Cambridge Spark Ltd

I did the Applied Data Science Course with Cambridge Spark from September 2017 to April 2018. I was part of the first cohort to do the training and far from feeling like a guinea pig, I thought that the program was really mature, even though it was the... Read More first iteration. I think some of the modules have been switched around slightly since then to help with the flow of the material, which should make the course even better.

I thought the topics were relevant to data science jobs in general, although there are topics at the edges such as survival analysis (useful for roles in ecommerce) which weren't covered but then I thought were quite easy to learn with the Python I had learned on the course.

On of the best aspects for me were the networking drinks and dinners where we met with data scientists and even startup CTOs from industry. I also really enjoyed working on the keystone project, getting access to a data set I would have never been able to work with otherwise.

When I think about what to improve, it would probably be adding a voluntary 'git-day' somewhere along the line - it took me a long time to really get used to git command line and all the magic it does. Other than that I really enjoyed my experience! Thanks guys!

I didn’t set out to become a fully fledged data scientist but I ended up being able to deliver an end to end data science project. I’m actually very proud of that. It took a lot of work on the sidelines to work on my coding skills and on some of the maths... Read More fundamentals but I managed to get this to a sufficient level to get by.

I decided to do the bootcamp for several reasons, including:
- I am managing the team that is developing the data science platform we will use and undertaking the data science.
- I didn’t attend the bootcamp to become an expert data scientist. I’m not at a place in my career where I would get value from dedicating the amount of time that would be required to practicing the art of data science to be proficient.
- The goal of attending the bootcamp was to learn the subject at a level deeper than online MOOCs or self-led learning would give me. I think to lead a team of data scientists effectively requires a reasonably deep understanding of what a successful data science workflow requires.
- Also to act as the ‘data translator’ for the business. To act as the interlocutor between the data scientists and the subject matter experts (‘SME’). The aim is to get the SME and data scientists talking each others language and always be focussing on how our work can add value. It’s easy to get distracted by the ‘academic’ value of a use case and the Kaggle-type pursuit of model accuracy. Often delivering something that meets the users needs in a short period of time will add more value than an extra 0.02 on your precision score.
- It was also very important that I was able to fit a bootcamp around work.

I would say the pros of the Applied Data Science Bootcamp are:
- The tutors are of a high overall standard.
- You’ll make some great new friendships amongst your cohort.
- You will learn what it takes to be a data scientist with real examples and the support you need to solve them.
- It pushes you to do the work much more than a self-led MOOC would. I had to miss one weekend (because snow storms cancelled all the flights from Aberdeen that weekend) but otherwise the rhythm pushed you to do the work.

The cons:
- There should be a longer lead time when sharing the pre-requisites and having a pre-qualification to start. The coding knowledge required a the start was comprehensive. I think at least completing EdX Harvard CS50, or Data Quest Data Science track would be required to be able to hit the ground running with this,
You get a sense this is still a growing organisation and there are one or two kinks that need ironed out (selecting the right communication channels, videos of sessions were promised but not made available)
- There was probably too much time spent preparing environments, fiddling around with GIT, working around Operating System bugs etc. Yes it's interesting to learn ways around this, but it went a bit far. I would *strongly* recommend the guys to set up a containerised environment, VMs or equivalent.
- The rooms need to be a bit bigger to be comfortable.

For me, compared with the study options available to me Cambridge Spark had the best balance between good feedback, time required to participate and complete and rigour. I’m very glad I chose to Cambridge Spark and will definitely stay connected with them.

I came into the course not really knowing what to expect as this was my first ever bootcamp. Coming out of the course, I can safely say that the investment has paid off for me, since the course led me directly into my current data scientist job.

My background... Read More prior to the course was not technical, I was proficient in SQL and had had some experience in coding macros in Excel using VBA as well as some very limited experience in python and C++ (a small personal project per language).

On the course:
The tutors are as good as they get, with the added advantage of the fact that they have had industry experience with the likes of Google and Amazon. There is quite a lot of material to go through and it may seem daunting, especially at the beginning, but as long as you are prepared to commit, with the help of the tutors you will be able to solve any difficulties you might encounter during the course. I would say that it is the same material (or parts thereof) that gets taught at university and post qualification level, so you can be confident that the knowledge and skills you will acquire will be of direct use to you, should you pursue the data science career path. The final project is a de facto industry placement and it is quite more than likely that you will be able to secure a job if it goes well (I was given an offer a few days after my final presentation).

As said, commitment is the key with this course as you do not get formally tested at the end. I had no problem with this however because it was so easy to bond with the tutors and the other students, as well as the way the material is structured. An additional perk for the non-coders is that by the end of the course you have had so much practice, you not only understand how to set up neural networks, but have done the necessary groundwork to implement pretty much anything in python since basic programming concepts are also covered in the course.

