Springboard Data Science Review: Pros and Cons

by Kyle Prinsloo      Last updated Jun. 28, 2019

This year I went through the Springboard Data Science Career Track and this article will be a review of how I found it, along with some stats.

 

If you are the type that wants:

• 1-on-1 mentorship
• career coaching
• solid curriculum
• job guarantee

...then you’ll find value in this article.

 

New to data science? Read this article with use-cases.

 

 

Who is Springboard and What Do They Do?

In short, Springboard is one of the top-rated coding bootcamps in the world.

 

10,000+ students have taken their courses by over 300 mentors.

The craziest thing that stands out to me about them, is that they have not needed to refund a student from their Data Science Career Track (out of 1,450+ students) which says a lot about their quality.

They offer various courses in:

• data science
• marketing
• analytics
• coding
• design

 

View full list of courses here →

The Pros + Cons of the Springboard Data Science Career Track

PROS	CONS
You are guaranteed a job	Can be considered expensive
6-8 month study schedule	You need a lot of discipline
Unlimited 1-on-1 mentor support	Minumum knowledge requirements
Develop a professional data science portfolio	Job guarantee only eligible to those in US/Canada
Get a job within 6 months (or refund)
Curriculum aligns with your career goals
Average salary increase of $23,000
0 refunds issued out of 1,450+ students
Flexible payment terms
Active online student community
Unlimited career coach calls
1-on-1 mock interviews

I know it looks 'overly-positive,' but it pretty much sums it up.

Here's some more info on each positive point:

Job Guarantee
Receive a 100% refund if you don’t get a job within 6 months.

Flexible Schedule
Choose between 6-8 months to study (15-36 hours per week).

1-on-1 Mentor Support
Unlimited access to an expert mentor will motivate, encourage, guide and help you achieve your goals.

Expert Curriculum
Their expert curriculum aligns with and is adjusted to meet your career goals.

Career Coach
You work one-on-one with a career coach to develop a job search strategy that will help get you hired.

Employer Network
Access to their sought-after employer network (Facebook, NASA, Amazon, etc.).

Salary Increase
Students have reported a verfied average salary increase of $23,000.

0 Refunds
A mind-blowing statstic of zero refunds issued out of 1,450+ students

Expert Instructors
The instructors and teaching assistants are highly qualified and experienced to help you understand the concepts and apply it in real-life projects.

Top Rated
Voted the best online coding bootcamp by SwitchUp.

 

The Overall Nuggets

Salary offers after completing course:

Job title breakdown of received offers:

Overview:
A 500+ hour expert-curated curriculum, to help you become a professional Data Scientist.

Duration Options:

• 6-8 months (15-36 hours/week)

Nuggets:

• Weekly video calls with your mentor
• Unlimited additional 1:1 mentor support
• Active online student community
• Support from community managers
• Unlimited career coach calls
• Resume and portfolio reviews
• 1-on-1 mock interviews
• Access to our employer network
• 100% money-back guarantee

 

 

VERY highly-rated:

Voted the best online coding bootcamp from Switchup

 

Desired outcomes:

• Data Scientist
• Data Analyst
• Data Engineer
• Business Analyst
• Software Engineer

 

When you complete this bootcamp, you’ll have the skills and tools to leverage statistics and programming to make predictions, optimize outcomes, and help guide business decisions

 

 

Curriculum Breakdown

UNIT 1: GETTING STARTED

• Course overview
• Career support intro
• Capstone projects
• Your portfolio
• Job guarantee

 

 

UNIT 2: WHAT IS DATA SCIENCE?

