Our world is overrun with data. Businesses are inundated with overwhelming amounts of data that need sorting from online payment portals to social media platforms. Data scientists are the ones hired to fix this problem. They sort and analyze data so companies can make informed business decisions, whether resource management or marketing strategies. The field of data science is relatively new, yet job opportunities continue to blossom. The U.S. Bureau of Labor Statistics estimates a 36% increase in data scientist jobs by 2031, amounting to 40,500 new openings. A career in data sciences will provide ample job security while promising intriguing, mentally stimulating work. But what if you want to sharpen your skills and portfolio before sending out job applications?
Washington University’s fully online boot camp includes over 400 hours of curriculum designed to help you build a well-rounded portfolio. With two hands-on capstone projects and lessons created by experts in the field, you’ll learn everything you need to stand out in the hiring pool. In addition to learning the Data Science Method, you’ll work with an industry mentor available for 1-on-1 calls. Your mentor, a dedicated student advisor and optional career coaches will guide you toward becoming a successful data scientist.
The boot camp curriculum is built out into two parts: Foundations and Core. Our application process will help you determine where to start.
Foundations will cover the basic data science concepts, such as the most applicable programming languages (including Python). This solid foundation will lay the groundwork for your success throughout the program.
Core includes the bulk of your course material.
The curriculum offers three different specialization tracks. Choose which best suits your interests and skills, and equip yourself with the training for a robust portfolio.
Explore the boot camp curriculum, specialization tracks and support team below.
Washington University’s Data Science Boot Camp will equip you with tools you can use across various industries in data science. Here are some job titles you can expect to find in the field and the average salary for each.
Data Scientist: $118, 967
Data Science Manager: $143,842
Data Engineer: $116,436
Big Data Architect: $142,908
Annual Median Advertised Salary. Source: Lightcast; Oct 2022 - Sep 2023; 0-3 years minimum experience required. United States.
The Core material units include fundamental data science concepts and will teach you the skills needed for each. The individual units are built out with lectures, theory, hands-on projects, coding exercises, real-world concepts in the field and reading exercises.
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
The curriculum is divided into three unique areas of specialty: The Generalist Track, The Business Insider Track and the Advanced Machine Learning Track.
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.
The cornerstone of your portfolio will be your two capstones. These will be your primary focus and help you build foundational technical skills and the confidence to show your work to future 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
The Data Science Boot Camp is entirely online, but throughout the program, you’ll have access to a critical support team. Not only will your team be available for questions every step of the way, but they’ll also help you build lasting connections and provide that necessary networking component that makes for a successful career in data science.
Student advisor: Having a student advisor along the way is integral to your success and accountability. Your student advisor will share helpful tips and ensure you’re on track to meet your goals.
1:1 personal mentor: Your unique mentor will chat with you regularly and give project feedback. As an industry expert, their feedback is critical, especially on your capstone projects.
Career coach: Career-searching will be easier with optional career coaches who help you develop your career strategy.
Slack community: Build lasting relationships with fellow students who can offer peer feedback and share their projects with you.
Mentorship is key to learning lifelong skills from those before you. Your mentor will help you grow with weekly meetings, where you will receive personalized feedback and get real-world tips from an industry expert. Our mentor-hiring requirements are strict, with only 1 in every 12 applicants accepted. We take this mentorship seriously and will only give you the best in the field.
With your mentor, you’ll get the following:
1:1 regular calls: Video calls with your mentor will build up your confidence as you receive help, individualized feedback and career tips from someone in the field.
Accountability: Our mentors hold you accountable for sticking to your career and program goals.
Dedicated mentor calls: We’ll provide you with other experts in the community at no additional charge.
Meet some of our mentors:
Washington University wants to see you grow into a well-equipped, qualified professional. This boot camp is designed for those with a thirst for learning who meet the following requirements:
Fluency in English (written and spoken), as determined by interactions with the admissions team
Proficiency in statistics and math
We will evaluate your skills during the application process to determine your foundational level.
Students with no coding experience, but who are skilled in math and statistics, will be given units that cover the core data science concepts, like Python, which is necessary for doing well in the program.
Students with programming experience–for example, work history as a software developer or data analyst–will have the option to skip the introductory unit and head straight into the core curriculum.
Data science boot camps are worth it if you are looking to switch careers or learn new programming languages and tools. The fast-paced environment of a boot camp can be beneficial if you have the motivation to learn and apply yourself.
The Washington University Data Science Boot Camp 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 boot camp 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. According to Lightcast, the annual median advertised salary for data scientists in the US is $118, 967.
Source: Lightcast; Oct 2022 - Sep 2023; 0-3 years minimum experience required. United States.
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 boot camp costs vary, but can be anywhere between $10,000 and $20,000. The Washington University Data Science Boot Camp is $9,900 when paid upfront.
Speak to our enrollment team by completing an application, email Carolina, our enrollment advisor, or explore more frequently asked questions.
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