Should You Learn to Code for Data Analytics? – Code vs. No Code

  • Jeffrey Agadumo
  • June 6, 2023

Learning to Code for data analytics has become an incredibly valuable skill today. By writing specific instructions as text, we communicate with computers, which then translate and carry out those commands to help us reach new heights of innovation and progress.

With this in mind, those skilled at writing instructions, in turn, and can get a computer or device to act, are highly valuable in today’s job market.

Data analytics is no exception to this and may seem like a superpower reserved for those who speak the secret language of code, but is it really necessary to learn programming to harness the power of data?

In this article, we’ll explore the world of data analytics, breaking down the barriers between “code aficionados” and everyday individuals who want to make data-driven decisions. We’ll examine the tools and techniques available to you, whether you’re a coding maestro or a complete newbie, and help you determine if coding is a must-have skill for your analytics journey.

The Analytics Code Report!

It is difficult to determine the exact number of programming languages in existence today as new languages continue to emerge while others become obsolete. However, it is estimated that there are thousands of programming languages available, now although a much smaller percentage of these languages are mainstream and used on real projects, if we take into account the languages optimized for data analytics and that number comes down to only a handful of languages:

1. Python

Python is a widely used programming language in data analytics, known for its simplicity, flexibility, and vast open-source libraries. It offers an easy-to-learn syntax and provides various libraries like Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and visualization.

Python’s flexibility makes it useful for handling unstructured data and allows for seamless integration with other technologies, making it possible to build end-to-end data pipelines and automate workflows. As a result, it is an essential tool for data analytics professionals, enabling efficient processing, analysis, and extraction of insights from large volumes of data.

2. R

R is a popular programming language for data analytics and statistical computing. It provides various packages and tools for data manipulation, visualization, modeling, and machine learning. R’s strengths lie in its ability to handle large datasets, its flexibility in data visualization, and its ability to work with various data formats. R also has a large and active community of users, which ensures that it constantly evolves with new and innovative tools for data analysis. Overall, R is a powerful and flexible tool that has become essential to the data analytics toolkit.

3. Julia

Julia is a high-level, open-source programming language designed to be fast and efficient, particularly for scientific computing and data analysis. Julia’s syntax is similar to that of MATLAB and Python, and it offers several features that make it well-suited for data analytics, such as built-in data structures and advanced linear algebra capabilities.

Julia’s speed and performance are particularly advantageous for large-scale data analysis tasks, as it can easily handle complex algorithms and large datasets. Julia’s built-in package manager also provides access to a vast library of packages specifically designed for data analytics, such as DataFrames.jl, which provides a powerful data manipulation and analysis toolset.

4. SQL

SQL (Structured Query Language) is a programming language for managing and manipulating data in relational databases. It provides a way to store, retrieve, and analyze data stored in tables. SQL is widely used in data analytics because it allows for efficient and powerful querying of large datasets.

Data analysts can use SQL to extract insights from large volumes of data, filter and sort data, join tables, aggregate data, and perform various other manipulations. SQL also supports creating and modifying database objects such as tables, views, indexes, and stored procedures.

The No-Code Alternative: Data Management for Everyone

Data analytics involves examining raw data to identify patterns and draw conclusions. It is a powerful blend of various tools, techniques, and skill sets, including computer programming, statistics, mathematics, and data modeling. These components work together to collect, organize, and analyze data, allowing analysts to extract valuable insights and make informed decisions.

In times past, using a computer required writing some form of code, whether it was assembly language for calculations or command line code for giving instructions. However, as time has passed, many technologies have shifted towards more user-friendly interfaces that do not require coding skills. This trend has also affected the field of data analytics, where no-code tools have emerged as viable alternatives to traditional coding-based approaches.

These no-code tools allow non-technical users to easily import, clean, analyze, and visualize data and automate tasks and workflows. This has made data analytics more accessible and efficient, enabling a more comprehensive range of users to extract insights from their data without relying on coding expertise.

Some software that make use of no-code data management features include:

  • Tableau: A powerful data visualization tool that allows you to create interactive and shareable dashboards.
  • Datameer: A no-code data management tool that enables effortless handling and exploration of large datasets. You can seamlessly integrate, transform, and analyze data with its intuitive interface and drag-and-drop functionality.
  • Microsoft Power BI: A business analytics service providing interactive visualizations and business intelligence capabilities.
  • Alteryx: A self-service data analytics platform that enables users to blend, prep, and analyze data with a drag-and-drop interface.

Code vs. No-code: Pros and Cons

While no-code features can efficiently save time in data analytics, certain advanced features or data manipulations require coding for accurate execution. It is important to note that both code and no-code options have their strengths and weaknesses in data analytics, which we will examine in the following tables.


Code vs no-code - pros and cons of code


Code vs no-code - pros and cons of no-code

Although no-code features may be convenient, their limitations become more apparent as the complexity of the data sets and project requirements increase.

So, is the no-code approach an excellent way to start data analytics? Absolutely. However, as the projects become more complex, you will likely need to learn a programming language to accurately address these complexities with code.

The Best of Both Worlds with Datameer

Datameer is a data analytics platform that aims to provide the best of both worlds between code and no-code tools. It is designed to make it easier for non-technical users to work with data while still providing the flexibility and customization of code-based tools.

On the one hand, Datameer provides a no-code interface that allows users to drag and drop data into a visual workflow. This makes it easier for non-technical users to explore and analyze data without writing code or using complex query languages . On the other hand, the platform also includes a range of pre-built data connectors, so users can easily pull in data from various sources.

On the other hand, Datameer also allows users to write custom code and scripts in various programming languages, including Python, R, sometimes involving the use of Datameer REST API. This provides greater flexibility and customization for more complex data analytics projects. Users can also use Datameer’s scripting capabilities to automate workflows and perform more advanced data processing tasks.

Datameer’s hybrid approach empowers users of all skill levels to collaborate and work on data analytics projects together. By bridging the gap between code and no-code worlds, Datameer helps organizations unlock the full potential of their data and drive better, data-driven decisions.

Discover the power of Datameer with just one click – a perfect solution for both coding wizards and no-code enthusiasts!