According to IBM, 90% of all the data in the world today was created in the last few years, and just about every last byte of it lands somewhere in the supply line that runs through Silicon Valley. The torrent of unstructured and uncategorized data that pours into the pipeline from billions of people doing billions of things every day creates a playground for the data scientists capable of transforming it into something companies can use to gain an edge in the marketplace.
From e-commerce and healthcare informatics to retail analytics and finance, data scientists are a hot commodity in firms dealing with the problem of how to collate and derive meaning from the massive troves of data that come spiraling out of their business operations every hour of every day. Data scientists help realize the full potential of big data by bringing structure to it, finding compelling patterns, and advising executives on the possibilities and implications found within it.
According to the executive recruiting firm Burtch Works, data scientists on the West Coast earn the highest salaries at every level, handily beating out the closest competition, those in the Northeast, by at least $7,000 annually.
Data science, of course, has a permanent home in Silicon Valley where the biggest and most globally recognized tech enterprises on earth wrangled big data a decade before the term was coined, inventing an entirely new industry from the slag of their daily business functions. And when that by-product was discovered to be as valuable as the very products and services these companies offered, data science was born.
There is no longer a clean line between the software we interact with daily and the data these interactions generate. One informs the design of the other in ways that are hard to comprehend and impossible to separate.
Professional opportunities in big data are not emerging; they are here now, and California’s global tech companies are competing for top talent – scientists with the creative mindset and technical capability to take the company vision to the next level.
Preparing for a Master’s Degree in Data Science in California
Schools offering data science master’s degree programs expect candidates to have a strong background in the high tech arts, including an impressive showing in undergraduate studies. Data science is a demanding field, and with many applicants vying for limited positions, you’ll find that you need a lot more than just a bachelor’s degree under your belt to get your foot in the door.
Undergraduate Degree and Master’s Prerequisite Courses
It starts with your choice of major. Many schools that offer graduate programs in data science require candidates to hold an undergraduate degree in one of the following:
- Computer Science
- Applied Statistics
- Math
- Engineering
- Physics
- Biological/natural sciences
Most schools require candidates to have a minimum 3.0 GPA in their undergraduate work, too, so this isn’t a field where anyone can just slide in without a lot of hard work and self-discipline.
Other requirements for admission into a master’s degree in data science often include:
- Minimum GRE scores
- Letters of recommendation
- Undergraduate coursework in:
- Computer programming
- Statistics
- Calculus I & II
- Linear algebra
It is common for data science programs to require candidates to sit for an admissions interview, during which time they assess their motivations and foundational knowledge.
Prepare for a Master’s Program or Skip it Completely by Attending a Data Science Bootcamp in Los Angeles, San Francisco, San Diego, Online, or in Many Other Cities Statewide
If all that sounds a little stuffy to you, there’s a high-octane alternative waiting in the wings that can get you prepped for your master’s application in record time… or maybe even bypass a degree entirely on your path to a high-paying data science job.
That alternative is attending a data science bootcamp. Entry-level bootcamps that accept candidates without a degree deliver fast-paced, intensive, practice-oriented training in data science that gives you the bare essentials with a focus on real-world projects that get you hands-on with the tools and techniques used in the trade.
Once rare, bootcamps are now becoming so common that even universities are running them. Although they do not result in any college credits, programs like the highly respected University of California Data Analytics Boot Camp do offer fast-paced training in subjects including:
- Python 3
- Pandas
- Statistical Modeling and Forecasting
- HTML and CSS
- Dashboarding
- Big Data Analytics with Hadoop
All of it happens under the close supervision of instructors with real-world experience, and typically using real-world datasets to solve realistic business problems with the same methods and tools you would use on the job. It all happens in just 24 weeks of night and evening classes.
Here you will learn the fundamental skills needed to analyze Big Data and turn it into clear insights. Learn in-demand technologies like Python, JavaScript, Programming in Excel, SQL Databases, and more. The program consists of 6-months of nights and weekends, available on-site and online.
USC is an IT powerhouse, and offers a comprehensive entry-level camp that covers the spectrum of analytics and visualization tasks and tools.You graduate from each of these programs with an extensive portfolio of projects that you have actually conceived and completed yourself. It’s all part of the bootcamp’s career-building approach that also includes mock interview preparation and resume reviews.
Bridge Programs and Massive Open Online Course (MOOC) Options to Bridge Gaps in Functional Knowledge
Many institutions allow students who lack functional knowledge in mathematics or programming to satisfy prerequisite coursework requirements and gain mastery of key concepts by completing bridge courses upon acceptance into the program. This option is reserved for those that have met and exceeded all other standard entry requirements
Typical bridge courses available as needed upon admission into a master’s degree in data science include:
- Fundamental bridge programs – courses in linear algebra, data structures, along with analysis of algorithms
- Programming bridge programs – training in such essential programming languages as C++, JAVA, and Python
Some students also choose to take a more proactive approach and complete massive open online courses (MOOC) to satisfy prerequisite requirements before applying to a master’s degree program in data science. MOOCs, offered entirely online, provide prospective students with a wide array of course options for supplementing their education. Online problem modules, lectures, and frequent interactions with professors are just a few of the hallmarks of MOOCs.
