Our program draws on the University’s leading scholars in computer science, statistics, and a range of other disciplines. We offer curricula spanning humanities and social and natural sciences, mirroring the cross-cutting nature of data science.
Advising Resources
Data Science at Berkeley
We offer curricula spanning humanities and social and natural sciences, mirroring the cross-cutting nature of data science. Students are equipped to tackle real-world problems and grapple with societal, human, and political ramifications of data science.
How to get started on the Job Search
- Update your resume and LinkedIn
- Looking at the unknown from new perspectives
- Reach out to alumni in that field through LinkedIn for advice on how to make your application stand out
Resume tips
- Tailor your resume for each role you apply to
- For business roles, include leadership and extracurricular activities
- For technical roles, include a projects section and feel free to use class projects as examples
- Including coding languages and the specific packages you are skilled in
- List relevant courses that you’ve taken
- List skills, awards, and unique interests at the bottom
Resources on Campus
- Cal Career Center
- Cal Alumni Career Network
- Berkeley Handshake
- On campus career fairs/networking sessions
- Club hosted recruting events
- List skills, awards, and unique interests at the bottom
Alumni Stats
Over 4/5 of Berkeley’s graduated data science majors are employed immediately after college. They’ve gone into various fields such as technology, finance, consulting, startups, and graduate school. Most people felt that courses at Berkeley well prepared them for their careers.
Career Paths in Data Science
Data Analyst
- Curates insights from existing data
- Looking at the unknown from new perspectives
- Programming languages like Python, R, SQL, HTML, JavaScript
- Spreadsheet Tools (Excel)
- Data visualization like Tableau
- Identifying data quality issues and partialities in data acquisition
- Creating reports to help a business executive make better decisions
- Typical degree: Undergraduate B.S or B.A
Data Scientist
- Can predict the future based on patterns, estimate the unknown
- Generates their own questions and then uses skillset to find answer
- More advanced skillset of data visualization, advanced statistical techniques, and programming
- Knows how to get the data that they need to perform the analysis they want
- Programming languages like Python, R, SAS, Matlab, SQL, Hive, Scala
- Distributed computing frameworks like Hadoop
- Big data packages like Spark, AWS
- Machine learning skills
- Typical degree: Masters or PhD, sometimes Undergraduate degree depending on the company
Data Engineer
- Builds scalable, high-performance data infrastructure for delivering clear business insights from raw data sources that the data scientists often interact with
- Tools: SQL, MySQL, NoSQL, Cassandra, and other data organization services
- Advanced database and programming knowledge
- Typical degree: Undergraduate or Masters with a degree in Computer science or engineering
Machine Learning Engineer
- Develop algorithms that can receive input data and leverage statistical models to predict an output while updating outputs as new data becomes available
- Sits at the intersection of software engineering and data science
- Ensures that the raw data gathered from data pipelines are redefined as data science models that are ready to scale as needed
- They feed data into models defined by data scientists
- Can take theoretical data science models and scale them out to production level models that can handle terabytes of real time data
- Skills: Python, Java, R, C++, C, JavaScript, Scala, Julia
- General education requirements: Master’s or PhD in computer science, math, or statistics
- Experience with vision processing, deep neural networks, Gaussian processes, and reinforcement learning
- Experience with distributed systems and messaging tools
Data Science Interview Prep Resources
- 7 Steps to Ace a Data Scientist Job Interview: Analytics/Inference Track (Medium)
- Data Science & Machine Learning Interview Guide (Analytics Vidhya)
- Free Data Science Interview Prep Course (Udacity)
- 109 Data Science Interview Questions and Answers (Springboard)
- Data Science Interview Study Guide (Medium) - includes probability, machine learning and coding review
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data (Becoming Human)
More UC Berkeley Career Resources for Data Science Majors
Not sure where to start? Check out this presentation from the Career Center on Preparing for a Career in Data Science
For further career advising including resume review, career prep and job search strategies, contact the UC Berkeley Career Center
Information on this page provided by former Data Science Peer Advisor Alekya Mallina and by UC Berkeley Career Center Counselor Revae Hitt.
