powerful machine learning algorithms by simply dragging data nodes on KNIME workbench.<\/p>\n\n\n\n

          Users pick visual data nodes as per business requirement and drop over KNIME project screen. A logical sequence is created within minutes.<\/p>\n\n\n\n

          \"KNIME
          An example of a KNIME data science workflow that creates a no-code data visualization in less than 3 minutes. Mimicking a code-heavy SQL operation, the visual sequence in KNIME makes it easy to: integrate two types of data together through Joiner; and create a data summary using GroupBy. A scatter plot is created using the Scatter Plot node and data is saved using Excel Writer\/<\/em>KNIME<\/em><\/a>.<\/em><\/figcaption><\/figure>\n\n\n\n

          KNIME Analytics Software<\/em><\/a> is absolutely free to download and use.<\/em><\/p>\n\n\n\n

          As you’ve studied the above best no-code data science tools, we don’t want to miss yet another top no-code platform that’s loved by its users. Obviously AI<\/em><\/a> is a complete data science team and offers capabilities that merge and clean data, and perform statistical work.<\/em><\/p>\n\n\n\n

          Become a citizen data scientist or simply save time and effort in the tedious building of ML algorithms. <\/strong><\/h1>\n\n\n\n

          At Dice Analytics<\/a>, KNIME L2 certified instructors teach in an 8-weeks complete KNIME analytics program, taking you through basics of Data Science to advanced use cases such as anomaly detection, fraud detection, and social media clustering.<\/p>\n\n\n\n

          Visit detailed <\/em>course outline<\/em><\/a> for <\/em>no-code Data Science<\/em><\/a> at Dice Analytics.<\/em><\/p>\n\n\n\n

          How no-code Data Science serves organizations?<\/strong><\/h1>\n\n\n\n

          No-code Data Science primarily serves those organizations who lack key resources <\/strong>such as data science professionals, technology infrastructure, and time <\/strong>in developing a mature data analytics ecosystem. Further, no-code data science helps business executives gain an intuitive view of Data Science, leading to quality data governance<\/strong>. <\/p>\n\n\n\n

          Learn more about the <\/em>right way of leveraging Data Science in business<\/em><\/a>.<\/em><\/p>\n\n\n\n

          The idea of no-code data science is also backed by credible market analysis. According to Gartner, citizen data scientists<\/a> can \u2018accelerate\u2019<\/strong> organizations into AI and ML without spending huge costs and efforts in complex implementation. Equipped with the right tools, non-specialists as well as professional data scientists can perform intricate diagnostic analysis<\/strong> as well as create models that leverage predictive<\/strong> or prescriptive analytics<\/strong>, using simplified technology platforms (view Data Science tasks in 2020<\/a>).<\/p>\n\n\n\n

          Another Gartner Trend Insight report on low code technologies<\/a> highlights an interesting fact on the utility of no-code data science. According to the report, within an organization, the majority of employees constitute business technologists <\/strong>who have an average technology grasp. This chunk of the audience (avg. 41% among all employees) is already using low-code and no-code technology solutions<\/strong> for data and analytics. <\/p>\n\n\n\n

          Does no-code Data Science pose a threat to the job security of Data Scientists?<\/strong><\/h1>\n\n\n\n

          Although <\/strong>no-code Data Science<\/a> empowers non-technical persons to gracefully take out advanced analytics, it doesn\u2019t mean it\u2019s the end for professional Data Scientists.<\/p>\n\n\n\n

          1. As we\u2019ve stated in section 2, how the no-code platforms come with advanced functionalities such as code editors that let\u2019s data scientists write code to customize<\/strong> workflows<\/strong>. This brings more flexibility for professional data scientists who can use their coding skills and data science expertise in building competing data science solutions.<\/li><\/ol>\n\n\n\n

            Hence, no-code platforms truly facilitate a data scientist who can use their programming knowledge to craft immensely valuable and innovative data models that bring a powerful difference in their work.<\/p>\n\n\n\n

            1. Apart from the above, no-code platforms salvage data scientists from repetitive coding<\/strong>; building or revamping code for every new project. With no-code tools, data scientists can focus on the design of analytics pipeline making their work more meaningful and interesting. <\/li><\/ol>\n\n\n\n
              1. Another exclusive benefit of no-code platforms for professional data scientists is the use of subject expertise to create breakthrough data analytics environments for no-coders; such as with the open source<\/strong> no-code data science platform KNIME<\/a>. Data scientists can opt for developing their commercial proprietary tools, and supply service layers and support at KNIME.<\/li><\/ol>\n\n\n\n

                Concluding: Data Science is for everyone with no-code platforms<\/strong><\/h1>\n\n\n\n

                From credible market research and subjective analysis presented above, it\u2019s certain that the reins of data science is safe at the hands of no-code platforms. With no-code data science it becomes extremely simple to create data models without writing a single line of code. This not only empowers citizen data scientists, but brings a lot more opportunities for professional data scientists as well. It\u2019s with the simplicity of technology that organizations now perform intricate diagnostic analysis as well as predictive or prescriptive analytics without investing plentiful resources.<\/p>\n\n\n\n

                Visit Dice Analytics\u2019 <\/em>no-code Data Science remote, live training<\/em><\/a>.<\/em><\/p>\n\n\n\n

                Watch the insightful demo-session on no-code Data Science using KNIME at Dice Analytics<\/strong><\/h1>\n\n\n\n
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