Make sure you are somewhat comfortable with mathematics to be able to grasp the underlying concepts and make sure your weekends are free!

If you do decide to take the course, congratulations! You have made a solid choice that will undoubtedly pay off in the future!

Before I attended the Cambridge Spark Applied Data Science bootcamp, I was working as a Data Analyst who was stuck in Excel, the only exciting part of the job was using Tableau. I decided to rebel against Excel by learning python and made a decision that... Read More I want to use this tool full-time.
Upon much research I found the most suitable job for me was that of a Data Scientist, because of the mix of programming, data analysis and math. I tried to do as many online courses as possible but nothing really stuck. I then decided to go down the bootcamp route, the only issue was they all required I quit my job as they were all full-time.
Then I discovered Cambridge Spark who offered a part-time course and after speaking with the founder Dr. Raoul, I took the plunge and signed up.
2 months after finishing the course and I have completed the transition and gotten a job as a Data Scientist!.

Pros
- Capstone Project: This was hands-down my favourite part of this programme. I got to work on an end-to-end Data Science project with a a Startup called Wefarm. This project was focused on NLP and gave me not only great technical skills but also communication skills as I had to present to both technical and non-technical audiences. This project stands out on my Resume and constantly impresses potential employers. Check out my project here: http://bit.ly/freddie-project

- Teachers: The calibre of the teachers who taught the course is VERY high, they all have PhD which range from Computer Science, Mathematics to Machine Learning. Although not all PhD's are good are teaching, it's safe to say that doesn't apply to these teachers. Apart from being smart and good teachers, they are also very relatable, patient and approachable.

- Content: The content was very hands-on and practical. Throughout the course we worked on a number of mini-projects e.g. Predict NFL players salary, Predict the successful kickstarter campaign, etc. These mini-projects really help you put into practice what you learned during the classes. The content is also delivered by Jupyter Notebooks where you have exercises and solutions, this has especially come in handy for me doing my own personal projects as I am able to copy some code directly and also learn the best programming habits.

- Community/People: This was perhaps the most surprising part of the course for me. I actually made some real friends that I still keep in touch with till today from the course. There is also a Slack channel where you can ask any question and both the teachers and students respond to your question. The calibre of people on the course also ensures your network expands with value-adding people. There was also some formal dinners held in Cambridge where got to network with extremely intelligent and successful people from the AI industry.

- Part-Time/Payment: The course is part-time meaning I didn't have to quit my job to participate in it. I was also able to pay for the course on a monthly instalments basis which took the pressure of my finances and made it more affordable for me.

Cons
- Social Life: Due to the fact it's a part-time course, this means classes take place on the weekends. This sometimes meant I worked for 12 days straight (5 work days then 2 work weekends then 5 work days) without a break. As a result for most of the 6 months during the course I didn't really have a social life. BUT if this is something you really want you have to be prepared to sacrifice, which I was fully prepared to do.

Lessons learned
- You get out of the course what you put into it. If you decide to just show up to the classes on the weekend and not do any supplementary work during the week then you might come out unfulfilled. My advice is to do the course along with a book like Introduction to Statistical Learning (ISL), this way you can dive deeper into the theoretical aspects as well as practical.
- Take advantage of the brilliant teachers. Never be afraid to ask questions no matter how 'silly' you think it sounds. Also if you do supplementary study on the side, you will likely have a lot of questions to ask, this is good. Make sure you have a thorough understanding on the fundamentals after each class.

Having worked as a data analyst for 2 years, I was looking to up my game and get a job as a Data Scientist. As most people looking to get into Data Science, I spend a lot of time comparing different courses (degree/certificate/in-person v on-line). I... Read More decided to go for this course as it focused on learning by doing, still giving you the necessary theoretical background.

I haven't had any regrets, as 2 months after finishing the course I was offered a position as Data Scientist.

Pro's:
* Amazing people - Doing a course like this is not only about expanding your skillset, but also connecting you with great people who can help you in your further career.
* Content - Balanced well between theoretical necessity and business application.
* Graded assignments after every weekend - end to end projects, working with very interesting data sets (bitcoin, NFL, insurance, fraud, ...). Furthermore, the data was purposefully messy,
ensuring you to learn how to clean, pre-process and build pipelines.
* Different speeds - The content is taught in such a way that people with different backgrounds are able to follow (of course, some additional work to catch up is needed)
* Events - The Cambridge Spark team did a great job organising formal (diners at Cambridge university), as well as informal gatherings.

Con's:
* Time - unless you actually have a 9 - 5 job, it can be challenging to combine with a full-time job. Then again, you get from it what you put into it. Right mindset is needed!

