Data science is one of the fastest-growing fields of this decade. With the exponential growth of data generated daily from health records to countless online and mobile activities, the ability to extract meaningful insights has become a critical skill. Data scientists are at the forefront of driving intelligent, outcome-oriented decisions. And a role in data science can be incredibly fulfilling; U.S. News & World Report ranks Data Scientist #8 in their list of the 100 Best Jobs of 2024, looking at qualities like good compensation, room for advancement, work-life balance, and providing an intellectual challenge.
Gonzaga University's Data Science Bootcamp, in partnership with Springboard, offers a comprehensive, 100% online program designed to equip you with the skills and knowledge necessary to excel in this dynamic field. Through a rigorous, 400+ hour curriculum, you'll delve into the data science methodology and gain hands-on experience through two capstone projects.
Our personalized approach includes 1-on-1 mentorship with industry experts, dedicated student advising, and career coaching to support your learning and career goals. To cater to your specific interests and career aspirations, you can choose from three specialization tracks.
The curriculum is structured in two parts:
Foundations: This section provides preparatory materials and covers essential data science concepts, including Python, which are fundamental to your success in the program's core.
Core: This section constitutes the majority of the coursework and prepares you for a rewarding career in data science.
By enrolling in Gonzaga University's Data Science Bootcamp, you'll gain the skills and confidence to embark on a successful career in data science.
Read on to learn more about the bootcamp curriculum, specialization tracks, and student support.
Because of the notable employers in Washington, there's lots of opportunity for roles that leverage data science skills. Compensation can vary based on industry, years experience, and more, but some common job titles and average salaries in Washington include:
Each unit of the Core material will cover a key data science concept and the skills associated with that concept. The units feature a mix of materials: projects, lectures, theory, coding exercises, reading/viewing exercises, and career-related coursework.
The units center around the Data Science Method. This method involves six steps:
Problem identification
Data wrangling
Exploratory data analysis
Pre-processing and training data development
Modeling
documentation
Python has become the lingua franca of data science. In this section of the course, you'll learn how to program in Python, follow best coding practices, and start using an ecosystem of useful and powerful Python-based tools.
In this section of the Core material, you’ll learn how to leverage Structured Query Language (SQL) to query relational database management systems. In other words, you'll use queries to understand the data contained in databases.
A data story is a powerful way to present insights to your clients, combining visualizations and text into a narrative. Storytelling is an art and needs creativity. This section will try to get your creative juices flowing by suggesting some interesting questions you can ask of your dataset. It will also cover a few plotting techniques you can use to reveal insights.
Statistics is the mathematical foundation of data science. Inferential statistics are techniques that help us identify significant trends and characteristics of a dataset. They’re not only useful for exploring the data and telling a good story but for paving the way for deeper analysis and actual predictive modeling. In this module, you’ll learn several critical inferential statistics techniques in detail.
Machine learning combines both computer science and statistics to extract useful insights and predictions from data. Machine learning lets us make valuable predictions and recommendations and automatically finds groups and categories in complex datasets.
You'll learn and use the major supervised and unsupervised machine learning algorithms. You'll learn when to use these algorithms, the assumptions they incorporate, their tradeoffs, and the various metrics you can use to evaluate how well your algorithm performs.
Each career unit is interspersed between the technical units and follows the progression of a job search. You’ll learn how to:
Create a job search strategy
Create an elevator pitch and LinkedIn profile
Conduct an informational interview
Find the right job titles and companies
Prepare for and get interviews
Interview Effectively
Negotiate Salary
After the core units are completed, you’ll then have the option to choose a specialization track, which will teach you unique skills that are intended to help you stand out from other data scientists. Choose from one of the following:
This track will prepare you to take on versatile data science roles across a wide variety of business domains and geographical locations. You’ll build on the foundational skills you learned in the core units and tackle more advanced topics like working with Big Data and software engineering best practices.
This track aims to teach you advanced data visualization and business analytics skills to extract actionable business insights. While you will have the ability to build predictive machine learning models, you'll primarily focus on learning how to identify insights and effectively communicate recommendations.
This track aims to teach you advanced machine learning skills and concepts, including deep learning and the deployment of machine learning models on standard industry platforms. If you want to broaden your machine learning skills, this track may be the right one for you.
In addition to small projects that help you reinforce technical concepts, you’ll complete two capstones which will be the cornerstone of your portfolio that you can show to hiring managers.
