How to Prepare for Springboard’s Data Science Career Track

Table of Contents

• Meet Your Expert: Kelvin Tse
• Inside the Data Science Prep Course
• The Springboard Data Science Curriculum
• Learning Data Science in 2021

Springboard’s online Data Science Career Track was created with one thing in mind: to help students land their first data job – but that process starts before day one of class. Kelvin Tse, Associate General Manager at Springboard, explains how this career track is getting students job-ready in 6 months with a hands-on curriculum including over 30 projects! Learn what it takes to get accepted into the program, and why Springboard’s Data Science Prep Course is an excellent fit for total beginners and students needing to brush up on their basic data skills.

Meet Your Expert: Kelvin Tse

• Kelvin is the Associate General Manager at Springboard for data programs, overseeing the strategy behind the Data Analytics program, Machine Learning Career Track, the recently launched Data Engineering Career Track, Data Science Career Track, and the Data Science prep course. 
• Kelvin’s main goal is to make sure the students in Springboard programs are having an exceptional experience, learning the right skills both technical and career, and achieving their career goals whether it is a career switch, a new job, or a promotion.

Who Should Take the Data Science Career Track?

Is there an ideal student for the Data Science Career Track at Springboard?

We are looking for people who are highly motivated and know exactly what they can achieve with their next career goals. Our students know they want to become a data scientist whether that is a promotion, career switch, or new job. The motivation is so important to sustain you through the journey because it is a long and challenging one. We want our students to be ready for that experience. Going in with your eyes wide open, knowing that it will not be a walk in the park is vital. You need to commit the time and put the work in to learning the material in order to land that first job. You need to talk to many people and push yourself out of your comfort zones. 

Is the Data Science Career Track geared for career changers and upskillers?

The Data Science Career Track is a great option for both career changers and upskillers because the skills are useful at every point of someone's data career. They can be relevant and applicable whether you are a couple of years out of school or a 10-year seasoned analyst who is looking to level up and become a data scientist. The flexibility in the bootcamp schedule allows students who may be working full-time the ability to pick up the program on the weekend and deepen their skillset. We have seen both types of students join our program and find success. 

What are the technical and soft skills that an applicant needs in order to get into Springboard’s Data Science Career Track program?

Our Data Science Career Track is a fairly advanced course. Our prerequisites for students are:

• 6 months of coding experience, specifically with object orientated programming languages like Python, Java, or C++. (Python is ideal and easiest for beginners to learn!) 
• Knowledge of basic probability and statistics.
• Some fluency in English because it is a key part in landing a job.

Besides that, we are looking for applicants who have good collaboration and communication skills. They must also have a strong sense of what they want to achieve and their career goals should be focused. 

Should applicants expect a coding challenge in the admissions process?

Yes. Every applicant is required to take a technical skills survey, which includes a coding challenge. 

Can an applicant re-apply if they don’t get in on their first try?

Students can definitely re-apply — we encourage that! Our standard policy allows students to retake the technical skill survey after two weeks to give them time to study. If students want to take the survey in less than two weeks, we ask that they show some proof of their studies with prep resources. That could be screenshots of completed exercise work or course completion certificates from another website. We want to make sure that they are putting in the time to learn the material before taking the survey again. 

Inside the Data Science Prep Course

Tell us about Springboard’s Data Science Prep Course! Who is this program for?

The Data Science Prep Course is meant for students who are interested in our Data Science bootcamp but may not be ready yet. It teaches the foundational Python and statistical skills that are required to be successful in our career track. For some of our students, this course helps them brush up old skills. It also gives students a taste of what the full program will be like and helps them to decide if they want to make that commitment. 

What will students learn in the prep course?

In the prep course, students will get a brief and basic introduction to: 

• Python including Pandas 
• Probability and statistics
• Computer science, such as data structures and simple algorithms
• Data exploration

How is the prep course taught? 

The prep course is taught in the same way as our primary career track, complete with instructional support. It's primarily self-paced but includes 30-minute weekly calls with the industry mentors (the same mentors that work with the data science students). Prep course students can also receive a student advisor and attend office hours. We try to closely imitate the main career track program.

Will students build projects in the prep course?

There is a small, lightweight project at the end of the prep course. Students work on the project in a real data science environment, which in this case is a Jupyter Notebook. They will be given a large data set and will take it through every step of the data science pipeline to wrangle the data, complete their analysis, and finish the project. It's a good first taste of what students can expect in the career track program. 

Is the prep course mandatory for anyone accepted into the program?

No, it's not mandatory. We encourage students to directly apply for the Data Science Career Track program if they feel they are ready.

If a student completes the prep course, are they guaranteed a spot in the Data Science Career Track?

Prep course students are still required to take the technical skills survey to be considered for the career track program, but many of our prep students go on to join the Data Science Career Track. There is a small tuition fee for our prep course program, but if you decide to join our career track program afterwards, we will take the full cost of the prep course and apply it to their Data Science Career Track course. The prep course is effectively free if a student continues to move forward with Springboard.

