Status: Featured
Data Science Career Roadmap: Jobs and Levels Guide
Explore the job-leveling matrix for Data Analysis careers. Understand the roles and skills needed to advance from beginner to leader.
May 17, 2024
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Data Science Resources
Explore our guides, templates, and tools for Data Scientists. Whether you’re an aspiring data scientist starting out or a senior data leader looking for a refresher, Coursera’s Data Scientist Resources will support you at every stage of your career.
Popular Data Science Courses
Status: AI skills
Skills you'll gain: Dashboard, Data Visualization Software, Data Wrangling, Data Visualization, SQL, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Jupyter, Exploratory Data Analysis, Data Mining, Data Cleansing, Matplotlib, Data Analysis, Unsupervised Learning, Generative AI, Pandas (Python Package), Data Manipulation, Professional Networking
Skills you'll gain: Jupyter, Automation, Web Scraping, Python Programming, Data Manipulation, Data Import/Export, Scripting, Data Structures, Data Collection, Application Programming Interface (API), Pandas (Python Package), NumPy, Object Oriented Programming (OOP), Computer Programming
Microsoft
Skills you'll gain: Data Storytelling, Data Analysis Expressions (DAX), Data Presentation, Power BI, Data Ethics, Dashboard, Data Visualization Software, Data Modeling, Data Governance, Extract, Transform, Load, Data Visualization, Interactive Data Visualization, Business Intelligence, Data Analysis, Statistical Analysis, Data Architecture, Correlation Analysis, Data Cleansing, Data Transformation, Database Design
What are the top skills needed to be a data scientist?
Analytical skills: Analytics skills are crucial for data scientists as they enable them to transform raw data into actionable insights, driving informed decision-making to help solve complex business problems. Top skills include:
Programming Languages: Proficiency in languages like Python and R is vital because they provide the tools for data scientists to manipulate, analyze, and visualize large datasets efficiently. Top languages include:
Math & statistics: Mathematical and statistical knowledge is essential for designing experiments, assessing correlations, and optimizing algorithms to ensure precise and reliable outcomes in any data-driven project. Key skills to learn:
