Becoming a Data Science Leader: Skills, Programmes and Courses

Data science leadership requires professionals to think outside the box, stay in tune with the latest trends and apply interdisciplinary methods to complex problems. This industry has evolved beyond standard data management, classifying and organising, and leaders must know how to lead teams of various backgrounds and expertise.

This guide explores how data science has evolved, the technical and soft skills required for data science leaders and the top career pathways. 

Table of Contents:

• The Evolving Role of Data Science Leaders
• Great Programmes for Aspiring Leaders in Data Science
• Great Courses for Aspiring Leaders in Data Science
• Core Technical Skills for Data Science Leaders 
• Essential Soft Skills for Data Science Leaders
• Career Pathways for Future Data Science Leaders
• Emerging Trends Shaping the Future of Data Science 
• Next Steps for Aspiring Leaders

With data collecting, cleaning and organising becoming more intensive, data science leadership roles are becoming more complex. These leaders are shifting from primarily focusing on technical expertise to bridging the gap between data science and business objectives through innovation and informed decision-making. 

Data science leaders must be in tune with the latest technologies and have an in-depth understanding of strategic thinking and communication to develop better connections between stakeholders and technical teams. Organisations across various sectors need data science leaders; these professionals must have expertise beyond standard data science concepts.

Here is an overview of what responsibilities you can expect as a leader in data science;

• Developing data strategies
• Data visualisation
• Predictive modeling
• Strategic leadership
• Data quality and ethics
• Project planning and execution
• Performance monitoring and reporting

Many programmes can help aspiring data science leaders gain the necessary skills and work on real-world problems. 

Here are a few programmes and degree pathways to equip you with the expertise you need to excel in data science leadership roles. 

The London Interdisciplinary School – MASc Degree

The London Interdisciplinary School (LIS) offers an innovative master’s degree in interdisciplinary expertise, helping aspiring data science leaders develop critical thinking and complex problem-solving skills. Students will not learn how to create solutions from one specialisation. Instead, they will build skills and knowledge from multiple disciplines to think outside the box and guide organisations through qualitative and quantitative methods.

To help students get started in data science, LIS also offers an interdisciplinary BASc degree extensively focusing on the fundamentals of this field. If you don’t have any qualifications, studying the BASc degree and then progressing to the MASc degree is best for a successful data science career. 

Curriculum:

• Cracking the Code: Learning to programme – Learn Python through data science and its applications, including quantitative research and web development. 
• Everything Counts: Probability, statistics, and numerical estimation – Gain knowledge in two different approaches, Bayesian and Frequentist.
• Complexity – Discern high-level patterns of behaviour and identity and describe similar dynamics in complex systems. 
• Integration – Broaden your understanding of integration in theory. 

Programme Length:

• Full-time, on-campus for 1 year
• Part-time, remote for 2 years

Tuition Fees:

• £14,000 for one year of full-time, on-campus study for home students
• £25,000 for one year of full-time, on-campus study for international students
• £7,000 /year for two years of part-time, online for home and international students

Imperial College Business School – Data Strategy for Leaders

This online programme by Imperial College Business School breaks down data strategies for aspiring leaders. It is built around insights from industry experts and case studies, aiming to help students understand what it takes to become successful data leaders. Students will learn through hands-on modules, live sessions, interactive activities and assignments. They will also receive dedicated support from a student Learning Team. 

Curriculum:

• Introduction to Data Strategy
• Data Orchestration
• Data Science, AI, and Machine Learning for Managers and Leaders
• Data-Driven Digital Transformation

Programme Length:

• 14 weeks 

Tuition Fees:

• £2,500

London School of Economics and Political Science – MPA in Data Science for Public Policy

The London School of Economics and Political Science (LSE) provides a full-time programme in data science for public policy. Aspiring leaders will find value in this pathway if they want to excel in the social impact and sustainability sectors. Students will learn about programming languages and cloud computing and refine their statistical analysis and mathematical skills. At the end of this programme, students will be well-prepared for leadership roles in government, NGOs and private companies.

Curriculum:

• Micro and Macro Economics for Public Policy
• Data Science for Public Policy
• Technology, Data Science and Policy
• Quantitative Approaches and Policy Analysis

Programme Length:

• 21 months 

Tuition Fees:

• £32,400

Here are courses for data science leaders, what they cover, how much they cost and the curriculum length.

The University of Aberdeen – Data Science: From Data to Insight

The University of Aberdeen offers the Data Science: From Data to Insight for 15 weeks online. Students should study for ten to 15 hours per week and gain insights into data science basics, such as data analytics, data mining, real-time data analysis, and neural networks. You’ll study through videos, lectures, projects, quizzes, and reading materials.

