Know The Top Seven Data Science Management Tasks What exactly is Data Science Management? Data scientists are information scientists, statisticians, natural scientists, social scientists, or mathematicians with extensive training. They solve problems, deviate from well-worn paths, and count the countable. In addition, they provide insights into complex processes, analyze massive datasets, and confront previously unsolved problems. In many ways, they help save time, automate processes, and build the future. However, they occasionally become so engrossed in solving problems that they lose focus. This is where the data science manager comes in. Data science management, not data science, is a subset of management. Data science managers represent and live the company's vision and goals. Managers must empower people, encourage teams, and steer and inspire those people to achieve this. They are most effective when they can avoid micromanaging their teams, stay focused on the big picture, and translate the real-world application of a project to data scientists and the results to everyone else. Furthermore, they must have a basic understanding of data science fundamentals which can be learned in an industry-accredited data science course.
Data Science Managers' Responsibilities Some tasks and duties are recurring in the daily business of a data science manager and must be tracked. The procedures are similar to those in a typical software engineering project; however, some differences stand out. 1. Management of Requirements The first step in most data science projects is to consult with stakeholders to determine their requirements. This primarily concerns gathering information and comprehending real-world business problems. It is critical to discuss expectations, which should eventually answer the question: What will be different for stakeholders once the data science project is completed successfully? The requirements that have been recorded must then be translated into analytical tasks for the data scientists. These tasks must be broken down into manageable chunks. The data scientists can discuss the technical or scientific depth. This could be accomplished by organizing all of the items into a backlog and writing user stories, as is standard in software development. 2. Time and Resources When dealing with complex problems, it is common to encounter uncertainty. Simultaneously, complexity must be reduced to estimate the project budget and, thus, the
available funds. It may be useful for stakeholders to put a price tag on user stories, bits of requirements, or project phases by estimating time and effort. However, dealing with complexity entails dealing with a variety of unknowns. It is best practice to include a time buffer in estimates based on the level of uncertainty. To get a first impression, sort user stories on the canvas of uncertainty.. The people who bring the necessary skills to a project must be present. In addition, they must have the time to run it. It is critical in this case not to overburden people with multiple projects. The more tasks they work on, the more time they waste on transaction costs. Due to context switching, approximately 20% of working time is lost per additional project [Weinberg 1975]. Having only one person with a specific skill may risk the entire project if, for example, this person becomes unable to work or leaves the company. Furthermore, data accessibility must be clarified ahead of time. Nothing is more inefficient than data scientists and other teams. 3. Promotion The project's progress and results must be presented to stakeholders in such a way that everyone is on the same page. The data science manager must be prepared to answer no longer one-dimensional questions. People become more judgmental as the product or solution evolves because they can use more senses than just their imaginations, as in the requirements assemblage. 4. Frame and Context Let us return to the team. Everyone involved must understand the project's road map, vision, and time frame. This includes a thorough understanding of what is going on and the responsibility to speak up if something is off track. It can be difficult to be a pessimist at times, especially when dealing with data scientists who love to solve problems and lose track of time as they dig deeper. However, not all problems are within the scope of the business. It is critical to demonstrate empathy and explain why there is another focus in this situation. 5. Facilitation of Communication Facilitating communication between data scientists, stakeholders, and other potentially involved people is a fundamental task for every data science manager, as it benefits the project. Most importantly, all parties involved must share a common understanding of processes, methods, and goals.
6. Team Bubble Problems should be kept as far away from data scientists as possible to provide them with the best environment in which to work. Coding Days or reserving the morning hours for concentrated work may be beneficial. However, the team bubble may be unrealistic because tasks and requirements must be refined with stakeholders. Data science managers do not always have all the information at their disposal, so data scientists must participate in meetings. 7. Project Management Fundamentals
Management essentials must be considered in most data science projects. A timeframe can be created once time and resources have been determined (see above). Depending on the preferred project management methodology, this may be more or less difficult. Some things are critical, such as: ●
●
Maintaining track of goals, monitoring overall progress, and controlling financial resources; ensuring the quality of both the process and the results; managing documentation, to-do lists, boards, and meetings Considering vacations, sick leave, advanced training times, and keeping the team productive.
To know more about the responsibilities of data science managers and data scientists, check out the data science course in Mumbai, and learn job-ready skills.