An Overview Of Agile Data Science: An In-Depth Guide

The data science industry is massive and thriving, it is evident when we consider the quantity of technological advancements we have today. It enables everyone involved in the data science process to work faster without the risk of errors. If you are curious to learn more about the different data science tools and software especially how Agile works, then you’re on the right page. It is a complex field that requires intense focus to learn as well as hard work to apply the knowledge to practical scenarios that bring change. It all depends on your curiosity and dedication towards the discipline, because you overcome the first step, the challenges that lie ahead will be easier to get through. In this article, you will learn about the different sides of Agile Data Science.

How Can We Trace the History of Data Science?

The term ‘data science’ has not always been a thing even if its roots can be traced to computer science and a few other disciplines. It had come into existence in the 1970s and was used as an alternative to computer science before it was formed into a separate field of study.

It was made distinct to define the survey of data processing methods used in a range of different applications. Peter Naur who was one of the pioneers, defined the data science field as something useful to data and data processing.

It derives from its applications in building and handling models of reality. However, it was not before 2001 that William S. Cleveland distinguished the ‘data science’ industry as independent. Today, it is one of the highest-paying career paths in India.

Modern data science incorporates several disciplines including computer science, statistics, and mathematics to bring out results of higher quality. As a data scientist, you gather raw data from the set and interpret it for decision-making purposes.

The major disciplinary areas within data science comprise data mining, machine learning, analytics, and programming. These areas have a role to fulfill before you get the results.

When data is gathered it is usually in huge quantities and the term to describe it is known as ‘big data’. There are several terms used in modern data science that you’ll come across.

• Machine learning is a term describing the science behind computer automation so that it can learn from experience instead of relying on explicitly programmed rules.
• A corpus is a large structured set of texts and each set of documents is viewed as a mixture of topics.
• Deep learning is something similar to machine learning and refers to the computer’s ability to imitate the workings of the human mind when it comes to processing data.

Modern companies and organizations depend on data science and skilled data scientists to bring it into action. Not only does it enable them to keep track of sales data of products or services, but also performs consumer analysis to gain a better understanding of current market demands.

For example, some big brands like Netflix can mine ‘big data’ to determine what the consumers are looking for and deliver products that will satisfy their needs.

The industry is evolving at a rapid pace and its applications will continue to enhance the lives of many individuals and shape the future of several brands.

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What is Agile in Data Science?

In general terms, ‘agile’ refers to being swift in your work process while in data science it’s a software development methodology that anticipates the need for flexibility.

It applies to working in a lightweight or highly responsive way so that the product or service is delivered to the customer exactly when they want it, including a specific time.

Manufacturing companies have always worked in traditional pre-planned processes when it comes to product development. However, sometime after the 1980s, there was a need to eradicate the inflexibility in traditional work processes.

Thus, companies began to incorporate and embrace speed, flexibility as well as overlapping work processes rather than settling for linear, rigid, and distinct project phases. These facts were brought to light by Takeuchi and Nonaka in 1986.

From then onwards, the movement picked its pace and codified into the Agile Manifesto, in the year 2001. Considering modern-day standards, it has brought about massive change because Agile practices are a staple for software development and are being adopted into practice across industries.

Today, Agile data science approaches the data science industry for web application development. It is simply not about accurately shipping working software, but how to better align data science with the organization.

It offers to address the misalignment of data science and engineering which results in creating the ‘pull of the waterfall’. Thus, these principles bridge the gap between the two teams, creating a stronger alignment as a result.

The principles of agile philosophy are merged with the data science practices but do not compromise the natural data science life cycle. It respects the boundaries of data science as a discipline, creating a highly exploratory process centered around scientific experimentation.

Some examples of Agile’s applications in data science can be observed through Scrum, a framework that emphasizes iterative development alongside regular stand-up meetings.

There is also Kanban which aims to manage and control the flow of features involved in the process. The software can be developed in a large cycle instead of smaller iterations.

What is the Agile Manifesto?

Effective Agile software development would not have been a reality if the Agile Manifesto, which is a brief document built on certain values and principles, hadn’t existed.

It was the creation of software development practitioners who had foreseen the need for alternative document-driven development processes. It goes as follows:

• Changing requirements should be welcomed, even when it’s late into development. An agile process can harness change for the customer’s competitive advantage.
• Delivery of working software should take weeks or months with a preference for a shorter timescale.
• Business people as well as developers must work together every day throughout the project.
• The organization must build projects around motivated individuals, provide them with the environment, support their needs, and trust them to get the job done.
• The most effective and efficient method of information exchange takes place through face-to-face conversation.
• Working software is the primary measure of development progress.
• An agile process promotes sustainable development. There has to be a constant pace which should be maintained indefinitely by sponsors, developers, and users.
• Continuous attention is needed for technical excellence alongside good design which enhances agility.
• Simplicity is the art of maximizing the amount of work done, which is essential.
• The best architecture, designs, and requirements emerge from the self-organizing teams.
• At regular intervals, the team must reflect on becoming more effective. The team members must tune and adjust their behavior accordingly.

