DATA 607 Leadership in Data Science

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

  1. 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.
  2. 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.
  3. 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.
  4. 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:

  1. Develop an understanding of leadership in the context of data science.
  2. Cultivate effective communication and influence skills.
  3. Foster team development and collaboration.
  4. Develop strategies for career advancement and industry impact.


  • 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

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

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
Navigating organizational structure

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

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

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

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

8 5 Virtues for leading people

Growing the team with coaching, mentoring, and advising

Representing the team confidently and contributing to broader management duties

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

10 6 Capabilities for Leading a Function

Crafting technology roadmaps to deliver the right features at the right time

Sponsoring and championing promising projects

11 6 Delivering consistently by managing people, processes, and platforms

Building a strong function with clear career paths and a robust hiring process

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

14 7 Driving successful annual planning processes, while avoiding planning anti-patterns

Securing commitments from partners and teams

15 7 Recognizing diversity, practicing inclusion, and nurturing belonging within your function



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

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.



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