Springboard offers online, flexible, mentor-led courses including the Data Science Career Track with a job guarantee, and UX design workshops. While learning cutting-edge digital skills entirely online, students receive teaching and mentorship from industry experts. Springboard offers a number of self-paced, mentor-led workshops, plus a full Data Science Career Track program designed to place students in data science jobs. The Career Track program has a job guarantee, where students who don’t get hired within six months of graduation, get a 100% tuition refund.
Each Springboard student gets paired with an industry mentor, who works with them one-on-one. Students start with the basics and work their way up to an industry-worthy capstone project they can add to their portfolio, and showcase to potential employers. Throughout the course, students receive support from their industry mentor, as well as Springboard's resident advisors, and student/alumni community.
Springboard also helps graduating students with career advice and job readiness – e.g. portfolio review and interview preparation. Career Track students will have a career coach and do mock interviews with professional data scientists.
Recent Springboard News
- Alumni Spotlight: Sheldon Smickley of Springboard
- Alumni Spotlight: Justin Knight of Springboard
- Meet the Data Science Career Track at Springboard
Recent Springboard Reviews: Rating 4.84
Intermediate Data Science: Python
Data Science Career Track
Get a job, or your money back. Introducing Career Track: An online, mentor-guided bootcamp, designed to get you hired. Enroll in Data Science Career Track, and you’ll get hired within 6 months of graduating, or we’ll refund 100% of your tuition. In this bootcamp, you will master the data science process, from statistics and data wrangling, to advanced topics like machine learning and data storytelling, by working on real projects. With the guidance of your personal mentor and career coaches, you will graduate with an interview-ready portfolio and a network of data scientists. We won’t stop there. We know that career transitions are hard, and we’ll support you every step of the way — until you get hired.
- Lending partner available: Climb Credit.
- Payment Plan
- Minimum Skill Level
- Comfortable programming and comfortable with statistics.
- Placement Test
Introduction to Data Science
Launch your Data Science career with this introductory course. Build a solid foundation in R and start exploring data-related careers with a mentor who is working in the field.
- Payment Plan
Digital Marketing Career Track
If you're looking to get a fulfilling digital marketing job, Springboard’s Digital Marketing Career Track is the perfect course for you. The Digital Marketing Career Track is a 200+ hour online course. You’ll learn core digital marketing skills and work 1-on-1 with an expert digital marketer on projects designed to help you showcase your competency in this rapidly evolving field. You’ll also learn how to leverage these skills in the job market through a career-focused curriculum and personalized career coaching. We’ll offer all the support you need to land a digital marketing job successfully, from resume review, mock interviews, to exclusive employer partnerships. You’ll get a foot in the door people usually work years to gain.
- Lending partner available: Climb Credit
- Payment Plan
- $3300, or $600 a month
- 7-day risk-free rebate period
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I took Data Science Career Track course after I did many mini-courses and read many books. The main issue with data science is that there is no clear start and end points. After a while of taking diffirent courses, you feel that you do not know what is data science career. In Spring board, besides learning more, I also could connect the dots and the field became clear to me.
After finishing Career Track Data Science course, you can continue your journey by yourself.
I took Springboard's Career Track Data Science course, which was exactly what I needed to transition my career. I'm pretty self motivated, which you'll need as the course is self-paced (you can also choose to pay per month which is an extra motivation to complete the course as fast as you can!), but wanted some sort of defined path to teach myself data science. There are a ton of online resources for free but it's pretty much impossible to know where to start without guidance.
The course does a great job of starting you from basics but they move you on pretty quick from there in case you already have a foundation in CS or statistics. They match you with a mentor who can give feedback on your projects or answer any questions you might have about the material or the industry.
The job guarantee is a big reason why I chose this course. It was pretty much a win-win. I started looking for a job about a month from finishing the course and got a job offer in about two months. I absolutely LOVE my new job and what I'm doing now.
There seriously isn't a better deal out there. I recommend this program to anyone who wants to become a data scientist. The ROI here is unbeatable.
I was a student of Springboard's Data Science Career Track between July and November 2017. Completing the track has been one of the most fulfilling experiences of my life. The track was extremely streamlined and sufficiently challenging to keep you involved for the entire duration.
The biggest take away of the program was the mentorship. My mentor, Baran, taught me skills that you will never find in a book or a publicly offered MOOC. He also gave me invaluable glimpses into the world of professional data science. With his constant guidance, I was able to participate in Data Science Hackathons, publish and gather traction for my kernels on Kaggle and develop a proposal for a talk on Inferential Statistics which was accepted in SciPy India 2017.
Another huge benefit of the program is the network you get to build. Springboard mentors and alumni work at amazing places. I connected and interacted with more than a dozen people doing inspiring work. My mentor personally introduced me to Data Scientists at Airbnb (my first capstone project was on Airbnb User Bookings) and Amazon. They, in turn, gave me a sneak peak of their work at their respective companies.
As part of DSC, I completed over 15 technical projects and 2 major Capstone Projects. With the help of my mentor, I was able to channel my work in the right places. My analysis on TED Talks caught the eye of the Kaggle CEO who then submitted my notebook to the official TED Team. My second capstone project on Movie Recommender Systems had me publishing a dataset that was trending at the top for 3 straight days.
The material I learnt from Springboard also helped me in performing reasonably well in Data Science Competitions. My bachelors' thesis project on Fake News Detection is also based on the knowledge I acquired from the program.
Finally, DSC prepares you for the non technical aspects of getting a job. There are intensive, personal evaluations of your LinkedIn Profile, your resume and mock interviews. These exercises prepare you well for evaluating, applying and eventually interviewing for various data science jobs.
All in all, DSC is a great investment if you want to switch careers or get started with the field of Data Science. It prepares you for entry level Data Science jobs and equips you with enough expertise to jump into more advanced material after the program. Highly recommended!
I am in Graphic Design industry for more than 8 years and I was happy to explore UX Design course here on Springboard. The curriculum is very well designed and covers all aspects of UX design. This course is ideal for busy people, who cannot attend scheduled classes on site. Mentors are all professionals from the UX field, and student's advisors are always right away ready to assist with any question.
My 12 weeks experience with springboard was excellent. My weekly mentor calls helped me to find the right way to analyze data and go beyond the actual case study. The learning material is easy to read and understand, not that the course is too easy, but the layout and structure of the course is very good and makes it a pleasure to learn. Catherina from student support is great to deal with. I had one small problem, none of springboards fault, but my first assigned mento wasn't available any longer, so Catherina went beyond her normal hours of work and found me a new mentor over the weekend. I was really impressed with the support she gave me and any other questions, were answered right away, even though we had like 16 hours time difference between Western Australia and San Fran.
A special thanks to Chinmay , my mentor, who was always available and even so we had to change sometime our time of call, he was always flexible and guided me through the course plus gave me some great insights from his past experience and working as BA.
I took the Foundations in Data Science course from Springboard. I have had some introduction to statistics and programming (quite a bit of stats in my MBA program and took many datacamp certificates for R and Python programming).
