A Compressive Guide How to Manage Data Science Projects

More data science projects have increased the demand for data science professionals. Usually, any project manager can perform the job, or an excellent senior data scientist will be a fantastic data science manager. But, it is only sometimes valid. Data science management becomes necessary for an organization wanting a competitive advantage. However, the role of data scientists is to put the data analytics process into a strategic context so the organization can leverage the potential of data while working on its data science project.

Data science management aims to align work with business goals and make the team accountable for the outcome. It ensures that each team is allocated to its place within the same department or distributed group. Hence, in this blog, we dive into the key areas of managing data projects effectively.

Key Concepts of Data Science Management

Here in the data science guide, we mentioned some key concepts that every data scientist should consider while managing their projects effectively:

1. Know Data Science

Being a data scientist does not mean having expertise or complete knowledge about all previous experience. All you need is a better understanding of the work that can lead you toward completing the project. Although more than knowledge of data science projects is required, you must also comprehend the challenges while working on the project. For example, when you are working on a project, it may be completed quickly or take up 70% of the effort. So, to solve its issues, you need to set up the project timeline before starting to work on it.

2. Engage Stakeholders

To make any project successful, the team should comprehend and follow the rule of “work smarter rather than harder.” The first and foremost step for any data science management process is demonstrating the project goal and metrics to team members. However, explaining the goals and metrics will enable the workforce to provide the correct value to the product and client.

3. Never Assume a Good Data Scientist Is a Great Manager

There is a vast myth surrounding data management that having excellent technical knowledge improves the chances of the data science management process. But, the reality is the opposite. Instead, it is noticed that data scientist repeatedly needs to translate their

technical knowledge into excellence. Moreover, not all team managers will lead and work as project managers.

For example, various data scientist professionals fear losing their technical skills, which might not use if they shift towards leading and managing the workforce. Therefore, they will need more data science management if given a management role.

4. Manage Workforce

A good data scientist manager is responsible for managing the project and the people on the team. Good data management is curious, humble, and willing to listen and discuss problems and successes with others. Everyone on the team must understand that no matter how well-informed they are, they can only solve some issues. The collective team approach will yield better solutions and insights into the problems that must tackle than an individual.

5. Define the Process

A data science process that is effective is essential for data science management. The team that is working on the project will always discuss and approve the final approach. Discussions should also include frameworks like CRISP-DM that will help to structure communication between the data science team and stakeholders.

Tips for Data Science Project Management

1. Accessing Data Sets is Crucial

Success or failure in a data-science project depends on the data sets, not the intended outcomes. There may be a possibility that few datasets are varied and better than others. Sometimes, companies may hide the data behind regulatory walls and need help accessing it. Thus, it is crucial to investigate the ownership and take consent to access it from the beginning of the project.

2. Offer Deeper Context

If you want to bring the best value to the project and its success, it is crucial to include developers and designers in the project from its beginning. Using the best mind collectively under the same umbrella brings an understanding of the users, limitations, success, and workarounds. Thus, to manage market issues in some situations, then you require being aggressive about defining the below context:

· Evaluate business goals and success parameters to enhance the license revenue from new customers and minimize the churn rate.

· Search the key constraints and comprehensive use cases for your data science team. Point out the players and their roles in the task.

· Share the research and assets with the company and team members. For example, share the customer issues about its bad user interface and revenue projection with them.

3. Explain the Accuracy Required and How to Handle “Wrong” Answers

At the beginning of every data science project, accuracy is always a topic of discussion. We spend a lot of time and energy determining “somewhat better” than a coin-flip accuracy, but this is insufficient when we risk lives in medical prediction applications by introducing false negatives. Each data science project will have something that surprises you; whether it’s an entirely wrong answer or something new, we learn about the world. You only need the plan to review results by humans and escalate the issue when they seem unfair.

Conclusion

Over the past decade, the business has adopted data science over the past decade to gain valuable insights about its customers, stakeholders, market, and operation to gain a competitive advantage. The landscape of data science will constantly develop, so the organization needs to find approaches to stay ahead of their competition and it enables them to successfully completion of the project.