还有哪些技能可以促进我在 Data Science 的职业发展？

除了掌握 Python 和 R 等编程语言的专业知识外，学习用于 Big Data 处理的 Apache Spark、AWS 和 Azure 等云计算平台以及 SQL 和 NoSQL 等数据库管理系统等先进工具和技术，还能提高您在各行各业的就业能力。有效沟通、项目管理和特定行业领域专业知识等职场技能也能让你成为一名全面的数据科学家。

Data Science Career Progression: Job Levels & Skills to Advance

Q: 数据分析师和数据科学家的主要区别是什么？

数据分析师 主要分析数据以产生可操作的见解，使用 SQL 和 Excel 等工具，并创建报告和 Visualization。相比之下， 数据科学家 则不局限于分析，而是要建立预测模型、执行复杂的统计分析和应用机器学习技术。要实现从数据分析师到数据科学家的职业转型，首先要重点培养 Python 或 R 的编程技能，了解机器学习框架，积累数据 Wrangling 和模型开发方面的经验。报读高级课程并参与预测建模项目，有助于缩小这些角色之间的差距。 阅读更多内容： 数据分析师与数据科学家：有什么区别？

作者：Coursera • 更新于 Aug 8, 2025

Explore the job-leveling matrix for Data Analysis careers. Understand the roles and skills needed to advance from beginner to leader.

Data Science Career Roadmap and Job Leveling Matrix

What is a Job Leveling Matrix for Data Science?

How to Use This Job Leveling Matrix for Data Science

Example of Data Science Job Leveling Matrix

Typical Data Science Career Progress and Roles

Educational Requirements

Recommended Data Science Courses

FAQs about Data Science Career Pathways

Data science is integral to modern analytics, driving innovation and strategic decision-making across industries. This guide offers a clear pathway for career advancement in data science, detailing the crucial roles and competencies needed to evolve from a novice data scientist to a senior leader.

What is a Job Leveling Matrix for Data Science?

Pursuing a career in data science can appear complex due to the breadth of skills involved. Newcomers and experienced professionals often face questions about progression, including promotion criteria, understanding organizational hierarchy, acquiring essential skills, exploring career opportunities, and fulfilling role responsibilities.

A job leveling matrix designed for data science helps clarify these challenges by outlining career paths and ensuring every data scientist understands their current position, potential trajectory, and steps needed for advancement.

How to Use This Job Leveling Matrix for Data Science

This data science career matrix guides you through a structured progression to enhance your career in data science:

Gauge your skill level: Assess your current proficiency and role in data science.

Plan for upward mobility: Identify the skills you need to develop or enhance to advance to the next level of your career.

Transition to leadership: Prepare for the responsibilities and competencies required for senior management and leadership roles in data science.

Data Science Job Leveling Matrix: Roles and Levels

Career Path	Level 1: Beginner	Level 2: Intermediate	Level 3: Advanced	Level 4: Expert	Level 5: Leader
Data Analyst	• Basic SQL - Excel • Simple Visualizations	• Advanced SQL • R/Python • Intermediate Machine Learning	• Complex data models • Predictive analytics	• Data-driven business strategy	• Leadership in analytics initiatives
Data Engineer	• Basic database knowledge • ETL tools	• Advanced database management • Data warehousing	• Cloud services • Big data technologies	• Architecting scalable data solutions	• Strategy for data infrastructure
Machine Learning Engineer	• Basic ML algorithms • Python/R	• Advanced ML models • Feature engineering	• Deep learning • Model optimization	• ML deployment • Automated ML systems	• ML strategy and innovation
Data Scientist	• Statistical analysis • R/Python	• Machine learning • Data wrangling	• Deep learning • AI technologies	• Advanced predictive modeling	• Leading data science research and development
AI Specialist	• Basic AI concepts • Programming	• Neural networks • TensorFlow/Keras	• Reinforcement learning • AI optimization	• AI system integration	• AI strategy and ethical AI leadership
BI Developer	• Report creation • Basic data analysis	• Complex reporting • Dashboards	• Data mining • BI software expertise	• Advanced analytics and visualization	• BI strategy and management

Data science is an ever-evolving field. Stay ahead by continuously refining your skills, seeking new knowledge, and embracing the endless possibilities of data science careers. Bookmark this guide and revisit it as you climb each career ladder rung.

Typical Data Science Career Progress and Roles

Understanding the career progression within data science can help professionals delineate a clear trajectory from entry-level to leadership positions. This section details example career advancement pathways along with definitions and descriptions of the essential data science roles and their responsibilities.

