Finally, a data engineering bootcamp that delivers

We love teaching data engineering. It allows you to focus on modern fundamentals like Python, SQL, and big data and cloud computing. Because of this, our students graduate with in-demand data engineering skills, but also skills that allow for a flexible career going forward -- whether students wish to pursue machine learning or backend engineering.

Multiple graduates have been hired as backend engineers right out of the bootcamp, and many data engineering skills are a prerequisite to becoming a machine learning engineer.

In fact, our first motivation for building a data engineering bootcamp was simply that we saw that going deep in the data engineering skills was a better entryway into data science and machine learning than by learning the skills taught in a data science bootcamp.
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First, we try not to just have you check off skills on a list but go in depth in a few core skills, and then branch out into other curriculum that reinforces those skills.  For example, our second module in backend development goes deeper into Python and SQL by teaching students how to build an API.  And tools redshift, snowflake, and DBT reinforce SQL skills.  Finally, tools like AWS, Docker, and bash teach the skills needed for cloud computing.

Second, we built the course to continuously providing mechanisms to ensure students end up landing a job.  This starts with admissions, where we only admit students we believe will be successful.  Then, it continues with our curriculum where we only teach material that employers are looking for, and remove the material if the job market changes.  

With our internship program, we vet companies to make sure students will be able to contribute, but still will be challenged.  And with our career services, we continue meeting to continue training for students and connect students with companies to assist in their job search.  It’s this combination has allowed all of our graduates to land jobs.
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Our data engineering course has a 100% job placement rate, and there are multiple ways that we achieve this.  

1. Ongoing Technical and Career Coaching - Even after our students complete our program, we continue to meet with them twice weekly to ensure that they continue to improve their skills and progress through the job search.   We work with each student individually to help them clean up their Linkedin profile and revise their resume so that it is most attractive to employers.  

2. Job placement - We help students in their job search by reaching out to employers on their behalf, gathering information about what employers are looking for and then making the connection.  

3. Program structure - Our curriculum focuses on skills that are most in demand by employers.  This makes it an easy sell for students to land interviews.  In addition, our internship program gives students on-the-job experience that employers love to see in a candidate.  
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To enroll in the course, the first step is to schedule a zoom call to learn more about our online bootcamp.  From there, we’ll schedule technical interview that assesses your Python programming skills.  The interview will cover the Python material in the first ten lessons of our free Python for data course.  

When we decide whether to admit students into our program, the primary question we ask is Do we believe this student will get a job upon graduating.  We only admit students we are confident will graduate from the course with a positive outcome.
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We developed our curriculum by (1) scraping job postings for data engineerings to identify skill sets (2) talking to hiring industry experts (data engineers, hiring partners, internship partners) and monitoring data engineering communities and (3) seeing what is actually asked on technical interviews.  

Our goal is for you not just to land your initial job, but to have a flexible career going forward.  While we have curriculum on Apache spark and Kubernetes, we moved it out of our core curriculum as we found it more asked of midlevel engineers, and now our graduates.  The same goes for real-time streaming tools like Kafka or big data databases like hadoop — these skills rarely came up in job interviews.  And we do not teach NoSQL as it has not come up in job interviews, and also is only listed in 15% of data engineering job listings.

We have chosen AWS as our cloud provider, however students have also learned to use GCP (Google's cloud platform) and Microsoft's Azure platform as part of their internship experience.

One thing to note is that because we constantly update our syllabus, we have a lot of battle tested curriculum outside of our core curriculum (one thousand pages).  And all of this is available to our students.  So if a student needs to learn a topic for an internship or a job interview, we often have material to ramp them up.
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None.  The primarily task of data engineers is to build the data infrastructure needed for machine learning and data analytics.  While learners are asked to perform data analysis work as part of their internship, students lean on their engineering skills, understanding of data collection and data processing, and not statistics or calculus to perform these calculations.    
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Data analysts may be asked to use a combination of spreadsheets, SQL, and data visualization tools to draw insights. Data scientists by contrast generally know either R or Python, as well as related machine learning libraries like SKLearn, and have a strong understanding of statistics to draw insights and perform predictive modeling.

Data engineers by contrast should be well versed in backend engineering end Python, have a deep understanding of SQL, cloud computing, big data tools like snowflake and redshift, and setting up data pipelines with orchestration tools like airflow.
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The primary language is SQL (although not technically a programming language), followed closely by Python. Data engineers should also know HTML and CSS so that they can scrape websites. And some literacy in Javascript also helps as it comes in handy for scraping websites, and for setting up marketing analytics pipelines.  (We have curriculum on all of these subjects for our students, even though Javascript does not often come up in interviews).

Some job descriptions may accept (or prefer)literacy in other object oriented programming languages like Java, as an alternative to Python. Still, Python and SQL are the skillsets requested most.
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Students complete their capstone projects through their internship.  Student projects have involved automating the deployment of machine learning models, setting up a data pipeline pulling data from slack using an EL tool, performing analysis using DBT, and orchestrating with Gitlab actions.  Still other students have worked with cryptocurrency databases to develop dashboards that helped their internship partner understand market fluctuations.  

During the post-work class sessions multiple students are working with Code For Boston, where they are helping to build a data pipeline that tracks police misconduct.