Springboard

Well-organized intensive online boot camp covering all aspects of the data science life cycle. In addition, materials on advanced topics (NLP, recommender system, Spark, etc.) are also included in the curriculum. The weekly one-on-one coaching call with industry expert is my favorite, providing necessary guidance and keeping me motivated throughout the program. Overall, I had a nice learning journey with Springboard and appreciated all the supports along the way. If you are looking for a good online boot camp with flexible hours at a reasonable price, Springboard Data Science Career Track is a good bet.

I tried many times in the past to learn data science, but work always got in the way. My top 3 ways way that Springboard helps you. They help you focus on what is important to learn so you spend less time on learning useless information. Your mentor is able to guide you when you do not understand certain concepts. And they make you concentrate on career development which is the part I hate doing, but it is the most important. Overall, I am glad that I have taken this course because I feel confident that I have developed a great foundation to start my data science career. The rest is up to me to learn and develop myself to become even better. 

Springboard's project-based, mentor-guided curriculum combined with their helpful and friendly career services and support staff made for a wonderful experience as a student. There is a broad range of skills learned and practiced through projects including statistics, hypothesis testing, data visualization, SQL, and applied machine learning. Through your capstones, you can dive deeper into data science topics that interest you. Since there is a job guarantee, a fair amount is asked of students, including finding additional resources to help complete projects. For me, this meant spending a minimum of 20-25 hours a week, so make sure you are able to carve out time in your day on a consistent basis.

I enrolled in the Data Science career track. I completed my course a couple of weeks back. I opted out of the career assistance because I already had a job which I am not planning to quit any time soon. I completed my course work. I had to interrupt my course twice. Once due to flooding and the other time when I was about to be a father. On both occasions Springboard paused my course and were supportive of me resuming it later. Pros:-
1. Structure: The course is well structured and gives you a good idea of how to progress
2. Exercises: There were a lot of exercises which gave a very good insight about how a data science project progresses. This included basic and intermediate statistical analyses and advanced topics.
3. Projects: Two capstones make you practice a lot. The capstones along with mentor guidance solidified many concepts in my mind.
4. Mentors (Biggest Pro): Having a mentor was the biggest pro. I was matched up to an excellent mentor, an expert in the field and he was extremely patient with me and always pushed me in the right direction. I understand a mentor can be different for different people but if they match you up with a mentor who is helpful, extract every thing from the opportunity. Cons:-
1. I felt that not enough attention was paid to big data and data engineering. Maybe they will fix it in the future.
2. The content was...ok. You can find equally good video materials on YouTube if not better materials. Overall:-
Its a very good program for the amount of money you pay. I am not sure about the job support but based on the quality of my mentors, I am sure that is solid too.

Overall I had a pretty good experience with Springboard. The curriculum was very robust, and I felt like they covered a wide range of topics. I found the homework to take much longer than the suggested time, but the Student Support was very helpful, and gave me specific help and corrections for my work. I got a job a few months after I started the program, so I can't speak much to the job support, but I'm glad I had the opportunity to build a portfolio. The mentor aspect also kept me honest and on task, which was positive. 

I was constantly looking out for a UX course from past 1.5 years or so. I wanted a classroom training but there were limited to no options. I had to settle down for an online course and I can say I made the right choice by opting for Springboard. 
I asked few of the designers to go through the curriculum and give me feedback and everyone suggested that the curriculum was thorough and covered all aspects of the process. I completed the course a few days ago and here is my detailed review :

Pros : 
- The course is self-paced which is exactly what I was looking for. 
- Every student gets a mentor and my mentor was really very helpful in resolving all the issues that I had. 
- The course material covers almost every important aspects written by great authors.
- Additionally, you also get 1 year lynda.com subscription which you can utilize to learn more in-depth about UX processes.
- Very supportive student advisors. 

Cons :
- Mentor calls are usually once a week and sometimes they are busy and cannot get back on time. 
- For countries like India, the fees is a tad bit expensive. 

The last 6 months has been quite the journey and I'm happy to have chosen Springboard to jet-start my data science career. The program covers a lot of ground and includes concepts such as Python programming, SQL databases, inferential statistics, building your resume/cover letter, networking with professionals, machine learning, etc. 

Pros:
- Everything is online so you can learn at your own pace.
- You are assigned a personal mentor who is a data science professional.
- You have access to extra daily mentor calls with one of the other data science mentors.
- The course will help you complete 2 major capstone projects that you will add to your data science portfolio.
- The curriculum is very thorough and presents you with an abundant amount of resources for learning.
- There are many mini-projects along the way that help you practice your learning.
- You can schedule calls with career coaches who will help you prepare for interviewing and beefing up your resume.

Cons:
- Since everything is online, there are no live instructors who give you feedback in-person.
- Getting feedback on your projects may sometimes take a while because the TAs are typically busy.
- Some of the material was confusing, which resulted in difficulty understanding some of the concepts.

Overall, I would give Springboard a rating of 4/5. I definitely recommend it to anybody interested in becoming a data scientist!

The program helped me to better understand what skillsets that are required to become a data scientist. The most helpful resources from the program are from their mentors and career services. The mentors are data scientists or quantitaive positions from the industry, where they can support your personal projects, interview feedback, and questions that benefit your transition in to data science field. The career service is excellent. I finalized my resume and a set of flow for behavior questions from the program. I feel I don't need to worry about my behavior questions in the future anymore since I learn tips and what people like to hear from the interview. 

I also appreciate immediate service from the program everytime I email them, especially thanks to Kane Li, she is super responsive and helpful. I feel to be taken care of and being supported from the program. 

The only cons for the program is overall the materials are easy and are limited in shaping your ability in becoming a "real" data scientist. The only way that enable you to become harnessing in data science field is by doing capstone projects. I learned a lot from the capstone projects. You can choose an easy dataset as the capstone project, yet I think it would be beneficial if you choose dirty dataset to work on, while it is very time consuming but I think that's what people are looking for as a data scientist in the real world. I am still revising and optimizing it even after I graduated from the program. 

In summary, the program prepare you well in basic skillset and directions to become a data analyst/ data scientist. You will have a clear head of how and what to work on to transitioning to this field.