Really enjoyed the foundations course from Springboard. Have tried using many MOOC`s (massive online open courseware) to learn data science but to no use. Springboard`s mentor driven and community driven coursework is a lot better. Setting up the R Studio environment, Github, writing code on R, R markdown etc, all take quite some effort, if you are from a non computer science background (I am not a CS major but had some coding in high school and undergrad). This is where the mentor and community are most useful.
The $ 500 per month fee is very reasonable for the community, mentor interactions and curriculum that you get in the course.
My advice to prospective students who have no background in statistics and programming is as follows:
1) First learn statistics from online resources like Khan Academy, Saylor.org etc. Topics may include descriptive statistics (mean, median, mode, variance, standard deviation, types of distributions, probability), inferential statistics (p-value, Z-scores, hypothesis test, t-test) and machine learning (linear regression, logistic regression, some simple classification algorithms etc). Even datacamp.com has many good coding courses built around statistics.
2) Learn some R, Python and SQL from datacamp.com. Take a total of 10 certificate courses from datacamp in these 3 programming languages. This will really help develop your coding skills. In R, take introduction to R, intermediate R, dplyr, ggplot, statistics with R etc. In Python, learn numpy, pandas, seaborn etc.
3) After you have finished above 2 points, you can move to Springboard. You may think that Springboard is also about the first 2 points. Yes, it is. But the most important part of Springboard is the capstone project. Once you have completed point 1 and 2, you can enter the Springboard foundations course confidently and spend most of your time thinking and working on your capstone project. Remember that nothing matches the skills you develop while completing an end to end capstone project. This is only possible on Springboard at a very reasonable price.
Lots of effort and motivation + Springboard + Datacamp is the best formula for becoming a quantitative analyst/junior data scientist, if you do not already have a background in statistics and computer science.
I've been a visual designer (mostly web) for about 10 years. I've done aspects of UX in many of my jobs/projects. But there were a lot of best practices in research and prep that I'd never properly learned to be an official UX designer. I learned and practiced all these textbook need-to-knows and quickly got my portfolio updated to prove it through this course in a matter of a few months. I saved $1000's from avoiding full-time courses that I didn't need since I had quite a lot of on the job training already. Since completing the course I've gotten messages weekly to daily from recruiters on LinkedIn looking to place me in new exciting roles at big and small companies. TOTALLY WORTH IT.
My UX project from the curriculum: https://www.jennlindeman.com/ux-project
I took Springboard's UX Design course, and really enjoyed my experience. The curriculum was engaging, varied enough to accommodate many different learning styles, and was dynamic - Springboard updates their curriculum all the time, so nothing feels out of date. The emphasis on working with other students through the online community made it easy to gather ongoing feedback about your process, and simulated a bit of what it's like to work on a UX team - you never work in a bubble ;)
The self-paced nature of the class is a bit of a double-edged sword, because you can finish the course while working a full time job, but it does mean you need to be really self-motivated - I work full time, and finished the course in 3 months. Both your mentor and Springboard advisors are extremely helpful and supportive, and can even provide suggested timelines for what to work on each week in order to finish the course in the time that meets your needs.
As I mentioned earlier, each student is assigned a mentor, with whom you work closely, meeting each week to discuss progress, goals, and other, non-course-related aspects of UX design work. This mentor/student relationship was invaluable to my success. My mentor is extremely talented and charismatic and working with her helped keep me on track.
I highly recommend Springboard to anyone looking for a dive into UX Design.
I would highly recommend the Springboard UX Design course to anyone that is looking to learn UX Design or would like to get a jump start on putting some UX examples on their portfolio. I had worked in UX for about a year but didn't have any personal examples that I could show for on my public portfolio. My Springboard (Sara) was extremely helpful in providing me with feedback and recommending resources that were outside of what the course had listed.
This course was great because as a freelance web designer, my schedule isn't consistent week to week. Since the course is flexible, you are able to do as much or as little of the work each week to make it work for you.
I was left with an amazing portfolio piece on the entire process of UX Design and have been offered multiple positions since completing the course. I also felt extremely comfortable in the interviews as I was confidently able to walk through the ENTIRE UX Design process since I had gone through every piece.
Would highly recommend this to anyone interested in UX!
Very good curriculum. Healthy amounts of machine learning and statistical inference. Statistical inference is very important for data science. It was not a part of the Foundations of Data Science from Springboard course. So, I am happy it was included here. Lots of material is from datacamp. Finishing many datacamp certificates will help you with this course. Good experience with the mentor. As always, comprehensive capstone project helps you utilize everything you learnt in a real world setting. Mentor gave many important inputs for finishing capstone. I definitely recommend this course for anyone trying to become a data scientist.
PS: I did the course fulltime so was able to get into the material in-depth. It maybe a bit challenging to do it part-time. So, if you do part-time and skip some material, make sure to go back to the curriculum and complete it when you have time.
I have just completed the curriculum of Data Science Career Track. I found it very helpful to me to achieve the skill set to become a data scientist. I get to know Data Science from Springboard's Foundation of Data Science course. After that I enrolled in this course. Now I have finished three capstone projects.
The courses are very well organized and the Mentor is very helpful. I get feedbacks about my questions, projects, job search strategies quickly. Because this is a self-motivated and self paced course, it fit my requirment and arrangement well. My Mentor is very encouraging and professional, I think I can't make all the progress and complete the course without his help.
Even though I read a lot about UX design through websites it was difficult to understand how to deal with a full project and that why I subscribed into Springboard.
I highly recommend Springboard to anyone who looking for great UX Design course, the curriculum is comprehensive and you'll have a friendly experienced mentor who will encourage you throughout the course.
Loved participating and Graduating from SpringBoard's UX design course.
I'm a working graphic designer - the way SpringBoard set up their course is for working individuals to continue their education. SpringBoard is unique because of their large online community and mentor's they have available to assist you throughout your course. I was very thankful for my mentor (shout out Sandy!!) because she was challenging and offered amazing constructive feedback.
My experience with springboard offer's an amazing foundation to continue learning about the entire UX platform and now I have an idea of what avenue I will continue with!!
Thank you again, Springboard!
I just completed the UX Design course. I chose Springboard after quite a bit of research into other online certificate programs because you can work at your own pace, which is a big plus for me, and this makes it more affordable if you finish sooner. The mentors were good and the curriculum gives you a good foundation in UX. Also, they contribute quality material to the community at large via things like their "office hours" videos which are free on YouTube.
After completing the course, I have a solid portfolio piece, and enough knowledge to set out on my own looking for jobs and projects. My mentor, Emily Holmes, was great.
Overall quite satisfied.
I recently (June 2017) completed the Springboard UX Design course. It was a great experience! I really enjoyed my weekly mentor call. It was super helpful to get feedback from a professional working in the field.
The projects were fun and allowed me to learn in my own way. I've tried other online courses and this is by far the best UX course I've experienced!