• Demystifying data science
• Understanding specializations
• Your capstone projects
• Wrap up

 

 

UNIT 3: YOUR JOB SEARCH STRATEGY

• Fundamentals of effective job hunting
• Your LinkedIn profile
• Job search strategy
• Wrap up

 

 

UNIT 4: PYTHON DATA SCIENCE STACK

• Python for data science
• Writing better python code
• Capstone project proposal
• Wrap up

 

 

UNIT 5: DATA WRANGLING

• Data wrangling with pandas
• Working with data in files
• Working with data in databases
• Collecting data from the internet
• Wrap up

 

 

UNIT 6: EFFECTIVE NETWORKING

• Using meetups to build your network
• Using email cold outreach
• Imposter syndrome
• Strategy with career coach
• Wrap up

 

 

UNIT 7: DATA STORYTELLING

• Storytelling resources
• Applying data storytelling
• Wrap up

 

 

UNIT 8: INFERENTIAL STATISTICS

• Foundations of statistical inference
• Influential statistics using python
• Exploratory data analysis projects
• A/B testing
• Applying inferential statistics
• Wrap up

 

 

UNIT 9: INFORMATIONAL INTERVIEWS

• Building your professional relationships
• Conducting informational interviews
• Call with career coach
• Wrap up

 

 

UNIT 10: MACHINE LEARNING

• Linear and logistic regression
• SVM and trees
• Bayesian methods and text data
• Best practices
• Unsupervised learning
• Capstone project
• Wrap up

 

 

UNIT 11: FINDING THE RIGHT COMPANY

• The right job title
• The right companies
• Wrap up

 

 

UNIT 12: SPECIALIZATIONS

• Generalist: Advanced Machine Learning
• Natural Language Processing Track
• Deep Learning Track

 

 

UNIT 13: SOFTWARE ENGINEERING FOR DATA SCIENTISTS

• Writing better code
• Testing and debugging
• Working with production systems
• Wrap up

 

 

UNIT 14: PREPARING FOR AND GETTING INTERVIEWS

• Your data science resume
• LinkedIn profile
• Using your network
• Creating a cover letter
• Wrap up

 

 

UNIT 15: CAPSTONE PROJECT 2

• Reporting on your capstone project

 

 

UNIT 16: DATA SCIENCE AT SCALE

• Advanced data wrangling with large datasets
• Spark and Pyspark
• Wrap up

 

 

UNIT 17: EFFECTIVE INTERVIEWING

• The take-home challenge
• Non-technical interview
• Technical interview
• Mock interviews
• Mastering the negotiation
• Wrap up

 

 

UNIT 18: CONCLUSION

• Capstone project
• Career next steps
• Interesting takeaways

 

 

 

A Few Student Success Stories

I became interested in data science as a means to learn how to develop better answers and knew needed to explore new techniques to get where I wanted to be in my analyses. Coming into the program with a non-IT, limited statistical background, I was surprised that the concepts could be easily understood and applied.

  Aaron Pujanandez, Data Science Advisor

I was looking for a change from my previous company, so that’s when I decided I needed to pivot in my career. One of the reasons I was drawn to data science is that fact that these bootcamps existed because I needed some sort of defined pathway to follow.

  Melanie Hanna, Data Scientist

Some of my favorite things were the open source curriculum, mentor resources, and the freedom in project selection. Working through exercises and lessons produced by multiple institutions or individuals, I think, accelerates the learning by exposing you to different approaches to data science concepts and techniques.

  Ledger West, Senior Data Scientist

With my kids in college now, I wanted to update my technical skills and get back into a full-time tech career. At the time, I knew I wanted to pursue data science. The resources that Springboard offers are amazing it's all laid out and the mentorship and the career counseling, it is a complete package.

  Karen Masterson, Data Analyst

Closing Thoughts

As you can see, the Springboard Data Science Career Track → is definitely a top learning path to consider.

 

The way I see it is, if you can afford it, it’s a no-brainer considering everything in context, plus the job-guarantee with the money-back guarantee.

 

Hope you found value in my review of this great course.

Note: Although I’ve tried my best to represent the information/data in this article as true (as well as receiving Springboard’s approval), it can change at any time on Springboard’s website, so please do confirm everything with them as well.