Final - Project:

At the end of the course you are given the choice to work on a project supplied by an industry partner or work on a project of your own (if meeting the requirements). This opportunity has helped me enormously do work on my start up whilst having some great feedback and support - which has been an incredible surplus for me.

Given the dept and the detailed level of the topics covered along the 6 months, the course should be renamed
EXPLAINED and APPLIED Data Science!!!

Pro:
• Technical preparation of the tutors. No matter how deep you will be willing to dig into technical and/or... Read More non-technical details, they will be there to answer your questions and/or provide the required material.
• Hands-on classes and graded assignments. Both (personally) essential in order to strengthen the theory and the concepts learned during the classes.
• No domain specific. Workshops and tutorials are usually focusing on a specific subject (e.g. insurance). Working with Cambridge Spark instead was a completely different experience. No matter what your background is and what industry are you involved into... assignments, examples and exercises are covering different topic (from cryptos and e-commerce to medicine and advertisement) to match every student’s expectation.
• Top-class Networking. Monthly events are organized to get to know the other students, the team and also (and especially) external startups and companies interested in expanding their Data Science teams.
• Final project. Startups and top-class companies are collaborating with Cambridge Spark to address real world problem and exploit data science techniques. Or if you have your own idea EVEN BETTER! Cambridge Spark will be your personal consultant to achieve your goal!
• Formal dinners in Cambridge University. Ever wondered how does it feel like to be part of a black-tie dinner in one of the most renowned university in the world? Here is your chance! ;)

Cons:
• Forget your weekends for the next 6months
• Work! If your idea is to have a high level understanding of Data Science, ML and so on... watch a YouTube video. If instead you are looking for a career change or “just” to familiarize with the tools needed to give you some boost in your actual (Data Science) position ... Cambridge Spark is what you’re looking for!

The CambridgeSpark team is super smart, great at communicating the lessons in an easy to understand way (I've not got any data science or software development background, so this was essential to help my learning) and very approachable and friendly. Each... Read More tutor has a great deal of hands experience and are able to communicate how the skills your learning can play out in things you act on everyday.

I had no previous Python experience but by the end of Day 1 I was pulling my own data into Jupyter and processing it. I think that shows how practical, hands-on and well taught their courses are. I look forward to doing more advanced data science courses with the CambridgeSpark team in the future.

The information presented over the two day machine learning bootcamp was far more useful than the courses one usually sees online: the python modules you learn and tips you're taught are very applicable to use in industry. Also, the teachers are all very... Read More approachable, and will help you with any queries you might have. All in all a fun and informative experience :)

Hello world,

Took a data science course with cambridge spark about 6 months ago from now and I would highly recommend it to anyone. The environment is fantastic - you will be surrounded by people who are genuinely excited and interested by what they're... Read More doing. The support you get throughout (and after) the course is invaluable - you will work through challenging and engaging workshops, while also being introduced to data science packages which are industry standards (pandas for pre-processing, sci-kit learn for machine learning algorithms, numpy, seaborne and matplotlib for visualization). In fact I was so impressed and interested by the presented material that as soon as I got home on the last day of the weekend course I proceeded to apply the exact same methodology to a kaggle dataset. This mini-project helped me secure a machine learning internship at a London-based startup which used similar industry standards (pandas, sci-kit learn, numpy). I am now a full time A.I engineer, 6 months later. None of this would have been possible without Cambridge Spark - I am eternally thankful for that weekend course which opened my eyes to the wonderful world of machine learning and data science. All you need is to have an interest in machine learning and coding - with a little willpower and curiosity, everything from thereon will follow.
What mostly stood out though is how warm and welcoming the community is, both during and after the course - I am still in close touch with my peers I met during the course, as well as my instructors, and we continue to help each other improve.

A Data Science bootcamp is difficult to get right especially for an ever-evolving multi-disciplinary field. Unlike academic courses which can be deeply theoretical, CambridgeSpark seem to have pitched their course content just right with the aim of providing... Read More highly relevant material for the needs of the current job market whilst simplifying key concepts making it accessible to those from a non-Data Science background such as myself. Coupling this with their well-chosen hands on class activities followed by the independent project offers an unparalleled re-enforced learning experience.

Soon after attending the introductory bootcamp I found myself booking another 3 of their follow-up courses because I was highly impressed by the delivery. The instructors (mostly Oxbridge PhD grads) imparted their knowledge fluently, constantly engaging attendees to interact and get involved at every possible opportunity. I also had the chance to have a 1-to-1 feedback session on my assignment project which helped boost my confidence in communicating my findings.

The only aspect I felt lacking was the career support, though I’ve been told that this is now made available as part of the 6 month Applied Data Science course.