I only have positive things to say about the data science intensive curriculum, my mentor and everyone at Springboard. I have had the best experience and I would do it all again in the future if I had the chance to do something that would boost my skill set. For anyone considering Springboard, the Data Science Intensive curriculum is the best course out there in the field of Data Science and prepares you completely for the industry. No other courses can compare to the quality of this course and the learning it offers. Mentor-led style of learning works best for someone who wants to enter this field and is by far he best way to learn everything about this dynamic field. The support-structure provided my Springboard, right from the student advisor to your mentor, is amazing and Springboard gives it's all for you to go out there and be successful. The perks of the slack community, career resources, office hours, capstone presentation as well as the electives offered in the curriculum make this certification totally worth it and makes it stand out from the rest. Finally, the mentors are some of the most passionate and the most knowledgeable people in the field of Data Science working for the biggest companies in the world, which provides you with an unique opportunity to network and learn from the best.
Springboard was a fantastic course with a multitude of resources and also a step-by-step structure which truly helped you understand the UX process. But where Springboard really shines is the mentorship you receive from your 1-on-1 calls. I would highly recommend this course to anyone as you will take away from it solid skills and a mentor for life!
Look all my money and matched me with a mentor who has a lot of ego, who cares if he works for facebook...
They dont know how to deal with students in a cordial way, I will not recommend this to anyone who is serious about learning Datascience, you can find their course on youtube for free
Response From: Parul Gupta of Springboard
I took the Springboard UX course early in 2017 when I had gotten laid off unexpectedly. I had planned to take it, but it just gave me a bit more time to focus on the course and since it was a pay by the month situation, I figured it was a really good time to dive in. The course itself is quite thorough in a lot of ways. I have a graphic design background, but you really don't need any background to go through it. Since I had read some books, did some reading online and took a few high-level UX courses, it was a bit easier at the start for me than maybe it would be for others. I enjoyed the set your own pace aspect and devoted 4-5 hours a day toward it, but it could easily be worked on part-time and be effective. I do think that the more immersed in the hands-on work you get, the better understanding you'll have. I found the projects and the mentor sessions to be arguably more valuable than some of the articles and videos you watch, but it's partially because I learn better by doing than observing. Overall, I really enjoyed the learning and came out of the course with a good portfolio piece as well as a solid understanding of how to do UX generally. I didn't utilize the job search assistance because I was able to get another graphic design job before I finished the course. I think if you go through the course you might be able to get an entry-level UX job if you did a solid job of thinking about your portfolio as you went through the course, otherwise, expect some work at the end to make your work presentable to prospective employers. I suggest Springboard for anyone who likes to get through a bootcamp fairly quickly and in the comfort of your own home.
For me it was either springboard or GA. I chose Springboard because I was able to get it done for more than 10,000 cheeper. I feel the quality was just as good and it suited my learning style and especially pace cause I'm working another job.
I live in a major metropolitan city so I can make all the connections I need by going to tech meetups and through friends so GA really didn't really sell me on the connection element. And again, I don't feel that I had any less quality of an education and my mentor Fabio Muniz (whom I strongly recommend) was very insightful and just as good or probably better than anyone I could have worked with at GA or elsewhere. And with Springboard during your mentor calls you will have their full attention whereas in a class if there's something really specific you need to get after you can get the full undivided attention without worrying about slowing a class or other students down.
In this case I'm giving the job support a 5 star as it may be not the same as some of the others it's 5 stars for the value. And your mentor will be able to tell you everything you need to know about finding your first career in UX.
Good Luck! And if you have any questions about the program feel free to email me @ whereisjeffrey@gmail
I wanted to start working with UX Design, and Springboard really helped me to know more about the subject. I totally recommend for those who don't know how to start. At the end of the course, I had a big project to use on my portfolio. The price is very good, and even after I finished my course, I still have access to the curriculum.
Our latest on Springboard
Sheldon Smickley is the CEO and Founder of Podible, a podcast discovery platform. After a stint in marketing agencies and solutions engineering, Sheldon actually built the prototype for Podible while attending Springboard’s Online Intermediate Data Science: Python Course! Learn why Sheldon strongly believes in the Springboard mentorship model, see the data science tools he learned throughout the course, and why technical skills have made him a better leader.
What is your pre-course story? How did your background in analytics and solutions engineering lead you to founding a company?
I studied Economics as an undergrad at Rutgers, but I focused pretty heavily on the quant side. I saw a lot of potential in the marketing and advertising space so I worked in analytics for about four years in the agency world. Building my company, Podible, really started during my Springboard capstone project.
Did you go to Springboard knowing that you wanted to start a company?
Nope, I went to Springboard because I wanted to become a data scientist at a high-skill tech company or to become a data engineer. The idea for Podible came about because I’m a pretty big podcast fan, but I couldn’t find new podcasts based on the podcasts I searched for and listened to. There’s also a podcast boom right now and the ecosystem is kind of crazy.
What motivated you to enroll in a data science course?
I really wanted to dive into machine learning and pursue a data science role with heavy analytics. When I was working at an agency, I was writing a lot of scripts. For instance, one of my former agency clients was a large finance company and we did a long report for them that took an analyst three weeks to complete. By writing some scripts in Python and R, I was able to get that report done in three minutes, and we were able to use that time diving into the data and coming up with more useful insights.
That inspired me to how I could use Python to dive even deeper into the data. I saw that it was crucial to have a background in Python and machine learning to get a heavy analytical role at Facebook or WeWork.
Springboard actually wasn't the first coding course that I did. Before I pursued data science, I did Bloc’s Rails bootcamp for software engineering.
While researching data science courses, what stood out to you about Springboard?
I was the total opposite of the Springboard student – that Ph.D. student who wants to switch careers out of academia. Instead, I was the hacker and the doer who tried to figure things out by reading documentation and learning on my own. I looked at courses on Udemy about how to use Python in Pandas, Spark, and their documentation on Python in data science. Those were $10-$15 courses. Then I tried the Coursera data science program, but I actually dropped the class because I found the support was pretty low.
What I wanted - and got - from Springboard was mentorship. I completely believe in the mentorship model – there are definitely individuals who are self-motivated and want to learn on their own but adding a mentor as a resource enables them to get through the learning process.
I also liked the fact that Springboard tested your knowledge in order to be accepted. The application required writing a simple Python loop, writing a little bit of code, and an analytics test. I liked that they curated the candidates that they accepted.
Was it important for you to learn online?
The online aspect was pretty big for me. I actually tried an in-person, expensive coding bootcamp in New York, but it didn't give me the resources that I really wanted. It felt like we were going through a canned curriculum versus being able to go off track and really think – that's how you discover and actually grow versus just learning a lesson. I didn’t finish that bootcamp program because it wasn’t working for me.
At Springboard, the mentorship aspect combined with the online aspect meant I could take the course at the pace I wanted. I also really liked the capstone project because I could work on my own idea which was really exciting to me.
Tell us about your Springboard cohort – were your classmates’ backgrounds diverse and did you get to learn with other students?
It felt very diverse in regard to different ethnicities and genders, and there were people from a lot of nationalities. It felt like most of the other people in my cohort were academia-focused. There were about 25 people in my cohort, but most of my interactions were with Tony, my Springboard mentor. Most of my learning was done with Tony, but I did love the idea of having a dedicated Slack channel and being able to reach out to people if I had questions.
