Data Science Career Track Reviews

PROS: Totally online, great curriculum, expert mentorship, really helpful career coaching, and helpful events.CONS: Totally online, so the human element of learning and socializing while gaining skills isn’t there so much. It happens through Skype / Zoom / Slack, but if you want to have informal random chats with peers or mentors as you pass by them and everyone’s definitely at the same part of the curriculum, that won’t happen here.

While the technical data science curriculum with Springboard is outstanding by combining Datacamp with dozens of hand-selected resources online, there are endless resources online for learning the technical aspects of data science and machine learning. If you just want to learn the technical side of data science, just get a Datacamp membership.However, if you want to take your professional career to the next level, join Springboard. Weekly mentor calls hold you accountable with the curriculum, and the career coaches guide you through networking, polishing the LinkedIn profile, building a focused resume, and writing cover letters. Springboard is not the least expensive bootcamp out there, but the networking, mentoring, and career coaching resources more than make up for the cost. It's an incredible value given the opportunities for salary growth afterwards. 

 Pros: Datacamp resource - I really liked in-browser coding paired with short lecture videos to be very effective to get practice with the key parts of data science problem solving. There were lots of practice exercises that helped give me a better understanding of the concepts. Mentorship - I really liked my mentor, he was very experienced and had lots of industry knowledge, and it was obvious he had a strong grasp of the subject matter. As a part of the curriculum you work on 2 Capstone projects, and several other ad-hoc projects with the guidance of your mentor. My mentor provided helpful feedback and advice for lot of the machine learning methods I tried and gave me a starting point to work on the project. Cons: Many parts of Springboard curriculum is just reading articles on data science or youtube recordings which can be find online. The video/audio quality was not the best. I think it would have been much better for Springboard to make some videos covering perhaps similar material but improve audio and video quality and create a more cohesive curriculum instead of many seemingly unrelated videos. Also, none of the assignments have answer keys provided to students, so there are not many ways to check your work. The machine learning unit's videos and assignments are almost entirely taken from a college class. The ad hoc projects were useful to get practical examples to refer to, but a few lectures from the class did not feel adequately sufficient to develop a good understanding of machine learning concepts and AI techniques. 

There is a lot of material online that teaches data science concepts such as machine learning and deep learning. Anyone capable of completing an online boot camp could probably successfully learn on their own. Springboard gives benefits beyond the actual curriculum. There are no concerns about the curriculum - they curate online sources so that you don't have to worry about assessing quality. While there were occasionally problems with dead links, these always seemed to be fixed relatively quickly. Springboard was the strongest in career coaching. I'm confident in my technical skills, but have always felt uneasy while networking. Springboard's coaches offered plenty of assistance in that regard. I have no regrets about taking this course.

Initially when I started researching boot camps and talking with friends, I wasn’t sure which career track is best for me till I found springboard. My decision to join Springboard was strictly based on how challenging it was, their one on one mentoring program and career coaches that are very unapologetically invested in fellows’s success.I started as a prep student back in November 2020 and was admitted into the data science career track in January after I passed the exam. I finished the career track in July and got my certificate on August 4 2021. The entire journey was challenging but it built me and made me who I am today both in terms of attitude and data science.I went really hard on myself because I was admitted into their job guarantee program and I didn’t want my money back, I wanted a better career that will allow me to do what I want. I took multiple breaks throughout the course to have fun and also raise money to pay for it. Springboard was understanding and worked with me every step of the way. My mentors offered excellent guidance and resources every week that helped me be successful. My career coaches helped me build confidence and learned how to network better which landed me a job a month after my graduation. Springboard built me, it took my terrible coding skills and transformed it and made me competitive in the job market. People say I am crazy because when I do something I do it with all my mind. I went all in for Springboard and the result was clear. You get what you put in. If you can’t challenge yourself or want to be challenged, I’m my opinion Springboard isn’t for you. Their job guarantee works  as I am an example. I built my first face recognition system in the fourth month of springboard, and 2+ Personal projects where I performed data analysis and built predict models. I found Springboard’s slack community help. I also used Stackoverflow and python libraries documentations. I have zero negative experience throughout the course, it was perfect for me. 

Prior joining Springboard, I had some technical background and experience with Data Science but struggled to actually get a job. After going through the process judiciously and with great dedication, I can confidently say  Springboard has helped me build a robust and very competitive portfolio. The curriculum typically covers everything needed to succeed. My favorite part of Springboard is the one-on-one mentorship where you could brainstorm, learn and typically create a connection with a professional who is willed to help you succeed. Also, the career coaches are really helpful.The underestimated section where many students miss out is the Springboard slack community where you get all the answers, motivation and collaborations on almost any topic. However, I would suggest  Springboard encourage collaborations between students by adding a group capstone project. This would benefit on many levels -- technical skill like git usage and soft skills like teamwork, collaboration etc.