Data Analyst

Career Progression: Data Analyst Intern → Data Analyst → Senior Data Analyst → Data Analytics Manager → Director of Data Analytics

A Data Analyst utilizes SQL, Excel, and simple visualization tools to extract and interpret data. As they progress, they employ advanced SQL techniques, programming languages like R or Python, and intermediate machine-learning methods. Senior roles encompass developing complex data models, predictive analytics, and driving data-driven business strategies.

Data Engineer

Career Progression: Data Engineering Intern → Junior Data Engineer → Data Engineer → Senior Data Engineer → Data Engineering Manager → Chief Data Architect

A Data Engineer focuses on building and optimizing data systems and pipelines. Responsibilities start with basic database knowledge and ETL tools, advancing to sophisticated database management, data warehousing, and utilizing cloud services. Senior roles involve architecting scalable data solutions and strategizing data infrastructure.

Machine Learning Engineer

Career Progression: ML Assistant → Junior ML Engineer → Machine Learning Engineer → Senior ML Engineer → ML Engineering Manager → Head of Machine Learning

A Machine Learning Engineer designs and deploys machine learning models. They start with basic algorithms and programming in Python or R, advancing to more complex models, feature engineering, and deep learning techniques. Senior roles include model optimization, automated ML system deployment, and leading ML strategy and innovation.

Data Scientist

Career Progression: Data Science Intern → Data Scientist → Senior Data Scientist → Lead Data Scientist → Chief Data Scientist

A Data Scientist performs statistical analysis and employs machine learning to extract insights from data. Responsibilities evolve from conducting basic statistical analyses and data wrangling to applying advanced machine learning algorithms and deep learning methods. Senior roles involve advanced predictive modeling and leading research and development initiatives.

AI Specialist

Career Progression: AI Research Intern → Junior AI Specialist → AI Specialist → Senior AI Specialist → Lead AI Specialist → Director of AI Innovation

An AI Specialist focuses on developing and optimizing artificial intelligence systems. This involves understanding basic AI concepts, programming, and neural networks, and utilizing frameworks like TensorFlow or Keras. Advanced roles include reinforcement learning, AI system integration, leading AI strategy, and ethical AI initiatives.

BI Developer

Career Progression: BI Intern → Junior BI Developer → BI Developer → Senior BI Developer → Principal BI Developer → Director of Business Intelligence

A BI Developer (Business Intelligence Developer) creates reports and dashboards to support business decisions. Starting with basic data analysis, they develop complex reports and dashboards and become proficient in BI software. Senior roles involve advanced analytics, data mining, and managing BI strategy and operations.

Educational Requirements for a Career in Data Science

Pursuing a career in data science usually requires a robust educational background in mathematics, computer science, statistics, or related fields. Most professionals begin with a bachelor's degree. However, advanced roles may benefit from a master’s degree or specialized certifications (such as IBM Data Science Professional Certificate or Google Professional Data Engineer). Key competencies include programming languages, machine learning, statistical analysis, and data visualization. Practical experience through internships, projects, and contributions to open-source projects is crucial.

Advance your career in data science with Coursera

To build the necessary skills and knowledge for a career in data science, consider enrolling in the IBM Data Science Professional Certificate. In this program, you'll learn the tools, languages, and libraries used by professional data scientists, including Python and SQL. You'll also learn how to apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers.

FAQ for Data Science Career Pathways

要过渡到机器学习工程师或 AI 专家等专业职位，应重点加深对机器学习算法、神经网络和 AI 框架（如 TensorFlow 和 Keras）的了解。您可以通过学习高级课程、获得相关认证和建立专业项目组合来展示自己的专业知识。通过实习获得实践经验、参与研究项目、了解 AI 和机器学习领域的最新进展也都是有益的。‎

数据分析师 主要分析数据以产生可操作的见解，使用 SQL 和 Excel 等工具，并创建报告和 Visualization。相比之下，数据科学家则不局限于分析，而是要建立预测模型、执行复杂的统计分析和应用机器学习技术。要实现从数据分析师到数据科学家的职业转型，首先要重点培养 Python 或 R 的编程技能，了解机器学习框架，积累数据 Wrangling 和模型开发方面的经验。报读高级课程并参与预测建模项目，有助于缩小这些角色之间的差距。

阅读更多内容： 数据分析师与数据科学家：有什么区别？‎