You already knew some Python before Springboard, so could you walk us through what you actually learned at Springboard? Was there a curriculum that you followed?
I previously knew web development with Python, using things like Django & Flask, but I had little experience using scikit-learn and the pandas libraries.
I was impressed with the curriculum they offered. It started with solidifying the basics of statistics and ended with using advanced machine-learning libraries that could be applied in your day-to-day as either an entrepreneur, analyst or data scientist. My favorite part of the curriculum outside of the capstone project was going through the machine-learning section from Harvard’s Data Science 101 course and doing the homework in Jupyter Notebooks.
Okay, tell us about your final project at Springboard, which would become your company today!
I was working with Tony, who was amazing at helping me throughout the entire course. He was really supportive and I could bounce ideas of off him and see if my ideas actually had legs.
I created a podcast search engine during the last month of Springboard’s Data Science course. It was a very basic Flask Python app where I transcribed a podcast from audio to text and then we took those podcast transcriptions, sorted them into specific models like topic model libraries. I searched those libraries and was able to actually see related episodes based on the content that was being discussed in the podcast. Until then, I only found really basic apps that searched the title or description.
What tools did you learn and use at Springboard to create Podible?
During Springboard, the app was actually called Podly, and the tools were very basic. I used Python with a Flask back-end and I used the Gensim library for topic modeling (topic modeling is when you automatically identify topics in text and use that to find hidden patterns). For the transcription side of it, I used a pretty well-known audio-text based transcription project called CMU Sphinx (Carnegie Mellon University Sphinx). Once you compile it you can actually write an audio app in that tool.
Today, the tools we use for Podible have completely changed. We’re now using a Django backend with React / Redux frontend, and for data engineering, we use Spark and Scala. It’s one thing to build a prototype with a few podcasts, but if you’re going to build an application that supports thousands of users simultaneously and transcribing hundreds of thousands of Podcasts, you need to up your tools and bring on experts to your team.
How long did it take you to complete Springboard?
I was working full-time at the time, so it took me the full three months to complete the data science course with Springboard, from the first lesson to the completion of my capstone.
Did you feel like your mentor, Tony, was able to support your entrepreneurial goals?
I felt really supported by my mentor. He had connections at Uber, gave me feedback on my project, and even introduced me to some VC’s that I could bounce ideas off of. I was still testing the waters at that time so I wasn’t truly sure about Podible. It was probably about four or five months after Springboard when I thought, "All right, let's really go all in and pull together a team.”
As the CEO of Podible, how do you spend most of your time? Walk us through your day-to-day and tell us about your team.
We have a full-time team of four people and one part-time person, and we’ve raised some money and now work out of a WeWork.
In regards to my day, 40% of my time is spent on coding, reviewing code, and helping build out the app with my CTO and our other software engineer. Our CTO is much more technical then I am – he went to UOC Berkeley and studied electrical engineering, computer science, and mathematics. He has a stronger traditional background – I completely agree with the theory of hiring people that are better than you.
30% of my time is spent in meetings with VCs, fundraising, and speaking to customers with our Chief Advisor who works in the VC space. He's helping us move in the correct direction because it’s a really competitive landscape and we want to make sure that our unique strategy works.
And the remaining 30% is spent on the marketing and analytics side, working with my Marketing and User Acquisition guy and helping grow our user base.
What skills from Springboard made you a better CEO and founder?
I think the most important skill was learning how to learn. I definitely learned that at Bloc but it was reinforced again at Springboard. When I don't know how to do something, I go and research it heavily, read about the documentation, read what other people are doing, and keep on coding to learn even more.
What's been your biggest challenge to learning data science or applying that to Podible?
I think the biggest thing is reading too much and not getting started right away. I think that's the biggest piece of advice I can give to someone asking for a big take away from this process. People are so focused on heavily preparing for a goal, that they don't ever get started. Whether you're going to go build your company or you're going to go learn data science or how to code – stop researching and actually go and get started. Even if you fail, that's absolutely fine.
I've had a million ideas and this is the first one that I pursued heavily to the point where I quit my full-time job. I have employees who have quit their jobs and are banking on this to work out; that pressure is the best learning experience.
What advice do you have for aspiring entrepreneurs or entrepreneurs who are already working on their business? Do you suggest attending a coding bootcamp?
If you are going to build an app or a product in the tech space, then you need to have some technical understanding. If I take my car to the shop, and I don't have an understanding of how the car actually works, then the mechanic can do whatever he wants. If he wanted, he could cause more harm than good, or give me an outrageous price for something relatively simple.
Alternatively, you could hire someone who you trust a lot as your CTO. My CTO is a former direct report when I was a Solutions Engineer. We have a really good friendship and a good understanding so I trust him with my life. But without any kind of technical background, I wouldn't be able to help build out the app. I don't think someone can just come up with an idea, not have a technical background and then outsource the entire project. That won’t work out well.
Having technical skills is the most invaluable advantage you could have in our current day and age – understanding how code and technology works. I’m not the CEO because I came up with an idea; it’s because I have a vision but can also provide value back to the team at Podible.
After spending years in academic research, Justin Knight wanted the skillset to share interesting insights from data. He dabbled in experimental data-driven artwork, then officially transitioned into the data science industry by attending Springboard’s online data science bootcamp. After honing data science skills like SQL, Spark, and D3.js at Springboard, Justin tells us how his final project helped him land a job as the Principal Data Scientist at Nielsen, where he helps improve business practices for Coca-Cola!
What's your pre-Springboard story?
I have a bachelor's degree in psychology and a Ph.D. in cognitive psychology with an emphasis in cognitive neuroscience from the University of Georgia. I studied EEG (Electroencephalography), which measures brain waves and how it relates to different cognitive processes like memory. I really enjoyed the research, and continued studying EEG in my Postdoc at the University of California, Davis, along with functional magnetic resonance imaging research (fMRI), also relating to human memory.
In my research, I worked with large amounts of data and enjoyed data analysis. After my Postdoc, I went on to do a research assistant professorship at the University of Georgia where I worked with people with schizophrenia and bipolar disorder to understand how their brain rhythms are different from people without those disorders.
Why did you decide to transition out of academia and into Data Science?
I enjoyed all my research, but I wanted to see my research efforts and results having an actionable impact. I was successful at publishing papers and that's where it stopped. Unfortunately, everyday people aren't reading scientific journals.It was a little anticlimactic and made me yearn for something else.
I ended my professorship and explored a hobby doing science-inspired arts. Directly prior to Springboard, I was making digital artwork where I layered drawings, photographs, and mathematically accurate plots, created with simulated data. I wanted to show scientifically meaningful principles through interesting and aesthetically pleasing art. I realized it was more of a hobby and I missed doing hardcore data analysis. That's what drew me back into data science.
I specifically wanted to attend a data science bootcamp to get practical industry skills. I had heard that academia prepares you about 90% for industry science jobs, but you’ll need those additional 10% skills like SQL, Apache Spark, etc. That led me to Springboard.
