The data engineering bootcamp that delivers

We love teaching data engineering. It provides students with a strong foundation in modern fundamentals like Python, SQL, big data and cloud computing. In fact, we often describe the data engineering skillset as backend and cloud engineering skills with a specialization in data pipelines.

Because of a focus on the fundamentals, students have been hired as data engineers, but also backend engineers and data scientists right out of the bootcamp. The reason this skillset is so widely applicable is because collecting, cleaning, and organizing data is  critical for drawing insights from this data -- whether for an analytics team, or to feed into a machine leaning model. And this data engineering work is also some of the most challenging work for a data driven organization. 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 a career in data engineering, machine learning or backend engineering.

Our course from beginning to end is focused on preparing our students to (1) land a software/data engineering position and (2) develop the fundamental skills that allow for a flexible career path going forward.  
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To achieve the first goal, we designed the course to provide a true onramp to getting hired as a data or software engineer.  This starts with admissions, where we only admit students we believe will be successful.  And it continues with our curriculum where we only teach material that employers are looking for, and remove the material if the job market changes.  

Then, with our internship program in the second half of the course, we select companies who provide projects and mentors that will challenge and help develop the skills of our students.  And with our post-graduation classes, we continue meeting twice weekly to ensure our students only improve their skillset.  With this combination, 90% of our students have graduated and all of our graduates have landed jobs.
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Now to the second point of our focus on fundamentals. We devote 240 hours of classroom time just to backend development skills — primarily Python and SQL.  Take a moment to consider how this compares to other online courses or bootcamps. So our philosophy is to not just check off skills on a list but instead to go in depth in a few core skills, and then branch out into other skillsets. This is because for us, programming is a craft. And by keeping Python and SQL as our focus, we develop that craft -- whether through teaching object oriented design patterns, writing clean functions, learning data structures and algorithms, or writing tests.  We believe it's this emphasis on the craft of programming that allows our students contribute in their internship halfway through the program, get a job after graduating, and have a flexible career in the future.

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. In addition, we only admit students who display a curiosity and demeanor that will contribute positively the classroom culture, and internship experience.
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We developed our curriculum by (1) talking to hiring industry experts (data engineers, hiring partners, internship partners) and monitoring data engineering communities (2) seeing what is actually asked of our graduates on technical interviews and (3) scraping job postings for data engineering positions to identify required skill sets .  

Our goal is for you not just to land your initial job, but to have a flexible career going forward.  While we have written curriculum on Apache spark and Kubernetes, we moved it out of our core curriculum as we found it more asked of midlevel engineers, and not 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 as it's the industry leader, 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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Our internship program runs from weeks 12 to 24 of the course, and provides students with data engineering work experience even before they graduate.  In the internship, students work in pairs to complete projects for various tech companies, pro bono. Students work six hours per week during class time, and many students put in anywhere from 2 - 15 hours outside of class, depending on their availability.  

To ensure companies are a good match for the program, we speak with various tech companies to make sure they have data engineering projects that students will be able to contribute to, and that the company has mentors to meet with students on at least a weekly basis.  Past projects have ranged from building an API for NYC school buses to help manage their bus fleet, to building ETL pipelines for a crypto company, to performing ETL and building a data warehouse for a news analysis organization.

Our data engineering course has a 100% job placement rate among graduates, 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 students twice weekly through ongoing classes to ensure that students 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 they are 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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The most popular jobs accepted by our students after graduation were data engineer (over 50%) and backend/software engineer, and data scientist.  The median salary of our graduates is $115k, with the lowest salary at $80k per year, and the highest at over $140k.

In addition to data engineer, backend/software engineer, and data scientist, other positions students would be eligible for include business intelligence engineer, analytics engineer, and data analyst.

Data analysts may be asked to use a combination of spreadsheets, SQL, and data visualization tools to draw insights. Similar to a data analyst is business intelligence engineer, which leans more on SQL than tools like excel, and data visualization tools like looker or metabase. 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 skills like Python, and have a deep understanding of SQL, cloud computing, big data tools like snowflake and redshift, and setting up data pipelines with scheduling tools like airflow.  

Machine learning engineers typically have both the data engineering skills and the knowledge of machine learning models that a data scientist would have.  This is why we recommend getting the data engineering skills first as an entry into a data career.    

The primary language is SQL (although not technically a programming language), followed closely by Python. SQL is used query data from big data databases like snowflake or redshift, or standard transactional databases like Postgres.  Python is used to pull data from APIs, coerce data, and potentially feed it into a machine learning model.  In addition to these programming skills, engineers should be well versed in cloud computing and building data pipelines.  Generally, early in their career, data engineers can move between different cloud providers whether Amazon, Microsoft or Google.
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Data engineers should also know HTML and CSS so that they can scrape websites. And some literacy in Javascript may come 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.

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 have worked with Code For Boston, where they are helped to build a data pipeline that tracks police misconduct, or another that tracks lobbying in local government.

Very little.  The primarily task of data engineers is to build the data infrastructure needed for machine learning and data analytics.  While students may be asked to perform data analysis work as part of their internship, students generally lean on their engineering skills to perform these calculations.