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Types of Agile Data Science

Just like any other field, there is more than one approach or type for Agile data science. It is a great fit for enhancing the natural way in which data science works. It is non-linear for its lack of clear understanding and ambiguousness which are built for situations involving agile process.

Listed Below Are Some Types of Using Agile for Data Science:

Scrum: It is popularly known as an agile framework meant for getting work done. This process uses all the core principles found in agile data science to define clear methods for facilitating a project.

However, it does not constitute the literal meaning of Agile and many different methodologies have to be taken to be considered an agile approach to project management. Scrum effectively manages agile software development.

It is often contrasted with the ‘waterfall’ approach, emphasizing up-front planning and scheduling of activities followed by execution. Its key concepts revolve around –

• Self-organization of processes because every individual must be independent.
• Collaborating with a full focus on awareness, articulation, and appropriation.
• Giving priority to tasks that have value and need to be completed on time.
• Time assorted for each process which is known as ‘sprint planning’. It must have specific start and stop timings.
• There should be an understanding that a project needs to undergo multiple refinement procedures. Thus, iterative software development allows teams to make adjustments and have control over the changes.

Agility in Big Data: whether it’s working with agile data science or any other data-related field, you are bound to encounter huge data sets. These are commonly known as ‘big data’ and can be a complex task to handle without expertise.

However, with proper applications of agile big data, you gain a methodology that helps with conquering the unpredictable realities of creating analytics and its applications from large amounts of data.

Agility in data processes generally utilizes the ability to distribute and consume data efficiently so that organizations can respond quickly while being effective. It is essential for several reasons, some of which are:

• Rapid response rates that keep pace with the dynamicity of the data itself. It is crucial because realistically speaking, business needs aren’t constant either and data scientists are expected always to be updated with the latest changes.
• Agility in data helps you avoid problems that may occur in the future. It makes you adept and well-suited to the dynamic nature of your business because you’ll be able to foresee the errors that may set you back.
• It also enables you to make better decisions based on your data. When you understand the insights presented to you and can process data quickly, you are capable of forming informed decisions that can take your business forward.

Extreme Programming (XP): Among several types of tools and software recommended for agile data science, you have Extreme Programming, or XP as it is called in short. This program is an agile project management methodology that aims at providing better results in software quality and increased quality of life for the team behind it.

It targets simplicity and speed with shorter development cycles. Moreover, it operates according to five guiding values, rules, and 12 practices for programming. The common roles it plays in agile data science include the customer, coach, developer, tester, and tracker.

Among these, the customer offers valuable feedback and represents the user’s end of needs while the coach guides an entire team on XP principles and has a hand at improving them.

Sprint Review in Agile Data Science: A better part of agile methodology is dedicated to where the team demonstrates their work and gets feedback from customers or stakeholders. It all starts with planning and priority meetings, then aligning the data team with the needs of the organization.

To better understand it, you can say sprint reviewing is a collaborative meeting that typically takes place at the end of every ‘sprint’. The team runs through their work, compiling pieces of information during iteration work cycles or sprints.

The involved individuals on the other end can remain updated on the progress and also give their feedback. The review period is generally not long and can last up to a few hours. This allows it to be scheduled in between packed work cycles.

Kanban: Agile data science also requires a guided project management framework that is provided by Kanban. It relies on different visual tasks to maintain the workflow and can be distinguished from Scrum which helps teams structure or manage work through a set of values, practices, or principles.

Visualizing the workflow with Kanban gives you a heads-up on potential tasks and features and facilitates the work process. To make everything go smoother, the work is divided into three parts consisting of a ‘to do’ list, an ‘in progress’ list, and a ‘completed’ list.

Based on it, the team decided to pick up their tasks. Sometimes, when working on software the team might decide to further divide the ‘in progress’ into testing and development, or split the ‘testing’ into verification and validation.

Kanban also takes steps to reduce the ‘in progress’ list by setting limitations to the maximum number of tasks that can simultaneously exist. The four principles of Kanban are listed below:

• Visualize the work to make it simpler
• Laying down limitations for work-in-progress
• Focusing on the flow of work
• Working towards continuous improvement

Crystal: This is more inclined to be a flexible approach in agile software development, its design is adaptable to the unique needs of the team and organization.

