Online Master's in Data Science for Jobs in California

According to IBM, 90 percent of all the data in the world today has been created in the last two years. All of this readily available data is a veritable playground for data scientists with the skills necessary to transform big data into something companies in all industries can use to gain new insights.

From ecommerce and healthcare to business and finance, data scientists are a hot commodity in firms with massive volumes of data to collate and derive meaning from. Data scientists help realize the full potential of big data by bringing structure to it, finding compelling patterns in it, and advising executives on the possibilities and implications of making sense of it.

According to the executive recruiting firm, Burtch Works, 36 percent of all data scientists work on the West Coast. Data scientists on the West Coast also earn the highest salaries, bringing home a median base salary of $110,000, which is 22 percent higher than those in the Northeast and 38 percent more than those in the midwest.

Big data, of course, has a permanent home in the Palo Alto area, the birthplace of Silicon Valley (home to Hewlett Packard, Walker and Company, Benetech, and Beneficent Technology, just to name a few), but the career opportunities for data scientists in California certainly do not end there. Data scientists enjoy plenty of professional opportunities working in the Data Collection and Analysis Team of Intel in Folsom, in San Francisco’s Square, and on the Data Sciences Team of Hulu in Santa Monica.

Professional opportunities in big data are not emerging, they are here, and California’s big name companies are vying for data scientists with a creative mindset who can take their company’s vision to the next level.

Preparing for a Master’s Degree in Data Science in California

Institutions offering data science master’s degree programs demand strong credentials, including an impressive undergraduate showing.

Undergraduate Degree and Master’s Prerequisite Courses

Many schools that offer graduate programs in data science require candidates to hold an undergraduate degree in one of the following majors:

  • Computer Science
  • Applied Statistics
  • Math
  • Engineering
  • Physics
  • Biological/natural sciences

Most schools also require candidates to have a minimum 3.0 GPA from their undergraduate work.

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 commonplace for data science programs to require candidates to sit for an admissions interview, during which time they assess their prerequisite knowledge. Many programs admit students based on the successful completion of specific courses.

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 individuals can gain the experience needed to enter a master’s degree in data science. For example, Kforce, located in Irvine, hires junior data analysts that work at both the strategic level and the tactical level across multiple teams. Acxiom Labs in Redwood City hires entry data scientists that help conceive and build prototypes of intelligent applications based on existing Acxiom technologies, Acxiom 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. Test takers may also 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 the ever-changing data science 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.

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 (sourced in March 2016) 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

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