Team Work Processes in Data Science Discussing the processes of team work in Data Science and machine learning model building, covering topics like process levels in companies, payment methods for teams, budget allocation on projects, project prioritization, project implementation process overview.
Company Management Levels Impact Exploring management levels within a company and how it affects specialists. Starting from strategic goals to key performance indicators (KPIs) alignment with overall objectives. Decomposing goals into departmental targets quarterly or yearly based on chosen approach within the company.
Companies with ML Engineers & Data Scientists Teams Diving into different types of companies with ML engineers and data scientists teams: product-based firms focusing on revenue generation through recommendation systems; project-based organizations developing solutions using a structured approach; outsourcing works where external entities handle specific tasks for clients' projects.
Effective Project Management Understanding the importance of project management methodologies like waterfall, iterative, and incremental approaches. Key focus on delivering unique results within time and budget constraints while satisfying stakeholders.
Iterative Development Process Exploring iterative development where features are incrementally developed with varying quality levels before presenting to users for feedback. Contrast between incremental approach focusing on high-quality components from the start.
Modeling Approaches in Design Overview of modeling methods such as flow model, emphasizing continuous task cycles that can be applied iteratively or incrementally based on evolving requirements.
Methodologies in Action Comparison between traditional project management practices like PMI and Agile frameworks SM & Scrum highlighting engineering practices over process-centric models.
Unified Approach for Equipment and Software Integration Combining equipment and software in a unified plan is crucial for project management. Project managers should have knowledge of tools used in each area to ensure effective execution.
Challenges with Fixed Price Projects Fixed price projects may not be suitable for most data science projects due to their exploratory nature. Adapting to new insights during the project progression is essential, which fixed pricing models may restrict.
Investment Committees in Large Corporations Large corporations often divide big projects into smaller stages with investment committees reviewing progress every three months. Changing planned hypotheses mid-project evaluation is usually discouraged.
Scrum Framework Usage in Project Management 'Scrum' framework emphasizes cross-functional teams, product owners, and Scrum Masters overseeing sprint cycles lasting 1-4 weeks. Daily meetings focus on tasks completed, planned actions, issues faced within short timeframes.
Sprint Review & Retrospective Practices 'Sprint review' showcases team's work progress while 'Daily Meeting' addresses current status and challenges briefly daily; 'Retrospective' involves group feedback sessions post-sprints aiming at process improvement.
Data Preparation & Experiment Design Initial steps involve defining business goals translated into machine learning objectives followed by data collection/evaluation processes before model training begins.