Did you research other coding bootcamps? What stood out about Springboard?
I found it through searching online for different approaches for transitioning from academia to industry. I did do some research on other bootcamps on Course Report, which I definitely found helpful. I looked into The Data Incubator in New York and Insight Data Science – those are geared towards people that have been in academia and have Ph.D.'s. I also looked at General Assembly.
When making my decision, it was a combination of cost and timing. I was able to start Springboard within a month, whereas Insight and The Data Incubator were several months down the road. Beyond that, the others were way more expensive at around $14,000 for 8-40 weeks, whereas Springboard offered a self-guided pace so you could choose to pay as you go. I finished Springboard faster than the expected six months.
I was able to pay less and also work remotely from Athens, Georgia which was a key part of my choice. The other bootcamps would involve me moving which wasn't feasible for me at the time.
Springboard has a job guarantee. Did that contribute to your decision at all?
Actually, yeah, that did have an impact. I researched the other bootcamps through Course Report and on their own sites, and most of them had pretty high placement rates for students. Having that guarantee was nice.
Was it hard to get into Springboard? What was the application and interview process like?
The application process was fairly challenging. They said about 20% of applicants are accepted to Springboard. I did a basic probability and statistics test to show that I had that basic knowledge. I think Springboard has some folks do a coding challenge as well, depending on your background. Since I already had Python and MATLAB experience, which I used mostly in academia, Springboard felt comfortable with my programming background. There was a video chat with one of the admissions consultants in addition to the test.
How many people were in your online cohort? Did you interact with them at all?
The interactions were more like posts to message boards online. I would imagine there were around 10-20 people in my cohort based on my interactions. Springboard starts a new cohort at the end of each month, so we were able to connect on LinkedIn and build our network.
There were weekly class meetings that you could attend with other cohort members, but because I was based on the East coast, it didn’t align with my schedule. However, since the course was self-paced, I had a regular weekly mentor meeting, which was a great component.
How long did it take you to complete Springboard since it's self-paced? How many hours did you work per week?
It took me four months to complete the course and I actually had some different stoppages along the way. The course is definitely feasible to complete in three months if you're able to devote all of your time to it – and most of the time I was working on it full-time. My goal was to transition into a data science career as soon as possible.
I'd say it was a typical work schedule – 30 to 40 hours a week on average.
How did you stay engaged and motivated while learning online?
That's a very good question and there's definitely a challenge that you face in this type of online environment. I made sure to keep focused on my goal: transitioning into a full-time job as a data scientist. I also picked projects that were of interest to me. Topics that I cared about kept me coming back – even though it did get tedious and technical at times.
In any kind of task like this, there are times where you face shortcomings, or the analysis doesn't work out. That’s a challenge that happens in industry and academia alike. You have to be resilient and know it's just a minor setback – you still learned something. Even when you get an error, you learn what doesn't work so you can then try the next approach.
Tells us about a typical day at Springboard. When you logged on – what happened?
Since Springboard is totally self-paced, you can see the entire curriculum from Day One. You can choose which parts of the curriculum to work on any given day, which is nice. It’s another way to avoid getting burned out. Let's say you chose to do a number of different coding projects and want to take a break from projects – the next day there are videos that you can choose to watch.
Some of the Machine Learning courses came from Harvard's online data science courses. There were also different articles to read about updating your LinkedIn profile and finding a job. They try to intersperse technical videos, coding, and career prep throughout the course. I generally would follow the curriculum, though at times I would definitely jump ahead or pick certain videos to keep my knowledge fresh.
How many instructors or mentors did you work with at Springboard?
I met with probably four to five mentors on a fairly regular basis. We were aware of the other mentors that you don't regularly talk to but have access to.
Tell me about your capstone project at Springboard.
My first capstone project was my favorite and I put in a lot of time into it. You're asked to produce three different capstone project ideas. When you're choosing your project, you write a one-page outline of the data set approach that you will use and ideas for potential clients.
I chose to build an an NFL play-by-play prediction model that predicted the outcome of the next play to help coaches and defensive coordinators make data-informed decisions about what player should be on the field, what play they should call, and how they should line up players. It can also be used in fantasy football where daily fantasy players could have a better idea of which teams, depending on their opponents, will be more heavy in runs or passes in the coming week.
What tools did you use to build your capstone project?
I accessed data from a nice online database that actually pulled from the NFL website and I organized it into a PostgreSQL database across eight different tables. I did multiple pulls to aggregate and merge that data into a Pandas dataframe in Python.
I focused on pass versus run and trained a number of different base algorithms like random forests, support vector machines, neural networks, and gradient boosting, but performance was leveling out around 69% to 70% accuracy. I was able to boost performance another 4% by ensembling eight different base models that were diverse in their predictions. It was something that I was quite proud of. In all of my job interviews, I got amazing feedback on that project.
You came to Springboard with a lot of technical expertise. What were the new technologies that you learned?
I was new to working with real-world data and showing off my skills – I learned how approaches in academia translated to real-world problems.
Learning SQL programming and accessing different SQL databases was something that I gained new expertise in. Also, I used tools like Apache Spark to do more distributed big data processing in memory across different computers. Doing some more interactive data visualizations with Bokeh and D3.js were other things that I hadn't done before.
Did Springboard help you find your new job?
Springboard helped with career prep and the job search where we were exploring different companies I'd be interested in. We also did mock interviews, technical interviews, and take-home coding exercises. They put me in contact with a few different employers. They didn't specifically connect me with my new job at Nielsen, but the training definitely helped. I connected with Nielsen over LinkedIn and had several interviews which were ultimately successful.
What is your role at Nielsen? Is the job what you expected so far?
I started about three weeks ago and I'm still getting access to things, but it's definitely been a positive experience. I feel better prepared and confident in this role given my training and experience through Springboard.
I am the Principal Data Scientist at Nielsen in Atlanta. I work about four to five days a week onsite at Coca-Cola where I am building different statistical and machine learning models to understand Coke's sales data and some of their customer surveys to better understand the predictive variables impacting sales and shipments.
My other role at Nielsen involves merging data from their different surveys and sales data that they get from retailers which is more in-depth than Coke’s data. Coke is tracking their products whereas Nielsen is tracking everything that goes out of supermarkets, stores, Walmarts, and things like that across the country. So we're looking to merge that data and build better-informed predictive models of consumer purchase behavior.
Did Springboard help you communicate business insights to clients?
That was definitely touched upon. Besides the main projects that I worked on, there were also about 15 different projects. Once built, you had to give recommendations to the client. So that was definitely a skill that was encouraged.
Also, my past experience in academia of writing papers, then selling my research to journals and to reviewers, definitely helped. Science is about telling stories from data so it’s helpful to know how to take your analysis and programming to a level where it's understandable to others.
How was your first month in your role at Nielsen?
I jumped right in. I work on a three-person team that has a bit more business experience than I do – it’s nice to pair with my technical experience in analytics.