The Springboard Data Science Curriculum

What can students expect to learn in Springboard’s Data Science Career Track?

The Data Science Career Track is an online, 6-month program with a focus on helping students land their first data science job. The Data Science Career Track was actually the first career track that we launched at Springboard so we are really proud of it! On Course Report, we have nearly 800 reviews and an average rating of 4.63. Students get a lot of value out of this program. 

We cover a lot in the program, but here are the key areas:

• Python - Python is the bread and butter of any practicing data scientist. Within that Python bucket, we cover a standard library that includes Pandas and Matplotlib. 
• Data Wrangling - With data wrangling, students learn how to take a real data set and prepare that for further analysis. Any data scientist will tell you that there is a lot of work involved there. 

• Statistics and Probability 

• Exploratory Data Analysis - I would call this skill, "How to tell a data story." A data scientist is not just responsible for analyzing the data, but also telling a story and solving a business problem. That's the difference between a theoretical conceptual data science learner and a practicing data scientist. It's about how to create value for your team and company. 
• Machine Learning - We spend a lot of time in this career track on machine learning. The most foundational elements we cover are supervised and unsupervised algorithms, logistic regression, decision trees, clustering, and ensemble methods like random forest and gradient boosting.
• Software Engineering  - I would call this data science at scale. If you are working at a company, there will be expectations with some companies that you can apply those immediately with basic software engineering skills or for a very large-scale data set. We will teach things like map reduce and SPARC. 
• Advanced Machine Learning - We cover topics like natural language processing and some deep learning. 

How do students learn data science remotely? Is this asynchronous learning? 

One of the key teaching philosophies of Springboard is active learning. We believe in learning by doing and learning through experience. There is a big difference between understanding the data science concepts and theories and being a practicing data scientist. We try to help students achieve the latter so that they can successfully get that first job. In practice, it’s many hands-on projects for our students to work through. 

Springboard is fairly unique because we are not an instructor-led program. Our programs are self-directed with a large support system built around the student. Students have weekly sessions with an industry mentor, someone who has already been practicing as a data scientist for many years. Students also have sessions with a student advisor, office hours, plus other support structures designed to help a student get through the curriculum. 

What kinds of projects do Springboard’s data science students work on?

In the last year or so, we have doubled the number of projects in our curriculum to expand that active learning experience. Students will now complete 36 projects, and they are all geared to build that hands-on training. 

There are three different types of projects:

1. Case Studies - These focus on a particular topic, such as supervised and unsupervised learning. Case studies are meant to be challenging for the student and force the comprehension of that piece of material.
2. Guided Capstones - This is an end-to-end project that guides students through the complete data science process from the beginning to the end. We have created detailed instructions at each step and provided pseudo-code for the students to help make it understandable for them. The primary goal of guided capstones is to give students an overview of a complete data science problem using a real-world scenario.
3. General Capstones - This is where a lot of the challenging work comes in. General capstones are most similar to what a true data scientist would accomplish on the job. Students are expected to choose their own data set and their own problem, and then work through that from start to finish with the support of their mentor. These capstones are important to showcase a student’s experience to potential employers when students begin their job search. 

Many Springboard students have accomplished amazing projects in the program! For example, a student in the Data Science program was a sustainability consultant and they worked on a capstone project applying data science and machine learning skills to improve hurricane damage assessments. That project was a great way to leverage their own unique background and apply it to a data science role. 

What types of data jobs does the Data Science Career Track prepare students for? 

Most of our students want to become a data scientist after graduation, but we have a substantial amount of students who want to join analytics roles, such as data analyst or business analyst. Data role categories can be fuzzy, which is why it is important to look at the specifics of a role in any employment opportunity to find out if it is a good fit for you. Our students who have previous SQL or software engineering experience sometimes go on to become data engineers after graduation. 

Learning Data Science in 2021

Why is now a great time to get into data science?

Data science is one of the fastest growing fields. Data science as a field is in the very early days, and I expect anyone who is getting into data science today to have an incredibly long and bright future. Data scientists can work on the most interesting and important problems over the next few years and decades. Whether you are interested in tech or health care or finance, every field today is being transformed by data science. That's very rare for any field and really a special opportunity for anyone jumping in now. It's an awesome time to get into data science. Anyone getting into the field will find that there are a number of job postings and salaries are very strong. The most recent job posting I saw was $96,000 a year for base salary. There is a wide range within data job salaries, but zooming out, this is the beginning of a long-term trend. 

What is your advice to incoming students on how to make the most of their remote bootcamp experience?

The best advice I have for incoming students is that your bootcamp experience will be what you make of it. Students who had the best experience took advantage of the entire community and environment built around the curriculum, including the mentors, student community, and alumni community. We really encourage our students to put in the time and energy to connect and collaborate with everyone. This is the beginning of your career, so the best thing you can do for yourself is to leave Springboard with new friends and mentors for your journey. 

Find out more and read Springboard reviews on Course Report. This article was produced by the Course Report team in partnership with Springboard.