Curriculum:

• Data analytics
• Data mining
• Uncertainty quantification
• Data visualisation
• Real-time data analysis
• Machine learning (ML)
• Artificial intelligence (AI)

Programme Length:

• 15 weeks 

Tuition Fees:

• £1,555

London School of International Business – Postgraduate Certificate in Leading Data Science Teams

The Postgraduate Certificate in Leading Data Science Teams by the London School of International Business is ideal for professionals who want specialised skills in leading data science teams. Students will comprehensively understand how to drive organisational innovation and success through data science strategies and maintaining results-driven teams.

Curriculum:

• Introduction to Data Science Team Leadership
• Data Science Project Management
• Data Science Team Building and Communication
• Data Science Ethics and Governance
• Advanced Data Analysis Techniques
• Machine Learning for Data Science Teams

Programme Length:

• 1 to 2 months 

Tuition Fees:

• £140 to £180

MIT Professional Education – Data Leadership

The Data Leadership course is 100% online, helping aspiring data science leaders learn how to leverage new tools and success to lead successful teams. From gaining insights into data history to studying how data platforms work, AI applications and data store technologies for organisational advancement, students will have all the skills they need for leadership roles. 

Curriculum:

• An Introduction to Artificial Intelligence for Business Leaders
• IT Architecture and Querying Data
• Frameworks for Continuous Data Innovation
• The Importance of Data
• Data Platforms and Database Design

Programme Length:

• 2 months 

Tuition Fees:

• $3,200 (approx. £2472)

Data scientists must have core practical skills to lead teams and successfully achieve stakeholder objectives. These are the top core technical skills for data science leaders.

Artificial Intelligence (AI)

AI and data science are interconnected, making the former critical for data science leaders. Leaders must know how to leverage AI for data analysis. For example, AI systems help data scientists efficiently analyse big data sets. Many organisations process high volumes of data, and scientists use AI to create machine learning models to draw actionable insights from this information. 

AI is also an effective tool for making informed predictions and recommendations. Deep learning, machine learning, and neural networks enable data scientists to develop predictive models that guide stakeholders’ decision-making. AI also simplifies data management, allowing professionals to automate tedious tasks like data mining, cleaning, and organising. 

Machine Learning 

Data scientists rely on machine learning (ML), which comprises two critical skills: computer science and advanced mathematics. Skilled data scientists use machine learning to improve analysis efficiency, accurately predict outcomes, and identify anomalies in big data analytics. Machine learning streamlines data processing and helps professionals interpret data. 

Data science leaders need machine learning techniques to find patterns in historical data, inform current decision-making, and identify suspicious activities or issues in data quality. They also rely on machine learning to facilitate classification - this technology sorts data into groups, making it easier to understand and manage. 

Big Data

Big data refers to extensive and varied structured and unstructured data collections. These data sets are so complex that standard data management solutions can’t analyse, store or process them. Organisations need data science techniques to analyse, classify, and draw actionable insights from this data. 

These data sets will grow continuously, making it imperative for data science leaders to have the technical knowledge to manage big data and guide teams to maintain an agile infrastructure that can process these extensive data sets. 

Every data science leader must have soft skills besides technical skills. Here is an overview of these soft skills and why they are imperative. 

Communication

Communication is critical for leaders across every industry, and data science is no exception. Data science leaders must know how to speak to their teams and listen to them. These professionals and their team members often work in highly stressful environments, with various data science projects co-occurring. Stakeholders expect ongoing updates and reports on how data science leaders intend to help them reach their goals.

Communication and interpersonal skills become imperative for data scientists to keep their teams aligned and understand what stakeholders want to achieve and how they can fulfil this. However, communication doesn’t stop at knowing how to speak and listen; leaders in data science must know how to connect with different team members and stakeholders and maintain clear lines of communication. 

Problem-Solving

There is no one-size-fits-all approach to data science – leaders must think critically and solve problems on the fly. While there are tools to guide data science techniques, leaders must know and understand the right questions to ask and identify opportunities or potential setbacks within data to guide organisations in making sound decisions. 

Problem-solving becomes more complex at the leadership level, with data science leaders guiding team members to think critically and apply innovative solutions they may not have worked with before. Leaders must think outside the box and leverage creative and logical thinking to ensure project completion. 

Cross-Functional Leadership

Cross-functional leadership skills distinguish data science leaders from the rest. Understanding the dynamics of leading a data science team is essential, but it becomes more valuable and sought-after when professionals know how to align and lead multiple teams. Cross-functional leadership breaks down the silos of traditional leadership and enables leaders to understand how to connect with professionals from various backgrounds.