Relevant Personal and Work Experience
In addition to a strong undergraduate background in relevant areas of study, data science graduate programs also require candidates to have experience in data analysis, business analytics, business intelligence, programming, software engineering, etc. Many institutions also look for candidates with specific knowledge of:
- Machine learning
- Computational statistics
- Large-scale scientific computing
- Operations research
Experience—both personal and work-related—should reflect a candidate’s:
- Curiosity and creativity
- Motivation
- Ability to solve problems
- Ability to learn quickly
- Analytical mindset
As expected, California is home to a vast array of companies where you can gain the experience needed to enter a master’s degree in data science. The hunger for junior data analysts in Silicon Valley and elsewhere is ravenous, and staffing firms like Kforce, Volt, and Staffigo are constantly hiring for work at both the strategic level and the tactical level for undisclosed, but big-name, clients. LiveRamp Labs in Redwood City hires entry-level data scientists that help conceive and build prototypes of intelligent applications based on existing LiveRamp technologies, LiveRamp data, and other leading-edge technology from third-party partners and customers.
Preparing for Success on the GRE/GMAT
Entry into many data science master’s degree programs requires students to take either the GMAT or the GRE exam and score in the 85th percentile on the quantitative section. Preparation is key to success on these examinations:
The GRE Revised General Test consist of three question types, all of which reflect graduate-level thinking: verbal reasoning; quantitative reasoning, and analytical writing. The quantitative reasoning questions of the GRE measures a test taker’s problem-solving ability using the basic concepts of arithmetic, algebra, data analysis, and geometry.
The GRE website provides test takers preparing to take the quantitative portion of the exam with a guide found here. Anyone studying for the exam may also want to download the POWERPREP II software, which includes practice tests.
The GMAT exam consists of four sections: analytical writing assessment; integrated reasoning; quantitative; and verbal. The quantitative section of the GMAT consists of 37 questions involving data sufficiency and problem solving. The GMAT website prepares tests takers for the quantitative section of the exam by providing a number of study tools, including videos, sample questions, and guidebooks, found here.
Earning a Master’s Degree in Data Science in California
California is home to a wide array of master’s degree programs for students interested in beginning or advancing a career in data science. However, many of today’s most competitive programs are available partially or fully online.
Some online programs do, however, require the completion of an on-campus immersion experience as part of the program.
Master’s degrees in data science may go by a number of titles, such as:
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science
- Master of Science in Statistics: Data Science
- Master of Computational Data Science
Curriculum and Core Coursework
Master’s degrees in data science consist of between 18 and 24 months of full-time study (part-time programs take about 32 months). A number of institutions offer accelerated programs, which take about 12 months to complete. Most programs include about 30 credits of core coursework in these areas:
- Experimental Statistics
- Exploring and Analyzing Data
- Applied Machine Learning
- File Organization and Data Management
- Data and Network Security
- Data Mining
- Statistical Sampling
- Research Design and Application for Data and Analyses
Program Competencies and Objectives
Master’s degrees in data science prepare tomorrow’s data science leaders to succeed in an ever-changing field. The multidisciplinary course of study of these programs prepares students to derive insight from real-world data sets and interpret their findings in innovative ways to solve real-world problems. Students of master’s degree programs in data science learn the aspects of experimental design, learn how to collect and analyze data, and learn how to make informed decisions from that analysis.
Some of the key competencies derived from a master’s degree in data science include:
- Statistical analysis
- Technical skills
- Visualization and communication
- Applied data science
- A global mindset
Graduates of a master’s degree in data science are able to imagine new and valuable uses for large datasets; apply creative methods for asking questions and interpreting results; identify patterns and make predictions; and understand the legal and ethical requirements related to data privacy and security.
Career Opportunities for Data Scientists in California with Advanced Degrees
Data scientists in California with advanced degrees are equipped to deftly handle every stage of the analytics lifecycle, including data acquisition, data cleaning/transformation, and programming/automation, just to name a few.
The following job listings are shown as illustrative examples and do not represent job offers or provide any assurance of employment. These examples do, however, provide a considerable amount of insight into the types of opportunities available in California:
Data Scientist at Pinterest: San Francisco, CA
Responsibilities:
- Influence the direction of the product through data-driven feature ideas
- Analyze everything from latency and availability to new user experiences to new marketing channels
- Perform strategic analysis on key components of the Pinterest product
- Drive the collection of new data and the understanding of existing data sources
Qualifications:
- At least four years of experience in data analysis focused on product changes
- Ability to manipulate large data sets
- Familiarity with or willingness to learn large-scale distributed computing tools
- Fluency in SQL and at least one scripting language, such as Python
Data Scientist at CoreLogic; Irvine, CA
Responsibilities:
- Create data derivation and linkage through algorithm and/or data rules
- Create enterprise-level entity matching engine through algorithm and/or data rules
- Create and select predictive features from raw data
- Perform pattern recognition model creation using various types of algorithms and machine learning model techniques
Qualifications:
- Master’s degree or higher in machine learning, a hard science, math, statistics, or an engineering field
- Ability to thrive in a team environment and adapt to quickly changing priorities
- At least four to eight years of directly related experience
- Strong problem-solving and analytical ability
Data Scientist at Siemens: Mountain View, CA
Responsibilities:
- Define and drive requirements for adaptors to enterprise data source, a data warehouse to receive and organize data
- Collaborate with data engineers to ensure availability of data
- Create and implement algorithms to process healthcare data for descriptive and predictive analytics
- Discover explanatory variables in collections of data that relate to clinically, financially, or operationally important use-cases
Qualifications:
- Master’s degree in a relevant field – clinical experience a plus
- Expert knowledge of data mining algorithms, including decision trees, probability networks, association rules, clustering, and neural networks
- Proven success as a data scientist/architect working with large data sets