We're paired with a 4-person data science team at Coke. I've had regular meetings to get an understanding of what kind of analysis and modeling they've done so far and brainstorm new approaches to build more robust models to gain a better understanding of the data. Staying in frequent contact, updating one another on the progress of projects, making different project plan metrics, assessing performance, and making sure we're on the right track.
Looking back on the last few years and your career change from academia to industry, do you think that you would’ve been able to make this change on your own without Springboard?
That is a good question. It is possible, but I'm convinced that it wouldn't be an easy transition if I were still in my Postdoc. That period where I worked as a freelance artist also added to my need to do a data science bootcamp.
I know a number of former Postdocs who did data science bootcamps straight out of their Postdoc. Some good friends of mine are actually considering making the jump and doing a data science bootcamp as well. Even though you're technically proficient and learning a lot of the same skills, there's still that concern that you haven't been in the industry. Employers want to see you work on certain types of problems with certain tools to be more comfortable in hiring. I definitely got way more responses from employers after my experience at Springboard.
What advice do you have for others who are thinking about making a career change and attending a data science bootcamp?
One – be clear on what your goals are and have that picture in your mind because there are going to be rejections along the way. I certainly didn't get an interview from all the job applications that I sent. You have to be resilient, persistent, and push through any setbacks that you may have. Even in your data science bootcamp work and the projects you do, you need to keep your head down and push through. Pick ideas, topics, and projects that are of interest to you to keep yourself engaged.
Two - keep in mind that it's a long process. Consider DJ Patel, the first Chief Data Scientist of the United States – it took him six months to get his first data scientist job.I hear many stories about people transitioning from academia to industry, and they expect that it will take six to nine-months to land a job. I fell right into that six-month mark myself. Keep your head down – it's a long road but if you put in the work, it'll definitely pay off.
Springboard recently launched their new Data Science Career Track, an online, mentor-driven course that promises graduates a job in the field or their tuition back! We chat with the Director of Data Science Education, Raj, to learn why he’s passionate about helping students make career changes, why their curriculum focuses on Python, and exactly how Springboard’s students are landing jobs when they graduate (hint: it’s not by blasting out resumes).
Tell us how you are involved with Springboard’s new Data Science Career Track.
I’m the Director of Data Science Education at Springboard, which means that my job is to create and maintain our data science curriculum, including launching new courses like our Data Science Career Track.
Why are you passionate about helping career changers become data scientists?
I actually changed careers through online education. I have a Master's and Ph.D. in Computer Science from Rice University. However, after a couple of years of working in the industry as a developer, I wanted to transition to data science. I tried out a data science class on Coursera, and then spent two years teaching myself through online classes at Stanford with their Continuing Education department, and then changed careers to data science. Before Springboard, I was Chief Data Scientist at a very prestigious startup in Atlanta called Pindrop Security. I had been mentoring for Springboard’s Python course, and when this opportunity came up to take over the data science curriculum, I jumped on it.
I’ve been through that career change using online education, so helping others and encouraging them to switch careers and upskill is something that I’m really passionate about. Working with Springboard is a way to have that kind of impact on a bigger scale.
What’s the difference between past Springboard courses and the new Data Science Career Track?
We teach three other Data Science courses: Foundations of Data Science (which is based in R), Data Science Advanced (which is based in Python), and Data Analytics for Business (which is based in Tableau and business case studies).
The Data Science Career Track is our first foray into providing career services. We've hired a full-time career services lead to take over the career aspects of the course, and I’m maintaining the technical curriculum.
What are the admissions requirements for the Data Science Career Track? Can someone start as a total beginner?
Because we offer a job guarantee for the Career Track, we’ve found that having some technical background does help students get jobs. So our admissions process involves a programming challenge and a statistics challenge.
For total beginners, we recommend one of our Foundation courses depending on their background. If they have no tech background whatsoever, then we typically recommend Data Analytics for Business. If they have a little bit of programming background or technical background, then we tend to recommend Foundations of Data science.
Will the coding challenge be in a specific language?
An applicant can choose their language, but the most common are Python, R, and Java.
Can you tell us a bit more about the Job Guarantee? What are the conditions of the job guarantee and why is it important to you at Springboard?
Roughly, our job guarantee means that students who complete all of our curriculum, including all the projects and follow the guidance of their career coach, and meet eligibility criteria are guaranteed a job within 6 months of completion. Eligibility criteria include willingness to live and work in one of 11 major US metropolitan areas, US work authorization, a Bachelor’s degree, and be age 18 or older. In addition, we require that to be eligible for the guarantee students are active in their job search and committed to their own professional success in that area. We have full confidence that if our students commit to the learning in the program, which includes both technical material and job search tasks, they will be successful in meeting their career goals.
Which data science languages have you incorporated into the curriculum?
We've decided to focus on Python. However, we don't really teach Python in this course; we assume that you know the basics of Python. We teach the Python data science stack, which starts with Pandas (a Python library to manipulate data, clean data, wrangle data), and then we teach Python libraries like NumPy, SciPy, scikit-learn for machine learning, Seaborn and Bokeh for visualization. In addition, we also cover Spark, which is one of the most in-demand tools for data engineering and scaling, along with PySpark (a Python interface to Spark) and MLlib, which is Spark’s machine learning toolkit.
Both R and Python are pretty common in data science, but if you're working as a data scientist, particularly if you're working on building machine learning algorithm prototypes, knowing Python is a huge advantage. You’ll find that R tends to be less connected to production systems, so we made a conscious decision to go with Python.
Did employer needs and feedback go into the curriculum design?
Yes. Many of our Data Science Intensive students were interviewing with employers, and we got feedback from those interviews. We had a sense of the gaps between our Data Science Intensive course and what employers were looking for. The Data Science Intensive was getting students 70% of where they need to be in terms of technical skills to find a job. So what was the remaining 30%? Employers said they wanted more experience with real world data sets and portfolios, and they also suggested we work on interviewing skills and job searching.
As a result, we weave career steps throughout our technical curriculum, so students are building their network, working on their LinkedIn profiles, and coming up with their pitch from Week One. Towards the end, when they're done with the technical curriculum, students can set up mock interviews. Some of our mentors have been interviewing candidates for many, many years, and they will give you feedback according to a preset rubric so that you get all the practice that you need for interviewing.
If you wait to finish the technical curriculum and then start your job search process, that's just going to cause a lot of delays.
What is the teaching style like at Springboard? What should students expect?
Our teaching model is completely online and self-paced, so students go at their own speed. First, you’re assigned a mentor, typically someone who currently works as a data scientist in the industry and has worked for a few years. They have not only data science experience, they also have the sense of what industry careers in data science are like.
Students work on the material in the curriculum at their own pace and the material is curated, which means that we collect the best content we can find on a specific topic. Then we assign mini-projects for each topic where students actually work on a realistic problem, and that's the way they learn each specific topic.