Your first capstone project comes up fairly early in the course. For this project, you’ll be given a lightweight introduction to each step of the Data Science Method. You’ll then be guided through each of those steps with helpful tips and instructions. This first capstone builds your foundational understanding of each of these critical steps while also allowing you to practice each step before applying your knowledge to your second capstone.
This capstone takes place later in the bootcamp and has less guidance. You’ll be asked to:
Come up with a project idea and proposal
Find and wrangle data
Use exploratory data analysis techniques to understand that data
Pre-process and create a training dataset
Build a working model
Document and present your work
You’ll complete this 100% online bootcamp on your own time, but you’ll always have the support of a team throughout your experience. You’ll have access to:
A student advisor who you’ll work with throughout the program. They can answer any questions you have and help you overcome obstacles.
A personal 1-on-1 industry mentor who you’ll meet with regularly to discuss your projects and receive feedback.
A career coach who can help you develop a tailored job search strategy based on your career goals.
A slack community of other students who you can connect with.
Mentorship is a critical aspect of the Data Science Bootcamp. You’ll meet with your mentor regularly, who holds you accountable, helps you grow, and will impart real-world knowledge and advice. Our mentors are experienced data scientists; we only accept one in 12 applicants.
Meet some of our mentors:
This bootcamp is designed for students who have:
English fluency (spoken and written), as determined by interactions with the admissions team
Proficiency in math and statistics
During the application process, you’ll take a technical skills survey to determine your starting line:
Students without prior coding experience, with proficiency in math and statistics will be provided units that cover essential data science concepts, including Python, which you’ll need to succeed in the core curriculum.
Students with prior experience in statistics and programming, such as software developers, analysts, and finance professionals, will have access to the same introductory units, but they will be optional. You’ll be able to move right into the core curriculum.
Data science bootcamps are worth it if you are looking to switch careers or learn new programming languages and tools. The fast-paced environment of a bootcamp can be beneficial if you have the motivation to learn and apply yourself.
The Gonzaga University Data Science Bootcamps in partnership with Springboard provides access to a 1-on-1 industry mentor, optional career curriculum, and a career coach to help prepare you for the next step in your career.
Data science is the process of extracting knowledge from structured and unstructured data. It involves using mathematical, statistical, and computer science techniques to analyze data, identify patterns and relationships, and propose insights that can help organizations make better decisions.
Data science is used in a wide range of industries, including finance, healthcare, manufacturing, marketing, and retail. It's an important tool for making informed decisions about everything from product pricing to inventory management to customer segmentation.
A data scientist uses their knowledge of statistics and computer programming to clean data, create algorithms and models, and analyze large data sets, looking for patterns and correlations that can help them understand what's happening within the business.
Once they have identified any trends, a data scientist will then create reports and presentations that explain their findings in a way that is easy for non-technical people to understand. This allows business decision makers to make informed choices about how to improve their business based on the data that has been collected.
How long it takes to become a data scientist depends on your background and prior experience. A data scientist typically has a mathematics, statistics, computer science, or engineering degree. However, there are many self-taught data scientists who have no formal education in these areas.
A data scientist can get up to speed fairly quickly if they are familiar with Python and have some basic knowledge of machine learning algorithms. But it would probably take someone several months to a year to become a data scientist if they had no prior background in this field.
Our bootcamp can help you prepare to become a data scientist in less than nine months.
Especially since this bootcamp offers three specialization tracks (generalist, business insider, and advanced machine learning), the skills you build can vary. After the bootcamp, you may choose to explore roles like:
Data Scientist
Data Engineer
Data Science Manager
Marketing Research Analyst
Machine Learning Engineer
Data Architect Manager
Product Analyst
The salary range for a data scientist can vary based on experience, location, and company. Indeed reports that data scientists in Washington earn $138,124 on average.
Data scientists are in high demand. The U.S. Bureau of Labor and Statistics (BLS) reports an expected change in employment of 26% between 2023 and 2033, which significantly outpaces the average of all occupations: 4%.
Yes, Gonzaga partners with Springboard, an industry-driven education company that prepares learners for high-growth careers, to offer the Data Science Bootcamp. These courses are designed, administered and led by Springboard, and the curriculum has been vetted and approved by Gonzaga University School of Engineering and Applied Science.
After successfully completing a bootcamp, you’ll receive an official Certificate from Gonzaga University and verification of continuing education credits.
Speak to our enrollment team by completing an application, email Carolina, our enrollment advisor, or explore more frequently asked questions.
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