It prioritizes your communication, collaboration, and flexibility, making sure that you can faster deliver high-quality software. The methods are color-coded to priority aspects as this framework is generally utilized for short-term projects by team developers working out of a single workspace.

Other big platforms like Udemy provide you with complete guided courses on the Crystal Agile framework, so you can learn it better. These are aimed towards team leaders, developers, and anyone else who is seeking to enhance their understanding of agile development.

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Can Agile Work With Data Science?

One aspect of working as a data scientist is overcoming the challenges in this field. Agile data science may not always fit well, which means there will be instances where agile processes will not be applicable at all.

The outset of the end solution is not known while the experiment may help you realize that the initial hypothesis is wrong and needs recovery. This eventually leads to another set of hypotheses and experimentation.

Through agile methodology and process you promote empiricism and help resolve complex adaptive problems like what current data science teams are facing.

Agile gains its true potential when data scientists apply it for planning or prioritization at the start of each sprint, define tasks with deliverables and timelines, and set retrospectives at the end of each sprint.

Clearly defined tasks could help you analyze various aspects of customer experience like delivery referring to timeliness or package condition, product referring to ratings or reviews, customer services like wait times or several touch points and app metrics referring to spam notifications, loading time, or a confusing UI system.

Answering these questions provides milestones for data scientists to achieve during their rigorous work process.

What Challenges Are Faced When Agile is Applied to Data Science?

Agile methodologies stand out when it comes to modern software development in the fast-paced technology-driven environment, but that does not mean the same thing for data science.

Agile is driven by its iterative nature, promises continuous delivery, resilience in the face of changes and much more. When it comes to data science, a field defined by depth, rigor and systematic exploration things are different.

There are increased chances of messing up the program pace because data science demands a rhythm of exploration. It requires validation and refinement and doesn’t always fit well into fast-paced cycles like agile demands.

You might be familiar with Scrum technologies similar to the engineering context, but most data scientists and project managers seek well-defined outcomes or deliverables. This is why applying agile in data science might get complicated.

There is an involvement of in-depth analysis where someone expects an answer or machine learning models that contribute to measurements in certain metrics. The acceptance criteria are hard to define while scoping tasks.

Other than that, the data science field is partly research-based which generally takes some amount of time to acquire perfection.

Most of the time, the field of data science demands to seek answers for problems that are ill-defined. Sometimes the answers are not straightforward and you might not be sure which data is to be used.

You’ll have to decide upon a dataset, estimate the efforts required for data exploration, work on clearing and preparing the data, work towards feature engineering, and assess multiple models, and only then can you achieve the target metric.

Agile data science is not functional with data science because these projects aren’t always like software projects, where you can see the tangible outputs at the end of a sprint. Here you often need to extend durations of data exploration before framing any form of meaningful insight.

The in-depth data exploration can be contradicting when added to Agile’s speed-based techniques and frequent deliverables. When the team gets too accustomed to using agile data science techniques, it could be a problem.

Stakeholders involved in the business are responsible for overseeing the projects and picking out the ones that make the most impact on the customer and business outcome, in a short amount of time.

On the other hand, they are more focused on the day-to-day tasks or the projects which are near-term goals. In most cases, when you have set priorities by the business it may lead to certain risks of being too overworked in a short time.

This leaves out several opportunities for you to innovate the project which could have helped you get done with more times the current improvements.

Agile data science appreciates a mix of versatile roles but the data science field is well-suited to specialized expertise. It can be in either data engineering, machine learning, or statistical analysis but not everything at once. The inherent specialization aspects do not align well with Agile’s collaborative and interchangeable team dynamics.

FAQs

Q) Is the Data Science Industry Rewarding?

Yes, data science is an advantageous career path and you get opportunities to learn and grow in your job sphere. Considering modern economic standards in India, data science holds full potential for being the career path for today’s youth.

Q) How Can I Describe Data Science?

Data science is an interdisciplinary field that studies data and interprets it into meaningful insights. Modern tools and techniques such as artificial intelligence, machine learning, and advanced analytics enable to have better results.

Q) Can Agile Help With Data-driven Decisions?

Yes, Agile techniques can help with data-driven decisions. It enhances the improvement rates, optimizes sprints, and propels product innovations that ultimately lead to improved products in the market.

Conclusion

The digital age has introduced many new technologies and brought us closer to many great things. The field deals with expert analysis of data and framing interpretations from it and most tasks performed by data scientists have to be automated for faster work procedures.

The field has always existed in the past through different subjects before it was established as an independent discipline. In this article, we have discussed essential facts about the Agile data science industry so that you can keep yourself updated.