Throughout the course, they work on two capstone projects. One can be a little bit more foundational, the other might be more advanced. The capstone project should be as realistic as possible as you should use some kind of real-world data set, and the question that students choose to answer should have some real world value. Students need to write a proposal where they state the question, why they care about it, the value of the answer, and who the client is. In the real world, when you're working as a data scientist in industry, you're never working on a problem in isolation. You are typically working to prevent or solve a problem for a business client.
Knowing how to translate a business problem into a data problem and then communicating the results of your analysis back into a business context is a super important skill for data scientists. It’s highly underrated and something that employers always look for. The way we teach that skill at Springboard is by making sure that every capstone project they're working on has the client in mind. The analysis and deliverables should all be targeted for that client.
How do you keep students engaged while they're learning online?
That's a really good question, and this is something that many online education providers are trying to figure out. Assigning mentors is a big part of this for us, because it means that students are being held accountable. Students meet with their mentors once a week, online as we’ve built video calling into our platform. In their weekly calls, we encourage students and mentors to decide on goals for the following week.
Student advisors will also follow up to check in on students’ progress. If you haven’t made some progress over the last couple of weeks, the student advisor will reach out – that kind of human touch often helps many students. When students accomplish specific milestones, they have prompts to set up calls with their advisors. For example, once they update their LinkedIn profile, they have a call with a career adviser who will review their LinkedIn profile and give them feedback.
We’re also always thinking about how to better design our platform and curriculum to motivate students. For example, a lot of students are motivated by seeing their progress as they go through the curriculum, so we built those rewards into the platform. Student do well when they’re aware of their own learning style because they can work with their mentors and their student advisor to make sure we’re motivating them in the right way.
What have you found is the easiest way to land a job as a data scientist?
When you look for a job, especially in tech and data science, you often get the advice that you need to pump your resume full of keywords and then blast it out as widely as possible. That's really not the most effective way to find a job. Referrals are the way to find jobs in tech, and that means building out your network, and then using your network to find jobs, interviews, or referrals to companies that you've already done information gathering and research on.
Students need to be very strategic in the beginning before they send out a single resume. And we’ve built that idea into our career curriculum. For example, students may be required to find a major data science meetup near them, attend, and make five contacts, take five people out for coffee, or schedule an informational phone interview to learn about their company.
One of our students put his data science skills to the test and ran an experiment where he sent out hundreds of resumes to different job sites, and got an acknowledgement ~10% of the time. When he submitted applications through referrals, he got a phone interview 85-90% of the time.
The next class starts May 29th; how are the current students doing?
We’re teaching a couple of hundred students right now, and we have about 50 mentors in our network. Some of those students are getting close to graduation, and then will be focused on finding a job. We accept applications on a rolling basis: however, admissions are quite selective, with about only 18% of students enrolling after they’ve applied. Click here to see if you qualify!
Great, we can’t wait to talk to a graduate!
Welcome to the January 2017 Course Report monthly coding bootcamp news roundup! Each month, we look at all the happenings from the coding bootcamp world from new bootcamps to fundraising announcements, to interesting trends. This month we applaud initiatives that bring technology to underserved communities, we look at employment trends, and new coding schools and campuses. Plus, we hear a funny story about an honest taxi driver. Read below or listen to our latest Coding Bootcamp News Roundup Podcast.Continue Reading →
Kristoffer has been a graphic designer for six years, but after trying out a few UI projects, he realized he liked it better than his current work. Not wanting to quit his job, Kristoffer decided to enroll in Springboard’s part-time online UX Design program to upskill and pivot towards something he was more passionate about. Kristoffer tells us how he managed to squeeze the whole program into one month, how he balanced it with his other commitments, and his plans for the future. He also shares his screen to show us Springboard’s online learning platform!
What was your background before you decided to study UX design at Springboard?
I went to school to be a graphic designer, and I've done that in a professional capacity for about six years now. Through that, I've done UI projects at work, and that is really where I thought, "Oh, I want to pivot into that and stop doing graphic design." It led me to where I want to be, and pushed me into taking an online course to further flesh that out.
Are you studying part-time or full-time, and are you able to work as well? What's your setup for learning?
I'm finished with the course now, but when I did it, I did it part-time, but I really focused on it. Thankfully my job was flexible enough that I had extra PTO, so towards the end I was able to take a week off and just focus on the course.
Other than that, I would do a little bit after work and then more at night after dinner. Instead of watching TV, I would work on the course and take care of what I could that night and then move on the next day. It was really flexible for me.
How long did it take you in total to do the whole course?
It took me a month, but that was like a marathon run for me. I had committed to only doing it for a month, so I had it in my head that I needed to really focus. I work better that way because it is a monthly thing and you can go at your own pace. I could've easily mentally just stretched it out longer or just say "No, I'll get to it tomorrow."
Knowing that I only wanted to do it for a month helped force myself to just do it as quickly as I could and to get as much out of it as I could. If I stretched it out any longer, I feel like in my own learning I would have lost some of it because it would’ve just taken too long. By focusing on it for just one month, I was able to really take it all in and get what I needed out of it.
What made you decide that you needed to do a bootcamp rather than learn on your own through another online-type of resource?
I had a deep background in the visual design side of UI and UX, but I only had very tangential knowledge of the user persona creation, user testing, and wireframing. I hadn't really touched a ton of that. So when I was reading up on different courses, Springboard stuck out to me because I could learn all of the stuff that I either hadn't touched at all, or barely touched.
In terms of my timeline and keeping that in mind, I was thought, "Okay, well my final project is going to rely heavily on what I know already. So I know that if in the first two weeks I can get the first book done, then the last two weeks will be easy for me because I already know all of the programs that I need to complete the project.”
Did you look at a few other bootcamps as well as Springboard? What made you settle on an online bootcamp in particular?
We didn't have a ton of options out here in Las Vegas and I had to keep my job so I couldn't really go anywhere for three months to do an intensive course. So I knew I had to stay online. I did research quite a few, and they all sounded wonderful, but a lot of it was either not going to be fast enough, or it was more of "This is a three-month program." I needed something that I could basically do it as fast or slow as I wanted, and that's where Springboard came in handy.
What was the application process like when you were applying?
I think they open it up to everybody who is willing, but I think if you don't have much of a background in it, they will tell you that you’ll need to take your time on each course. For me, I remember I had to fill in an application saying why I wanted to do it, and if I did have any experience, what that was. I put my background in and I linked it to my LinkedIn account. Springboard basically looked at my resume and said, "Oh, okay. He's done this, this, and this. He's good to go."
What actual technologies and subjects does the UX design program cover?
It covered idea creation, minimum viable product, competitive analysis, user persona creation, wireframing, visual design, logo design, and color palettes.
Did they cover any front end programming languages? Did you cover HTML and CSS?
We didn't cover that. I have some knowledge of that just through my work experience, but Springboard didn't cover that in a classroom setting. I think the culmination of your projects would rely on high fidelity mockups, and then some interactive prototypes using invision or something like that, but no HTML was not really gone over. It was mentioned, so it wasn't like it was hidden, but we didn't go over it.
What was the actual learning experience like at Springboard? Did you watch recorded lectures or did you have one-on-one time with a mentor? How does that work?
