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Data Science Bootcamp with USF CTPE
Data science is one of the fastest-growing fields of this decade. The amount of data created each day has surged thanks to the ubiquitous storage of everything from health records to the millions of actions taken on websites and mobile devices. Being able to make sense of all this data — and thus drive intelligent, outcomes-focused decisions — is a vital skill and one that is at the heart of being a data scientist. Learning data science is one of the best career investments you can make. In fact, according to LinkedIn, data science hiring has increased by 46% since 2019.
The 100% online USF CTPE Data Science Bootcamp consists of a 400+ hour curriculum designed around two hands-on capstone projects. You’ll learn the Data Science Method, the process that leads to successful data science projects. You’ll also have access to a 1-on-1 industry mentor to discuss your projects with weekly, a dedicated student advisor and career coaches to help you succeed.
The curriculum is divided into a two-part structure. During your application, you’ll take a technical skills survey to determine your starting line:
Foundations: Provides prep materials and covers essential data science concepts, including Python, that you’ll need to succeed in the program core.
Core: This section contains the bulk of the coursework, and prepares you for a data science career.
Within the core curriculum, you’ll be able to choose from one of three specialization tracks, where you can learn specialized skills to help you stand out from other data scientists.
Data Science Bootcamp curriculum
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:
Exploratory data analysis
Pre-processing and training data development
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.
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:
The Business Insider track focuses on developing your data visualization and business analytics skills.
The Generalist track offers a mix of technical skills, business skills, and mathematical knowledge.
The Advanced Machine Learning track focuses on the deployment of machine learning models.
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
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.
Hands-on, portfolio-worthy projects
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
Bootcamp student support
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 weekly 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.
Personal 1-on-1 mentorship weekly
Mentorship is a critical aspect of the Data Science Bootcamp. You’ll meet weekly with your mentor who holds you accountable, helps you grow, and will impart real-world knowledge and advice. Our mentors are experiences data scientists; we only accept one in 12 applicants.
Meet some of our mentors:
Data Science Bootcamp prerequisites
The core Data Science Bootcamp is designed for students with prior experience in statistics and programming, such as software developers, analysts, and finance professionals.
For those without prior coding experience, but with proficiency in math and statistics, you may enroll into foundations, which provides prep materials and covers essential data science concepts, including Python, which you’ll need to succeed in the core curriculum.
During the application process, you’ll take a technical skills survey to determine your starting line.
Tuition & scholarships
Access and affordability are important – which is why USF CTPE offers options when it comes to covering your Data Science Bootcamp tuition.
By paying upfront, save 14% off of the full bootcamp tuition of $11,500.
Pay only for the months you need up to 9 months or $11,500.
Paid at the time of enrollment: $500
Monthly payments during the course: $58 -$154 (interest payments only).
Monthly payments after course: $380-$435 for 36 months.
For more details see the FAQ.
Please note: lending might not be available in all 50 states - click here for the current full lending list.
*range varies based on approved interest rate and only available for U.S. residents
To make a career in data science more accessible, USF CTPE offers the following scholarships:
$750 Veterans & Active Military Scholarship
$750 Women in Tech Scholarship
Confirm your eligibility by speaking with an admissions director. Only one scholarship may be applied and cannot be combined with other discounts.
Data Science Bootcamp FAQs
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 USF Data Science Bootcamps 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.
Data Engineer: $112,493 average salary
Data Scientist: $117,212 average salary
Data Science Manager: $162, 066 average salary
Marketing Research Analyst: $60,280 average salary
Machine Learning Engineer: $131,001 average salary
Data Architect Manager: $121,422 average salary
Product Analyst: $78,401 average salary
The salary range for a data scientist can vary based on experience, location, and company, but Salary.com reports a range of $121,443 - $150,223.
Data scientists are in high demand. The U.S. Bureau of Labor and Statistics (BLS) reports an expected change in employment of 22% between 2020 and 2030, which significantly outpaces the average of all occupations: 8%.
Data science bootcamp costs vary, but can be anywhere between $10,000 and $20,000. The USF Data Science Bootcamp is $8,940 when paid upfront, and is much more affordable than a traditional degree program.
More questions about the program?
Speak to our admissions team by completing an application, email Nigel, our admissions assistant, or explore more frequently asked questions.