So, you want to work with data, but you’re not sure which area of expertise is right for you?
At Springboard, we’ve developed several courses that teach students the skills they need to get jobs in different data-focused roles.
Let’s break them down.
- Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company identify and then answer specific business questions, they are filling the data analyst (or business analyst) role.
- Data scientists fine-tune the statistical and mathematical models that are applied onto the data. A data scientist will be able to run a data science project from beginning to end. They can identify a business problem, store and clean large amounts of data, explore data sets to identify insights, build predictive models, and weave a story around the findings.
- Data engineers are in charge of the delivery, storage, and processing of data. A data engineer’s job is to provide a reliable infrastructure for these functions. Data engineers do this by building data pipelines that transform and transport data from various data sources (such as a CRM system) to a storage system such as a data warehouse. These pipelines enable raw data to be converted into an analyzable format to be used in data science projects.
- Machine learning engineers are mostly responsible for building, deploying, and managing machine learning projects. Most machine learning roles will require the use of Python or C/C++. The easiest path to a career as a machine learning engineer, though by no means the only one, is to start off with a software engineering background and then gain the statistics and machine learning knowledge needed to take on the role.
How does Springboard prepare you for these different roles?
The Data Analytics Career Track, developed in partnership with Microsoft, teaches all the necessary technical skills to become a data analyst—fundamental business statistics concepts; key tools like Microsoft Excel, SQL, Python, Microsoft Power BI, and Tableau; advanced analysis techniques. But the 400-hour program also emphasizes structured business analysis training. Through this work (designed alongside experts from McKinsey, Bain, and Wharton), you’ll develop your business thinking skills so you can break down complex problems and test them.
Prerequisites: You should possess strong critical thinking and problem-solving skills, and have at least two years of professional experience working regularly with office, design, or programming tools.
The Data Science Career Track prepares you for your first job as a data scientist by guiding you through hands-on learning projects to replicate the work of data scientists. This is an intensive program with a 500+ hour curriculum designed around 14 data projects. You’ll master the Python data science stack, tackle advanced data science topics, and choose a specialization that aligns with your career goals.
Prerequisites: You should have a strong background in probability and statistics, and be very comfortable programming (in any language)—comfortable enough to pick up a new language using resources on the web.
The Data Engineering Career Track ideal for data scientists, data analysts, or software engineers (including new grads) who want to transition into a new career in data engineering. It will take most students six months to complete at a pace of 15-18 hours/week. Over the course of six months, you will design, build and maintain scalable data pipelines; learn to work with the ETL framework (Extract, Transform, Load) for copying data from multiple sources into one destination; build data pipelines using cloud solutions, virtualization, and containers; design data streams and APIs; learn data warehousing and modeling using intermediate SQL; work with cloud warehouses and AWS Redshift; and learn key data engineering tools including MapReduce, Apache Hadoop, Spark, and more. You’ll also complete 15 technical mini-projects and two capstone projects covering end-to-end development processes you’ll perform as a data engineer, graduating with hands-on experience and a portfolio of work to share with employers.
Prerequisites: Generally, students must have 1+ years of experience in data or software engineering.
The Machine Learning Engineering Career Track offers a rigorous and deeply technical curriculum, teaching you the foundations of machine learning and deep learning. But it’s also hands-on. Of the 400 hours of overall work we estimate it will take to complete this course, 100 hours go toward capstone projects. You’ll build and deploy large-scale AI systems—with guidance from an experienced machine learning engineer currently working in the industry.
Prerequisites: You should have at least one year of professional software engineering experience using a general-purpose object-oriented programming language, such as Python, Java, and C++. And you should have completed university-level courses on probability and descriptive statistics, linear algebra, and calculus.
Still not sure which data course is right for you?
No matter which course you choose, you’ll enjoy weekly one-on-one calls with your personal mentor (plus unlimited access to additional mentorship), one-on-one career coaching during the course and for six months after graduating, plus support from student advisors, community managers, and your fellow learners.
And each course is backed by our job guarantee—if you don’t find a job within six months of graduating, we’ll refund your tuition.
If you’re not sure which path is right for you, take this quiz.