It's a wonderful blending of both. You get a phone call with your mentor once a week and you can also email them. They're usually pretty open to email, and they're flexible on calls too. For the rest of the learning, it's a combination of PDFs and links that you read through and then go over.
There’s also Lynda.com videos and Skillshare videos, which you don't have to pay for because they are part of the course. Once you get through those, you've got some projects to work through. Each chapter has a project and then at the end you have a culmination of those learnings in a capstone.
How often were you meeting with your mentor? Since you wanted to do it in such a short time, was the mentor able to accommodate that?
Yes, he was very accommodating. I told him at the outset that I was planning to do this in one month. I knew it sounded crazy, but I had looked through the course and when I talked to him I just reinforced that "I'm going to need to do this only for one month,” and he was really flexible. We talked once a week at the beginning and then we talked maybe two extra times at the end because he knew I needed to get things done before a certain date.
Would you like to share your screen now and give me a little demo of what the learning portal looks like?
So here is my back end when I log in and then right here is all of the chapters. You read through all the information and this is your intro when you first sign up. Then as you go through, each of these activities would not be grayed out, and you would just click through to complete them. Once you're done it says completed.
Did you have a checklist where you could see which activity you've finished and which ones you still had left to do?
Yeah. I wrote down on a notepad what I knew I had left so I could extract stuff out. But when you're in the midst of going through the course, it defaults you back to basically where you left off. So it knows what you have completed and then it brings you to what's up next so that you don't have to scroll through every time.
Could you submit your projects or assignments through the portal? How did that work?
Let me find one that has a project. So when I got to this portion of the course, it was not grayed and then instead of "Submitted," the button said "Submit project." When you click that a little box comes up for a link and you just paste the link in there to where you have your project hosted. I used Google Docs for 99% of what I did.
What kind of programs did you use to actually build and create your projects?
For the initial parts where I was submitting ideas and chart based stuff, I did Google Docs and Google Sheets. Then as we got into the more visual side of things, I used Extensio and Balsamiq. Balsamiq was for wireframing, and Extensio lets you build user personas that look really nice. I can show you an example of that if you want.
Yeah, that would be cool if you can show me an example.
So Extensio lets you build something that’s a nice visual, quick overview of a persona that you create. They also have templates in there that I used, for example they let you do empathy maps.
This is my case study that I did for my final my capstone project. I used Illustrator to make my competitive analysis because I wanted it to be super simple. I don't like the way that normal spreadsheets tend to look even once you adjust for cell sizes. It was a fairly fast project.
What was your capstone project?
For my capstone I designed an app that allows users to catalog and keep track of items that they have in their home or apartment for insurance purposes. So in case of fire or water damage, or even robbery, you have a list of your items that you could submit to insurance for reimbursement checks.
Was that an original idea you came up with yourself?
Overall, when using the Springboard platform, how did you find it was different from using some of the free self-guided online resources that are out there?
For me it was that old feeling of when you pay for it, you feel like you need to get the value out of it. So if I was just looking at YouTube videos, I could go down a YouTube hole and end up not learning what I wanted to learn. I could just be clicking and then watching the next step in playlist. This guided me down what I know I needed to learn.
With YouTube or other online resources, I would feel like, "I don't like the way that they're teaching these so I'm just going to move on to something else," and eventually that did not work for me. So for Springboard it was a little bit of hand-holding. It gave me the steps I needed to take to fully do user experience research for a new project or a new feature and I liked that hand holding.
I also took online classes when I was in college because some of them were only offered online, and this was so much better than that.
How many hours per week did you find yourself spending on the Springboard curriculum?
Early on I broke it into two separate sets of two weeks. The first two weeks was the main lead up, which was everything up to the wireframing. It was the MVP persona, the competitive analysis, all of that. For those two weeks I probably spent around 10 to 12 hours a week on the course. The third week I was off work, so I was able to put in basically 30 to 40 hours. Then the final week I was back at work but most of my stuff was done. I was just refining my capstone project with input from my mentor and users that had tested it. So that week I probably put in maybe 12 to 15 hours.
How does Springboard help you or give you advice about how you can use this knowledge in your future career?
Throughout the course, they'll mention various Lynda courses, and explain how this will apply in an office setting. They'll say, “you're learning user research where you're having them test it in a room with you and cart sorting and stuff, but it’s not necessarily how everything will go.” Some companies are so large that you will never touch that aspect of it.
It's good for you to know it so that you can talk with those people and understand the data that they're giving you and how it influences what you do. Towards the end, they start explaining, "Here's how you set up a UX resume and here are the programs you need to know.” If you're going to focus more on UI and visual design, you'll want to know Photoshop and Sketch and Illustrator. If you know Sketch, you probably don't need Illustrator. Springboard does say, "Know the Adobe Suite, know Sketch and you'll be set in terms of visual." For the others, it's a lot of Word Docs or Google Docs; anything that you can have a full office suite of spreadsheets, PowerPoints and Word processing.
Did they offer any job placement help if you're wanting to find a job using your new skills?
I think they help you with links in terms of good search engines to use for this particular field. Your mentor can be pretty helpful in that regard too. Even if it isn't necessarily finding you a job, he can help look over your resume, look over your portfolio, make sure that you're hitting the things that need to be talked about.
What was your goal when you decided to go through this program? Were you planning to get a new job or did you want to upskill for your current job?
It was definitely to get a new job. I've been in the same job and the same skill set for about four years now and felt, "It's time for a switch." At the same time, I was realizing how much I enjoyed the UI side, because I had some freelance projects that I was working on that were UI focused. I thought, "This is so much more fun than what I'm doing right now.”
I wanted to pivot and move into a startup role. So I'm currently looking and interviewing, and this is already helping. It reinforces the fact that I do have experience in some of this stuff. Having the course behind that, people see, "Oh, okay he's serious about it."
So what are the types of roles that you're looking for that you would ideally like to get?
I would love to get a UI design role or a visual design role. I still love that aspect of it. I love playing in Photoshop, playing sketch, and doing interactive mockups. I enjoy all of those parts of it building buttons and figuring out how it should look for the end user. That's really where I've concentrated my search. I've had a few interviews for UX based stuff – less on the design side, more on the how it's going to flow side. It's been incredibly informing, but I can see I'd much rather go into UI visual.
What advice do you have for someone who is considering doing an online bootcamp like the one you did? Any tips you might have for staying motivated and engaged?
I think the first tip I have is if you feel like one of the course videos is going to a little too slow, usually somewhere in the settings on the video player there's a way to speed it up and that helps a lot. Because some of them had very intensive talking. It was deliberate talking. So I believed, "Okay, I can speed that up to one and a half times the speed and get done with this quicker,” and it would still be fast enough that I could get through it; but not too fast where I didn't get anything from it.
On top of that, I think you should know how long you want to be in the program, even if it's not a month, just know how long you want to be in it and make sure you work towards that. Don't let it become something that you let just fall off. Focus on it and do it, because even if you don't end up using it in your career, you will at some point. Even if you don't go into UX design immediately, you will use what you learn I think at some point.
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