Cambridge Spark Ltd

I developed many skills at the frontier of data science and had a lot of fun doing the ADS bootcamp. The delivery of the course from the instructors, and the design of the homeworks, were excellent; as were the constant support from the CS team (including... Read More helping you to find a job after) and the opportunities for networking. I'm so glad I decided to take the course and recommend it to anyone else who's curious about data science.

With an ever-increasing variety of options, I found trying to decide how I wanted to further my data science skills challenging. I'd already completed a number of online MOOCs (such as Andrew Ng's famous Machine Learning Coursera course), however knew... Read More that I wanted some more formal, in-person tuition. I was/am a Python developer and Data Engineer by profession, and so wanted to find a course that would spend relatively little time on the basics of Python programming and jump straight into the really interesting stuff (note: most of the class had far less programming experience – there’s no requirement to already be very experienced with Python!). I'd already heard of/taken free classes with well-known providers such as General Assembly and DataCamp; it was whilst looking at reviews for their Data Science bootcamp offerings that I came across Cambridge Spark. I was attracted to the breadth of topics in the curriculum (everything from linear and logistic regression to neural networks, natural language processing, and big-data processing with Apache Spark) and the fact that I'd be receiving a full weekend of tuition (9:30 am to 5pm, Saturday and Sunday) from highly experienced practitioners every fortnight for nearly 6 months. I knew I wouldn't be able to take 5-8 weeks off work to complete a full-time bootcamp, and preferred being able to devote weekends to learning, rather than spending a couple of hours in the evening twice/three times a week after work (when I'm already knackered). Additionally, compared to other part-time bootcamps, the Cambridge Spark offering presented a very competitive hourly rate for the amount of in-person tuition received. I took the plunge, and was not disappointed. The quality of the materials and instruction far exceeded my expectations. Time during the taught weekends was usually split roughly equally between informal lecture style tuition and hands-on exercises via Jupyter notebooks; allowing students to gain experience with the concepts being taught, and have all questions answered by the tutors. The weekends were held in a perfectly pleasant function room at a hotel near Kings Cross; however future students will benefit from the custom teaching facilities that Cambridge Spark have recently opened (again near Kings Cross). Between the taught weekends there was additional recommended reading, the ability to re-listen to recordings of the taught materials, and assignments to complete via Cambridge Spark's proprietary learning platform KATE (a real jewel in their crown). At the end of the course I spent 6 weeks on a capstone project – working with a start-up on a real-life problem to develop object detection models. Whilst the high-level theory behind the various machine learning algorithms covered is very well taught, the course primarily focuses on practical implementation. Those that complete the bootcamp will be well versed in how to go about approaching data science problems, including: data cleansing and feature selection; using Numpy, Pandas and scikit-learn; selecting and calculating appropriate performance metrics for models; performing hyperparameter tuning; and having the knowledge to justify their choices and be able to explain how their models work. Personally, I’m now very keen to gain much deeper knowledge of the underlying mathematics of machine learning algorithms, such that I may be able to contribute to the development of new techniques – hence I will soon be starting a full-time MSc in data science. However, I am very glad to have first completed the Cambridge Spark course, which has provided all of the skills I need to move from data engineering into data science. I honestly can’t recommend Cambridge Spark highly enough. (P.S. you also get to attend a formal dinner held at a Cambridge University college; what other bootcamp offers that?!)

Overall review
I applied for the course through work and managed to secure one of the few remaining spots on offer. My overall impression is that it has been a very worthwhile experience so far; challenging and extremely rewarding. The course is split... Read More into 3 sections; data analysis, data science and project work and has so far focussed primarily on Python (including pandas, numpy, scikitlearn, Anaconda, beautiful soup) with some SQL. It is ideal for CPD in the workplace. The lecturers/team are very supportive/approachable when you need them.

Pros
You will accomplish a lot in a relatively short space of time. Cambridge Spark is partnered with leading players in industry and academia so the course content is designed with real-world problems in mind. Provided you keep practising, you will leave with a brand new set of industry class skills.

Cons
The course is very involved and extra reading around the topics is required. The breadth and depth of topics is equivalent to a masters in my view! Balancing a full-time job with the course has, therefore, been quite challenging. Fortunately my employer has been as generous as possible with study time. Persistence with the course does pay off, though, and you will get there if you keep at it. The rewards are twofold; the skills you get, and most importantly the confidence you gain in having worked through a problem and passed. I would thoroughly recommend it. (Originally posted at https://careerkarma.com/)

The program was well planned and covered the basics of all the most important topics in data science. The instructors were knowledgeable about their subject and the exercises were in depth and allowed for further exploration into the material. The only... Read More downside of this was that some topics (neural networks, NLP) were not covered in as much detail.

Deep focus on applying different algorithms to data using python.
Tutors were knowledgeable and experienced .
Would like to see more content on real world application scenarios and case studies .
Provide more sessions for advanced topics like Neural networks... Read More .

Pros:
- In-person teaching with excellent lectures and Jupyter notebook assignments
- Coursework assignments every two weeks and 2 revision sessions that reinforce what you learnt in the taught lectures
- Project component was helpful as I was able to work... Read More on my own Natural Language Processing project and receive advice
- Friendly atmosphere on the course and staff happy to give advice

Cons:
- Feedback could be more rapid on the project and your CV

I can honestly fully recommend this course. I could really feel the difference of the before and after, both in terms of knowledge of the topic and in confidence in my own newly developed skills. The teachers are all very knowledgable and the class exercises... Read More and homework really challenge you and push you to improve on your coding skills. Also, the organizers are very supportive and always take student's feedback into account.

The aspects I was looking for when I selected a shortlist of Data Science courses to attend were:
- Content covered;
- Practical application of the content;
- Connections with the industry.

Cambridge Spark has fully delivered on all 3. The course content... Read More covered in detail several of the mainstream concepts used in the industry today and, at the same time, provided a friendly platform in terms of support from teachers / other staff and the adequacy of the teaching materials. The opportunity to apply all skills in a final project (and the flexibility of the topics chosen and entities to work with) is also of incredible importance to the experience.

I would also like to add that even after finishing the course Cambridge Spark is a relationship to maintain for the future as they provide a network that includes connections to other alumni, several industry players and relevant events that definitely bring value to a course graduate over the course of a career.

Pros:

- content broad and detailed, really encompasses most of what's needed for a junior data scientist role
- fun, engaging lecturers for the most part
- thorough notes and workbooks that can be used in the future
- great mentorship for capstone project... Read More
- strong network to be able to use in the future

Cons:

None really! My only feedback would be giving more access to jobs / companies earlier on in the process.

The classes and the topics were good organized, the teaching was good and the teachers had a good predisposition to explain the details.
I enjoy working in edukate, it is interesting that we can work with Git.
The individual feedback in the projects were... Read More missing, a part from edukate feedback, just to know the best way to develop the projects.
Reinforcement learning could be a topic to add in nexts bootcamp.
En general I think was an amazing experience.