Often, leaders find themselves in a position that demands cross-functional skills. Cross-functional leadership is imperative if new members have joined your teams from entirely different backgrounds or two departments have merged, and you’re leading them. Regardless of the scenario, having cross-functional expertise prepares you to manage all kinds of teams, propelling your leadership skills and making you an even more desirable hire for employers. 

Data science leaders have various career pathways, depending on your career goals and experience. You can succeed in these few pathways as a data science leader. 

Senior Data Scientist

It’s typical for aspiring data science leaders to launch careers as senior data scientists. Professionals in these roles earn around £68,000 annually; however, your earning potential fluctuates based on your experience and company. Senior data scientists lead the data science and analytics team, ensuring they meet stakeholder objectives and have the necessary resources to draw insights from data.

As a senior data scientist, your job role includes:

• Develop predictive data models.
• Manage data science projects.
• Mentor junior data scientists and influence onboarding training.
• Stay ahead of industry trends to identify the best tools and frameworks for efficient data management.

Data Science Director

While data science directors and senior data scientists seem similar, they aren’t. The former assumes more leadership responsibility, and directors in this industry oversee the entire data science department. This job role is better suited for professionals with leadership experience, while working as a senior data scientist is a great starting point if you haven’t had much experience leading various teams.

The average annual salary for data scientist directors is £104,000. Typical day-to-day responsibilities of data science directors include:

• Set strategic direction.
• Develop data strategies.
• Lead data science departments.
• Collaborate with cross-functional teams.
• Translate complex data analysis into business recommendations.

Senior Sustainability Analyst

A senior sustainability analyst evaluates and reports on a company’s environmental impact. These professionals lead teams and develop strategies to help businesses become more environmentally responsible and comply with regional regulations. This career path works for professionals with experience as data analysts who want to broaden their leadership skills.

The average salary for senior sustainability analysts is around £51,000 per annum. These are the responsibilities you can expect in this role:

• Support ethical sourcing and fair trade.
• Collect, organise and visualise data to measure environmental impact.
• Work with external vendors to source sustainable resources.
• Prepare for regulatory compliance.
• Develop internal standards for ESG data reporting.

Data Science Consultant

Working as a data science consultant is ideal for professionals who want a leadership role while working with various clients. These consultants work with small businesses, government agencies and institutions to help them improve their data management and analytical processes. This entire role is built around masterful leadership skills as professionals must develop, manage and implement their strategies while overseeing how they impact data science departments.

As a data science consultant, professionals earn around £49,412 per year. You can expect tasks like:

• Collecting and cleaning data.
• Data exploration and analysis.
• Research and implement data science tools.
• Design data pipelines.
• Present data insights and recommendations to stakeholders.

Every leader in data science must understand emerging trends to help organisations leverage tools and processes that automate and streamline data management. 

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Growing Integration of AI and Machine Learning

One key emerging data science trend is AI and machine learning integration. These technologies become more sophisticated, allowing data scientists to improve data efficiency and establish new methods to streamline data processing. 

Data science leaders must know how to leverage AI and machine learning to maximise the benefits of these solutions for businesses. For example, leaders must identify areas that require optimisation and deploy AI and machine learning to help team members automate repetitive tasks and maintain data quality. 

Cloud Computing Expansion

High data volumes demand increased storage and computing solutions. Cloud computing is an effective way to get around this, as it offers vast storage capacity and computational horsepower to manage large data sets. The overhead costs and complexities of on-premise infrastructure often challenge organisations. Fortunately, cloud computing platforms like Azure and AWS solve these drawbacks. 

Leaders in data science must understand how to implement cloud computing software and maintain cloud infrastructures. 

Generative AI

Generative AI uses neural networks to create new, realistic data, including audio, text, image, and text. This technology is growing rapidly and becoming a reliable solution to automate creative tasks across various industries. For instance, in marketing, companies can use generative AI to create tailored ad campaigns and promotional emails. 

Likewise, data science leaders can use generative AI in cybersecurity. They can expose data models to security threat simulations to train them to identify and solve potential attacks before they evolve. 

Data science is rapidly evolving, with new technologies and processes becoming imperative for organisations to extract valuable insights. As more organisations rely on data science, leaders in this industry must have the best soft and technical skills to lead teams to success. They should have expertise in AI, machine learning, managing big data, communication, problem-solving and cross-functional leadership.

Many programmes and courses are available to help you gain the skills you need to excel in data science leadership roles. At LIS, we provide a master’s degree programme equipping you with the skills and knowledge to help organisations solve complex problems through innovative solutions. We also offer a comprehensive course on what it takes to become a cross-functional leader.

Want to become a leader in data science? Contact LIS today.