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He agile methodology it was discovered in early 2001 when 17 people met to discuss the future of software development. It was founded on 4 core values and 12 principles.
It is very popular in the ever-changing and fast-paced tech industry, which reflects that very well. It’s a perfect data science project management method, as it allows team members to continually review project requirements, go back and forth, and communicate more as the project grows. The model evolves to reflect user-centric results, saving time, money and energy.
It’s better to make decisions about changes during different phases of the data science lifecycle, rather than at the end, once everything is complete. Let’s talk about the 2 steps you can take to get your agile data science project management up and running.
Scrum
An example of an agile method is Scrum. The scrum method uses a framework that helps create structure in a team using a set of values, principles, and practices.
For example, with Scrum, a data science project can break its largest project into a series of smaller projects. Each of these mini-projects will be called a sprint and will consist of a sprint plan to define goals, requirements, responsibilities, and more.
Why is this beneficial? Because it helps different team members to be accountable for their tasks to complete a sprint. All completed sprints play an important role in the ultimate business goal, for example launching a new product.
Employees focus on delivering value to end users by being able to discover solutions to challenges they may encounter through sprints.
Tools for Scrum include:
kanban
Kanban is another example of an agile method. It is a popular framework that originates from a Japanese inventory management system. Kanban shows employees a visual status of their current and pending tasks. Each task, also known as a Kanban card, is displayed on the Kanban status board and represents its life cycle to completion.
For example, you might have lifecycle columns such as work in progress, developed, tested, completed, etc. This can help data scientists identify bottlenecks earlier and reduce the level of ongoing tasks.
Kanban is considered to be a very popular framework in the data science world, with many data enthusiasts embracing the method. It is a lightweight process that is visual in nature to improve workflow and easily identify any challenges. It’s an easy method to implement, and data scientists are very responsive to “What’s 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 cannot stress enough how important this point is, which is why it is important to learn to walk before you run. Having a tool like Monday or Jira is great, but you won’t get anywhere if you don’t plan.
Holding discussion sessions between you and your employees so everyone is on the same page, everyone understands what needs to be done, and everyone has the same plan in mind is essential. Lack of planning can cause failure to meet deadlines, lack of motivation and productivity of employees, as well as the unfeasibility of the project.
Once everyone is on the same plate, you can move on to the next step.
team design
The next phase is to design your project, and this is based on the conversations you had with your employees. All of the aspects that your team covered in your planning discussions will help you design an effective solution for your current task.
Communication is your greatest tool during this phase. Other members of your team may have different ways of working or compartmentalize tasks. Therefore, it is your responsibility as team members to design a solution that meets everyone’s needs, based on their working method, availability, etc.
During this phase, you can assign who will own what 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’s their responsibility to make sure it runs smoothly, meets deadlines, and everything goes according to plan.
Build your solution
This is where your discussions, planning, and design show up. You may think at this point that you no longer need to communicate with your team members and you can get to work. That is not true. This is where communication matters most. Weekly meetings are important, they help all employees stay informed and interact with each other.
During the development of your solution for the task at hand, you will encounter challenges or bottlenecks that can be very overwhelming and will disrupt your schedule and the ability of others to complete their tasks. Communicating each successful and failed step is important to keep all members informed, and allows people to help you.
test, test, test
If you’re working on data analysis, creating an algorithm, or producing a new business product, you’ll want to give it a try. And then test it again, and definitely test it some more.
There’s nothing wrong with making sure you’re as accurate as possible when it comes to data science projects. The team members not only invested their time and energy into this solution, but it would be even better if it was accurate and solved the problem at hand.
The last thing you want to do is go back and forth because your results aren’t as accurate as they were in the first round.
Deploy
One of the proudest moments during a data science project. Communicate with team members to put together the latest increment in production, before it’s available to live users.
Data scientists need to think of one place as delivering the solution to the customer next. It is important to review, document, fix, and discuss the entire data science project, and the ups and downs.
Because let’s face it, a similar project will come along, and instead of having to start from scratch, you have documentation from your previous projects to provide a springboard for your next project. It is these reviews and documents that will be used in the first step of the discussion/planning of your next data science project.
Making sure you have the right tools to succeed in agile data science project management is one thing. But being able to get the most out of each phase is even more important. Communication is important, you will know that now as I mentioned it to you 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 aria He is a data scientist, freelance technical writer, and community manager at KDnuggets. She is particularly interested in providing Data Science career tips or tutorials and theory-based knowledge about Data Science. She also wants to explore the different ways that Artificial Intelligence is or can benefit the longevity of human life. An enthusiastic student looking to expand her technological knowledge and her writing skills as she helps mentor others.
