Organizations use data to derive insights that influence business decisions. As data increasingly becomes an asset for organizations, the demand for Data Scientists has continued to grow.
Low Code platforms have helped democratize data science. It has made data more accessible to people by enabling non-coders to build applications without advanced programming skills.
They provide features that ease how cloud storage works, data generation, analysis,
Continue reading to know more about Low Code and whether or not it is what Data Science needs in the future.
Understanding Data Science
Data Science is a field that involves using large data volumes and tools like algorithms, systems, and scientific methods to extract meaningful insights for decision-making or problem-solving.
Data Science professionals are trained in machine learning technologies to identify patterns and make predictions and forecasts.
While data science has been prominent in research, its application extends to other fields. HR professionals employ data science during recruitment, while marketing teams use it to analyze data and gain insights into customer behavior and experience.
What Is Low Code Data Science?
Low Code is similar to Data Science but is more accessible to citizen data scientists. These are non-technical individuals who desire to perform the same tasks and functions as data scientists.
With Low Code platforms, users accomplish tasks through visual drag-and-drop components when creating applications. The technique reduces the need for hand-coding and makes it easier for developers to build applications and software.
Low Code Platforms make the integration of third-party software seamless. Besides requiring little technical knowledge, Low Code is ideal for individuals who wish to automate the app development process by replacing manual coding with a drag-and-drop function for a faster building process.
Unlike No-code, Low Code still needs input from a programmer to integrate it successfully with other systems. No code, on the other hand, requires zero skills as you can customize your software or applications to your needs using building blocks.
Uses of Low Code Data Science
Low code is applicable in a variety of ways by professionals and newbies. Below are specific ways you can use Low Code technology in Data Science.
1. Data Collection
The first step in Data Science is data collection. Low Code technology can integrate APIs for data collection from different sources on the web. Whether you’re collecting data from an internal database or a SaaS system, Low Code can help.
On the same note, you can increase your data sources, which improves your algorithms. Resources you can use for data collection include Octoparse and Bright Data’s Data Collector.
2. Data Cleansing and Preparation
Most data is usually unstructured. To gain meaningful insights, you should structure and prepare your data by correcting errors and eliminating duplicated, incomplete, or inconsistent data.
Low Code platforms help with data structuring and preparation by cleaning data through automation. You can use drag-and-drop functions to keep it readable using bots.
Helpful data cleansing tools include OpenPrise Data Cleansing and Merge and Dedupe by Clodingo.
3. Data Visualization and Gathering Business Insights
Low Code platforms allow you to gain business insights from data and Machine Learning models.
You can also visualize data and create user interfaces and graphs using formats based on your data sets. It is especially useful for quarterly reviews or auditing the company’s data footprint.
4. Training, Deployment, and Testing of Machine Learning
There is more to Low Code data than collecting, organizing, and preparing data. Low Code platforms facilitate the training and development of Machine Learning solutions that you can use to analyze patterns and trends. You can also train Low Code chatbots to engage with customers.
Low Code libraries for deep learning include PyCaret, Runway ML, and Teachable Machine. Although some Low Code libraries for training and deploying ML may require knowledge of Python or R for optimal results, others are more codeless.
5. Developing Recommendation Systems
When using a platform like Netflix or YouTube, you usually get recommendations on what to watch next. This is possible with the help of a recommendation system consisting of complex software that suggests relevant content based on your interests.
While you can use Low Code to build a recommendation system, you require math skills and logic for efficiency.
With the above uses, it is no doubt that Data Scientists can leverage Low Code platforms to find solutions to common problems.
Benefits of Using Low Code Programming
Low Code technology has numerous benefits that make it attractive for users interested in Data Science. Below are the advantages of using Low Code
(I) Increasing Demand for Apps
Apps and software are the big things in the current digital world. Research shows that the demand for new apps will continue growing up to 5 times faster than IT capacity to deliver.
As demand for app development increases, Low Code programming allows citizens to meet the growing demand. This frees up developers’ headspace and enables them to focus on other critical issues.
Usually, when businesses don’t get what they want, they resort to third-party solutions and shadow IT. Low Code, therefore, helps monitor shadow IT.
(ii) Faster Development Process
Low Code platforms and tools offer speed, which quickens the development process as you don’t have to write custom code. You can create apps and software much faster, as it takes 2 to 3 days to develop enterprise apps. This is because the platforms contain templates, business process models, and drag-and-drop functions that make the development process much faster.
Considering the speed, you can create different solutions for various platforms in one go within a short time. Considering the rate of technological advancement, Low Code allows you to innovate faster.
(iii) Low Development Costs
Low Code platforms are inexpensive for businesses who want to experiment with ideas that otherwise could be costly.
Also, the demand for technical resources makes them expensive for IT departments. Low Code is a more affordable option in terms of research, resources, and time. You also don’t have to hire a costly full-time programmer or incur high maintenance costs.
Low Code tools provide a flexible approach to data construction, enabling users to bring together data from various sources. Low Code integration functionalities can help you achieve a unified data view by connecting on-premise and Software-as-a-Service systems.
Also, Low Code systems allow businesses and organizations to make changes to applications quickly and easily, thus promoting agility. With the drag-and-drop function, you can redesign and update applications easily. Doing so helps you avoid risks and utilize market opportunities.
(V) No Need for Advanced Programming Skills
The online world is rapidly changing and has more personalized demands. With Low Code platforms, anyone can create a functional website or app with little technical skills regardless of background. You don’t need expert programmers as you can utilize your internal resource to develop applications.
Organizations can assign individuals familiar with app development to develop a solution without hiring outside talent. Also, different departments can collaborate as there are no organizational boundaries.
Since Low Code shortens the development process and utilizes automation, it saves on costs and increases productivity. Users can complete large projects fast and have more time for more creative ideas and innovation. The workload also reduces and provides more time for the business to perform more research, resulting in a better customer experience in the long term.
Limitations of Low Code Data Science
While Low Code has its benefits, it also comes with the following shortcomings relating to Data Science.
- Customization: It can be challenging to create highly customized customer algorithms and models through Low Code. You may have to rely on traditional methods to get the best results for complex data issues due to the limited number of functions you can implement with Low Code.
- Debugging Challenges: Unlike traditional coding, where you can easily see where the code breaks down, Low Code can be difficult to debug in case of errors.
- Scalability: Optimizing Low Code for performance can be challenging as you may not get the same efficiency as traditional coding.
Low Code is undoubtedly the future of Data Science as it increases the accessibility of analytics and data management. It can potentially make work easier for Data Scientists by saving them time and resources and boosting their efficiency in processing tasks.