7 Steps to Mastering Data Science Project Management with Agile

In a data science project, you typically have the following management tools: Waterfall, Agile, and Hybrid Methodology. In this blog, in particular, we will focus on Agile.

7 Steps to Mastering Data Science Project Management with Agile
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What is Agile?


The Agile methodology was discovered in early 2001 when 17 people came together to discuss the future of software development. It was founded on 4 core values and 12 principles.

It is very popular in the fast-paced, ever-changing technology industry - which reflects it nicely. It is a perfect data science project management method as it allows team members to continuously review the requirements of the project, go back and forth, and communicate more as the project grows. The model evolves to reflect user-focused outputs, saving time, money and energy. 

It is better to make decisions about changes during the different phases in a data science lifecycle, rather than at the end once it's all complete. Let’s speak on the 2 steps that you can take to kickstart your agile data science project management. 




An example of an agile method is Scrum. The scrum method uses a framework that helps to create structure in a team using a set of values, principles, and practices. 

For example, using Scrum, a data science project can break up its larger project into a series of smaller projects. Each of these mini-projects will be called a sprint and will consist of sprint planning to define objectives, requirements, responsibilities and more. 

Why is this beneficial? Because it helps different members of the team be accountable and responsible for their tasks to complete a sprint. Completed sprints all play a major role in the end goal of the business, for example launching a new product. 

Employees focus on delivering value to the end-users by being able to discover solutions to challenges that they may come across through the sprints.

Tools for scrum include:




Kanban is another example of an agile method. It is a popular framework which originates from a Japanese inventory management system. Kanban shows employees a visual status of their current and pending tasks. Each task, also known as the Kanban card, is shown on the Kanban status board and represents its life cycle to completion. 

For example, you can have life cycle columns such as work in progress, developed, tested, completed, etc. This can help data scientists identify bottlenecks earlier on and reduce the level of tasks work in progress.

Kanban is deemed to be a very popular framework in the data science world, with a lot of data enthusiasts adopting the method. It is a lightweight process that has a visual nature to improve the workflow and identify any challenges easily. It is a method that is easy to implement, and data scientists respond very well to ‘What is your next task?’, rather than ‘What tasks do you have in your next sprint?’.

Tools for Kanban include:


Walk Before You Run


The first initial step in the agile methodology is to plan. Plan, plan, plan! I can’t stress enough how important this point is, and that's why it is important to learn how to walk before you run. Having a tool such as Monday or Jira is great, but you will get nowhere if you do not plan.

Holding discussion sessions between you and your employees, so that everybody is on the same page, everybody understands what needs to be done, and everybody has the same plan in their head is essential. Lack of planning can lead to missed deadlines, lack of motivation and employee productivity, as well as project infeasibility. 

Once everybody is on the same plate, you can then move on to the next step.


Design as a Team


The next phase is designing your project, and this is based on the conversations you had with your employees. All the aspects your team covered in your planning discussions will help you design an effective solution to your task at hand. 

Communicating is your biggest tool during this phase. Other members of your team may have different ways of working or compartmentalizing tasks. Therefore, it is your responsibility as team members to design a solution that caters to everybody's needs, based on their method of working, availability, etc. 

During this phase, you can allocate who will be owning which aspect of a project. This gives employees a sense of importance, which increases their productivity levels. Once an employee has been given ownership of a part of a task, it is their responsibility to ensure that it runs smoothly, meets deadlines, and everything goes as planned. 


Develop your Solution


This is where your discussions, planning and design show. You may think at this point you do not need to communicate with your team members anymore, and you can just get to work. That is not true. This is where communication matters the most. Weekly stand-ups are important, it helps all employees stay in the loop and bounce off one another. 

During the development of your solution for your task at hand, you will come across challenges or bottlenecks which can be very overwhelming and will alter your timeline, and other people's ability to complete their tasks. Communicating every successful and unsuccessful step is important to keep all members in the loop, and it allows people to give you a helping hand. 


Test, Test, Test


If you’re working on analyzing data, creating an algorithm, or producing a new product for the business - you will want to test it. And then test it again, and definitely test it some more. 

There is no harm in making sure that you’re as accurate as possible when it comes to data science projects. Not only did team members invest their time and energy into this solution, it would be even better if it is accurate and solves the problem at hand. 

The last thing you want to do is go back and forth because your results are not as accurate as they were in the 1st round. 




One of the proudest moments during a data science project. Communicating with team members to put together the latest increment into production, before it is available to live users.

Data scientists need to put their minds in a place as if they were handing the solution over to the customer next. Reviewing, documenting, fixing, and discussing the whole data science project, and the highs and lows is important. 

Because let’s face it, a similar project will arise and rather than having to start from scratch - you have documentation of your previous projects to provide you with a stepping stone for your next project. It is these reviews and documents which will be used in the first step of discussing/planning your next data science project. 


Wrapping it up


Ensuring that you have the right tools to be successful in your agile data science project management is one thing. But being able to get the most out of each phase of it is even more important. Communication is important, which you will know now as I mentioned a thousand times. But just to remind you, to reap the rewards, you have to work hard but that comes with a lot of communication.
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.