Course Description: In the rapidly evolving field of data science, technical expertise alone is not sufficient for success. Effective leadership is essential to navigate the complexities of data-driven decision-making and drive impactful outcomes. The course is designed as a practical stage-by-stage field guide for our students to their careers in data science. It provides valuable insights and strategies for individuals at different career stages, from aspiring data science tech leads to seasoned data science executives. Through a comprehensive examination of several case studies, students will develop a deep understanding of the leadership skills, capabilities, and virtues necessary for success in the field of data science.
Prerequisite: Enrollment in the Data Science Program.
Course Goals
- Students will gain a comprehensive understanding of what it means to be a leader in the field of data science. They will explore the unique challenges and responsibilities faced by data science practitioners and learn how leadership skills can amplify their impact.
- Effective leadership in data science requires the ability to communicate complex technical concepts to diverse stakeholders and influence decision-making processes. Students will learn strategies to effectively communicate their ideas, build strong relationships, and influence others to drive positive outcomes.
- Leading data science projects often involves managing cross-functional teams and fostering collaboration among individuals with diverse backgrounds and expertise. Students will learn techniques for building and nurturing high-performing teams, promoting collaboration, and leveraging the strengths of team members to achieve project success.
- Students will explore strategies for advancing their careers in data science and making a significant impact within their organizations and the broader industry. They will learn how to navigate career progression, seize opportunities for growth, and inspire positive change in their organizations and the data science field as a whole.
Course Learning Objectives
Upon completion, students will:
- Develop an understanding of leadership in the context of data science.
- Cultivate effective communication and influence skills.
- Foster team development and collaboration.
- Develop strategies for career advancement and industry impact.
Textbook
- Jike Chong and Yue Cathy Chang, How to Lead in Data Science, Manning Publication, 2021. ISBN: 1617298891, 978-1617298899.
Reference Books
- Emily Robinson and Jacqueline Nolis, Build a Career in Data Science, 1st ed., Manning Publications, ISBN: 1617296244, 978-1617296246
- Jeremy Adamson, Minding the Machines: Building and Leading Data Science and Analytics Teams. United States, Wiley, 2021. ISBN:9781119785330, 1119785332
- Peter C. Bruce and Grant Fleming, Responsible Data Science: Transparency and Fairness in Algorithms, 1st Ed., Wiley, 2021, ISBN: 1119741750, 978-1119741756
- Robert de Graaf, Managing Your Data Science Projects: Learn Salesmanship, Presentation, and Maintenance of Completed Models, Apress, 2019, ISBN: 1484249070, 9781484249079
- Bernard Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Wiley, 2016, ISBN: 978-1-119-23138-7
To see the list of reference papers and good reads, please follow this link.
Tentative Schedule
| Lec. No | Ch. No | Subjects | Lecture Notes | Slides |
| 1 | 1 | Introduction to DATA 607
What Makes a Successful Data Scientist? Data Scientist Expectations: Past vs Now, Capabilities and Virtues Career Progression in Data Science Case Studies |
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| 2 | 2 | Capabilities for Leading Projects
Framing the problem to maximize business impact Discovering patterns in data Setting expectations for success Specifying and prioritizing projects Planning and managing data science projects |
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| 3 | 2 | Project management: Waterfall or Scrum?
Dealing with Trade-offs Data Science Projects Clarifying business context of opportunities Accounting for domain data source nuances Case Studies |
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| 4 | 3 | Virtues for Leading Projects
Ethical Standards of Conduct Getting clarity on the fundamentals of scientific rigor Monitoring for anomalies in data and in deployment |
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| 5 | 3 | Taking responsibility for an enterprise value
Positivity and tenacity to work through failures Curiosity and collaboration in responding to incidents Respecting diverse perspectives in lateral collaborations |
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| 6 | 4 | Capabilities for Leading People
Delegating projects effectively Managing for consistency across models and projects Making build-versus-buy recommendations Building powerful teams under your supervision |
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| 7 | 4 | Influencing partner teams to increase the impact
Managing up to your manager Broadening knowledge in multiple technical and business domains Recognizing the fundamental domain opportunities Assessing the ROI of projects despite missing data |
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| 8 | 5 | Virtues for leading people
Growing the team with coaching, mentoring, and advising Representing the team confidently and contributing to broader management duties |
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| 9 | 5 | Observing and mitigating system anti-patterns and learning from incidents
Driving clarity by distilling complex issues into concise narratives Managing the maker’s schedule versus the manager’s schedule |
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| 10 | 6 | Capabilities for Leading a Function
Crafting technology roadmaps to deliver the right features at the right time Sponsoring and championing promising projects |
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| 11 | 6 | Delivering consistently by managing people, processes, and platforms
Building a strong function with clear career paths and a robust hiring process |
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| 12 | 6 | Anticipating business needs and driving fundamental impacts | ||
| 13 | 7 | Virtues for Leading a Function
Establishing project formalizations across your function Coaching as a social leader and organizing initiatives for team career growth |
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| 14 | 7 | Driving successful annual planning processes, while avoiding planning anti-patterns
Securing commitments from partners and teams |
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| 15 | 7 | Recognizing diversity, practicing inclusion, and nurturing belonging within your function |
Grading
At the beginning of each lecture (except the first lecture), there will be a quiz. This quiz will assess students’ understanding of the subjects that are covered in the previous week. This quiz will also be utilized as a tool to collect attendance data. There will be 14 quizzes. These quizzes can be taken only during the lecture time. If you are absent or late, there won’t be a make-up quiz.
| Item | Explanation | Contribution |
| Weekly Quizzes | 13 * 6 | 78 |
| Midterm Paper | 10 | 12 |
| Term Paper | 20 | 20 |
| TOTAL | 110 |
Grading Distribution: 94-100: A, 88-93: A-, 83-87: B+, 77-82: B, 71-76: B, 66-70: C+, 60-65: C 50-59: D, 0-49: F.
Policies
Please visit this page to learn about course and institutional policies.