Even as many organizations express an urgent need to develop in-house data science capacity by hiring and managing data science teams, many struggle to attract high quality applicants or to properly gauge the preparedness of applicants to perform the necessary tasks on the job. Though the volume of content geared towards the budding data scientist appears to be growing at an exponential pace, material targeted to those tasked with leading teams of data scientists has failed to keep pace.
As such, questions abound: Which job boards do data scientists read? What interview questions should they be asked? What is the difference between a junior, mid-level, or senior data scientist and what should one expect of each of these different experience levels? Once an organization has succeeded in hiring a data science team, how should they be managed? Where do they fit into traditional engineering, research, and development workflows?
In this course we will explore a series of case studies and workshops designed not only to address each of these questions but also to develop practical techniques for successfully building and managing data science teams to lead one’s organization towards more effective data-driven decision-making.
This course is part of the Leadership track of the Advanced Data Science Certificate. In particular, it’s part of a three-course leadership sequence that explores different aspects of leadership and management in a data science context: How, focusing on product management and infrastructure; Who, focusing on building effective teams, and Why, What, & Where, focusing on organizational strategy.
Upon successful completion of the course, students will:
Identify and differentiate the skill sets necessary to compose a successful data team including data science, data engineering, programming, and other skills. In a field that can seem impossibly broad and impossibly deep, identify the specific skill profile of a data scientist that is best suited for a particular organization.
Formulate effective hiring practices: role definitions, job postings and interview questions to attract and vet candidates for data teams.
Understand and navigate the varying needs of different audiences from junior team members and senior technologists to project managers, product owners, and technical leadership to clients, investors, and company executives.
Understand the tradeoffs between different “people” considerations: designing career paths for individual contributors and team leaders, evaluating performance, and forming and managing teams.
Explore the elements of creating and maintaining a culture that supports sustainable performance.
Define the characteristics of a leader and manager of data scientists, and navigate the ways in which different organizations may require different types of data leaders.
Evaluate the unique needs that data teams have for learning and development.
Enrollment in this course is open to all students and applies credit toward the Advanced Data Science Certificate Leadership tracks.
Course prerequisites include:
Experience using the Python programming language for data analytics
A laptop with Python/Anacondas and pip/conda installed and administrative access to install third-party Python libraries and make API requests
Familiarity with Jupyter notebooks, matplotlib, and pandas
Applies Towards the Following Certificates
- Certificate in Advanced Data Science : Elective Course