Engineering + Technology

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How many engineering disciplines can you name?

UCSB educates students in five key areas: chemical engineering, mechanical engineering, electrical engineering, computer engineering, and computer science. However, there are over 50 other engineering disciplines to discover including aerospace, automotive, biochemical, civil, environmental, geotechnical, industrial, manufacturing, nanotechnology, nuclear, petroleum, security, telecommunications, and traffic engineering. All areas overlap with basic engineering knowledge and skills.

Learn how you can pursue your discipline or pivot your UCSB education into the area of your choice through a career path in Engineering + Technology.

 

 

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Meet Your Counselor

Maddie Foster

Maddie Foster

Career Counselor / Peer & Practicum Supervisor

Engineering + Technology

How I Can Help

With advancements in engineering and technology, we now have the ability to communicate and collaborate across the globe, diagnose diseases earlier and with more accuracy, monitor environmental concerns and alert individuals in danger, and most importantly, take a selfie with dog ears to show the world. All of these innovations started with an idea and a person. That person could be you.

I’m here to help; let’s chat about your strategy to build skills and connect with professionals beyond UCSB.

Learn more about me

Discover Options: Data Science

For complete information on assessing yourself, choosing majors, and exploring careers, review our starter tips to Discover Options in all careers as well as the specialized tips on this page.

Diving Into Data Science

In just the last few decades, we have witnessed an explosion in growth of the amount of data that has been collected and made accessible. According to a 2013 report by Vault, “IBM reported that 90 percent of the world’s data had been created since 2011.” Within only two years, humanity created 90 percent of its data. Now fast forward to today; data science is a rapidly growing industry and there are many different facets to it.

What does the term, “data science” actually mean? What should you study to best prepare for this field? These two seemingly simple questions invoke very complicated answers. Data science draws from various areas of mathematics, statistics, and computer science using theories, algorithms, and tools to better understand and predict modeling, while utilizing the data of the world around us. 

Because the world of data uses various skill sets, there are diverse job families associated with it. One person does not take on everything. Instead, cross-functional teams are formed to integrate both technical and analytical skills. Then, those in high leadership roles (such as CEOs and CIOs) utilize data reports and insights from those teams to inform strategic and operational decisions. 

 

Common Data-Based Job Families

To begin understanding the broad ways in which roles are organized in the rapidly growing and complex world of data science, click the items below.

Data Analysts focus on solving problems by utilizing a variety of data and modeling techniques. They produce reports, identify patterns, and collaborate with others to provide insights. These roles can also be referred to as Business Analysts or Data Mining Specialists, depending on the organization for which they work.

Here are some common tools that these professionals often use:

  • Microsoft Excel
  • SPSS
  • SAS
  • SQL
  • Tableau

Data Engineers utilize hardware and software to design, construct, and maintain organizational data and analytical infrastructure. They focus on the design and architecture of big datasets, and create big data warehouses.

Here are some tools that data engineers typically incorporate into their work:

  • Hadoop
  • NoSQL
  • SQL
  • MySQL
  • Cassandra

Data Scientists advance statistics and machine learning to turn insights into action items. In addition to having similar skills as data analysts, data scientists are expected to have programming skills and the ability to design new algorithms.

These professionals should be able to explain the data through visualization techniques and narrate stories about solutions to their problems. They combine their technical skills with communication skills to be able to speak with stakeholders while understanding the data structure.

Here is a list of example tools that data scientists often use:

  • Python 
  • Hadoop 
  • Machine Learning 
  • RStudio

Functional Analysts utilize data and models to inform decisions within a specific domain, such as actuarial science, finance, human resources, geography, operations, and research. A common example of a functional analyst is a Marketing Analyst, whereas a niche example is a GIS Analyst.

Here are a few tools that functional analysts may use on a regular basis: 

  • GIS
  • HRIS
  • CRM

Not only are the roles different within the field of data science, but all of these positions are also found within various industries. Functional analysts are often found in finance, insurance, healthcare, and retail trade, while data engineers are more common in information technology, manufacturing, professional, scientific, and technical services.

For any industry in which data is collected, there are data scientists analyzing it. 

 

Academic Preparation

Because most positions in data science require technical knowledge, individuals often pursue degrees in mathematics, statistics, and computer science. UCSB offers multiple courses that provide you with the various skills of data science, so be sure to research your academic options in further detail.

Outside of your degree, it is important to obtain the related skills and tools for each position. Keep reading to learn more about gaining experience to build your career toward data science.

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