Careers in Data Science

Career Paths in Data Science

Check out this external guide to 14 different data science job types, or see below for more details about some of the most common jobs for data science majors.

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

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.

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