Pros:Datacamp resource - I really liked in-browser coding paired with short lecture videos to be very effective to get practice with the key parts of data science problem solving.  There were lots of practice exercises that helped give me a better understanding of the concepts.Mentorship - I really liked my mentor, he was very experienced and had lots of industry knowledge, and it was obvious he had a strong grasp of the subject matter. As a part of the curriculum you work on 2 Capstone projects, and several other ad-hoc projects with the guidance of your mentor. My mentor provided helpful feedback and advice for lot of the machine learning methods I tried and gave me a starting point to work on the project. Cons:
Many parts of Springboard curriculum is just reading articles on data science or youtube recordings which can be find online. The video/audio quality was not the best. I think it would have been much better for Springboard to make some videos covering perhaps similar material but improve audio and video quality and create a more cohesive curriculum instead of many seemingly unrelated videos. Also, none of the assignments have answer keys provided to students, so there are not many ways to check your work. The machine learning unit's videos and assignments are almost entirely taken from a college class. The ad hoc projects were useful to get practical examples to refer to, but a few lectures from the class did not feel adequately sufficient to develop a good understanding of machine learning concepts and AI techniques.

Pros.Complete course: its content covers the fundamentals of data wrangling, exploratory analysis, statistical inference, machine learning modelling and model permofmance evaluation among others. You also have the chance to go deeper into some of these topics with the help and support of your mentor.Not only data science: Understanding and using data science techniques is not enough. This course also makes you focus in communicating your ideas in a clear and effective way. From the typical slide deck to a brief summary, you study different strategies to become a great communicator.Support: It is mandatory to have one call with your mentor per week. Still your mentor also helps you via e-mail answering any question that arises during the course. You may also schedule more calls with your mentor when needed. There are also office hours and community blogs to discuss ideas and ask questions.Experience: Lots of excercises and capstone projects that simulate real life situations in which yoy need to analyze data in order to make a prediction or find a solution to a business problem.Career support: Lots of tips and strategies for you to have a great resume and succesfully find a job in data science.Very intensive course: Be sure to have the time to fulfull all the requirements!.GREAT EXPERIENCE.

The Springboard Data Science career track was a great course that helped me solidify my knowledge of data science and gave me the confidence to apply for data science positions.The program was flexible which allowed me to complete the course while having to take care of a 4 month old baby. No other program gave. E that flexibility. The program also has great hands on learning material which helped me understand and practice the coding and data science process.The career resources were very helpful. I’m a senior level professional and still found the job search information very helpful.In my journey to learning data science I have taken a masters degree, online self learning and Springboard. If I could do it all over again, I would have take. Springboard first.If it’s not obvious, I would highly recommend Springboard’s Data science career track to anyone who would like to transition to this field

Pros: 1:1 Mentoring. They will guide you through the curriculum, help you set goals, keep you accountable if that's something that you want or need. The program has also an active Slack community, On demand Mentor Support, Career Support, Career Lessons and 3 different types of specializations, to meet different needs. It also has a lot of hands on case studies and 2 Data Science Capstone Projects.Con: I have to say that I started to really appreciate the program in the second part of it. The mentor change, the 2 Capstones and joining a group study with weekly meetings made the program more enjoyable.Overall, it was a really good decision to pick Springboard. 

This was one of the websites that I initially looked at when deciding which boot camp to pursue. Springboard was highly rated, and with good reason. After finishing the curriculum, I can say that I feel confident in my abilities in implementing data science methods. The boot camp provides access to the platform DataCamp, which has become one of my favorite resources to practice my coding skills. The staff and my mentor were also very helpful throughout the journey. I never felt intimidated about reaching out because they were all so nice. As long as you pace yourself and commit to improving yourself daily, you will have no trouble achieving  great feats with the course. 

I don’t write reviews, but I have to write this particular review and warn others to be careful. Springboard doesn’t teach you anything for their Data Science track program. Don’t waste your good credit score, you will end up being in debt. Their curriculum is poor, and their mentorship program sucks so badly. They basically don’t add any value to you as a student. You will still end up being jobless and in debts due to some hidden clauses in their contracts.
If your goal in going to Springboard is to get a job, as was mine, then go to Udemy or Data camp and save money. Springboard charges $7500 for their Data Science Track whilst you can get those same courses on Udemy or data camp for as little as $25. 
My girlfriend warned me not to registered for springboard, but I didn’t listen to her and went ahead to register for springboard tuition deferred payment. Four months down in the program, I traveled internationally with the approval from my springboard mentor, only to return two months’ afterwards and springboard had canceled my class. No word from my mentor or any of the springboard administrators, only for me to get a bill (six months after they canceled me from the course) asking me to pay $7500. What the fuck? Why didn’t you send me a bill immediately you canceled me from the program if I owed you? Why wait 6 months afterwards and now telling me to pay you $7500? I am still talking to my lawyers to fight this reap off and unprofessional conduct of Springboard as the debt is now referred to collection agencies. One of their representatives contacted me by email apologizing for their unprofessional conduct but the apology hasn’t wiped off the bill. I still have to pay them $7500 through the collection agency.
Please don’t let springboard mess you up and mess your credit score too like they have done to me. Find a better and cheaper learning platforms like Udemy and Data camp. Also, learn to listen to your girlfriend or wife.

Pros: Expansive curriculum that presents substantive exercises on the most important/common aspects of Data Science; mentor is a tremendous fount of insight; capstone projects are completely open for you to choose subject matter, data science problem, models, etc. The benefit of this freedom is that you really have to take take the proverbial 'reins' and carefully think about how you want to frame a problem, and how you want to approach with solutions. But your mentor is always there to bounce ideas off of. ; very supportive and accommodating staff. Cons: This may not be an issue for all students but my pace suffered a bit because I didn't have sufficient computing power necessary to executing the more intensive code that is iterating over very large datasets.