Springboard

The main positives to take from the course are the interactions with the mentor and the interview preps. Now I had a very hesitant mindset about applying for a bootcamp wondering whether I would be able to get employment considering I do not have a computer science degree or a STEM degree. I have a political science degree. I was told that these things do not matter and that you can easily land a job after you get both your projects done. I had done very detailed projects and posted them on github and did everything the career coaches suggested I do. Unfortunately not a single data science position had reached out to me. 

Now for the curriculum. This was sadly most dissappointing. The course mainly revolves around data camp courses. And then these are supplemented by youtube lectures from Harvard or other institutions that would talk about the subject with the impression that you have intermediate knowledge of the subject matter, despite it being a beginners intro video to a concept. I have had much better success with the self study resources available on the internet, such as youtube tutorials etc. Also did two Udemy courses for 11 dollars each and got a better understanding of the subjects. There was no feel of an actual boot camp teaching you, they basically link you to external sources that arent theirs. 

I enrolled in the Springboard Data Science Career Track on December of 17, completed April 19. Due to life circumstances, I had to freeze and extend my completion date during the latter half of 2018. When I returned, I made sure I kept the same mentor as that was an absolutely highlight of this experience. I was working full-time while enrolled.

Pro:
Mentor relationship

Con:
Depth of coursework

My mentor was simply superb, this was in my opinion the best feature from this program that differs itself from others. Having a professional data scientist with working experience to offer insight concurrently while the course progresses enhanced the learning experience, I was able to bounce questions and solicit feedback both from course content and my perfornce freely and openly on a weekly basis. This has helped fuel my growth tremendously.

Although, in my opinion, I wish there was stronger emphasis and more depth offered on the machine learning portion of the coursework. For those who are curious, the way Springboard structures their coursework follows the format of data science work flow: Familiarization with programming -> Data Wrangling -> Exploratory Data Analysis/Visualization -> Statistical Inferences -> Modeling/Machine Learning -> Delivery of Results/Implementation. This flow offers a very good broad perspectives on what a Data Scientist does, but I was left craving a bit more material on the depth of Machine Learning models, such as the math and science behind some of the methodologies and the tools used.

In terms of Job Assistance, I thought the career advisors I worked with provided good feedback. However, I thought a lot of the resume and cover letters support are readily available online already, and that the job opportunities within Springboard I noticed are more relevant for students in the SF Bay Area and NYC. For those not living in the two areas, the process will not be noticably easier.

I was completing my PhD in geophysics and I started my online course on Data Science with Springboard. One of the things I really liked the curriculum it stated with the basics and then went difficult. The curriculum helped me to understand the bigger picture of Data Science. I really appreciate the mentoring part of the program. It really helped me to get a good understanding of various topics in Data science. Without mentoring, I would not be confident in communicating with the Data science group. I shared my articles in Medium and LinkedIn and got acknowledged by the Data science group. I am still working with the career service to land a job as a Data Scientist. I can honestly say that I would not be able to make a transition from geophysicist to data scientist so fast without Springboard. Also special mention to the staff, incredibly responsive and helpful.

Before I started this course, I didn't know how to code, and I didn't truly understand what data science was.  But with the help of my mentor, the curriculum, and the projects in the course I know have valuable skills and a deeper understanding on how to analyze data.

This course is perfect for setting up your own pace. Since I had 0 experience coding, I was not pressured to cover it in a pace that I wasn't comfortable with. In contrast, since I did have a background in statistics I was able to quickly go though all of that material without being forced to work at a pace that didn't suit me.

This is an entry level boot camp program designed for students with limited background in data analytics.

Pros: Weekly mentor calls is the main selling point, I enjoyed chatting with my mentor, but you need to be well prepared with agenda items, otherwise 30 minutes can pass so fast with no solid progression made. The mini projects in Jupytor notebooks are all very interesting and insightful, I think I've learnt more from those than videos and quiz. While I can see the trace that those are probably not what they originated, they did make modifications to those projects so it ended to be a good summary of each section. 

Cons: I'm not a big fan for a student proposed capstone project. I can easily imagine someone getting stuck at thinking about valuable business ideas and reliable data source to begin with their project, especially for a program facing inexperienced students. The program course material is mainly from Datacamp, there is nothing wrong with that business collaboration, but you won't feel happy realizing Datacamp subscription is only $29 per month. I convinced myself I'm mainly paying for the tutoring, and this could be subjective people believe it worth the price or not. Not much job support if you are not choosing the career track.

One suggestion I'd like to make is to alter a little big the tempo of the program. Currently I can strong feel a jump of level of difficulty moving from statistical inference section to machine learning, and I consider myself half-experienced for those topics. If this is supposed to be a program for people start thinking about exploring their career at data analytics, I think you can simplify on machine learning topics and make the mini project as easy as what datacamp offered. If you believe this is a hard-core data science program, you'd like students to put more hours into the topic.

This was an excellent program to gain exposure to many different areas in the data science process. I had excellent support from my mentor and the career resources during the program, and am confident this will continue throughout the job search. The curriculum is very good, although some issues are slightly present due to pacing but are relatively minor. There is a lot of work which will need to be done in the program over a long period of time but if you are willing to put the effort in it will certainly be worth it. 

The introduction to UX Design takes students on a comprehensive journey into the field. The flow of the curriculum demonstrates the progression of the UX workflow, and gives students not only the ability to gain the skills and understanding of this process, but leaves them with the work to prove it. I'm happy to have a portfolio completed and ready to present to employers. This was an extremely helpful course for a complete beginner, and I'm confident that it will help me to changes careers into the UX field.