AI News, Data Science Bootcamps

Data Science Bootcamps

We recently caught up with Kim Nilsson, CEO at Pivigo, the company behind S2DS (Science to Data Science). We will be chatting about her transition from academia to Data Science education.We’ll also explore their process and curriculum at S2DS and some of the factors that differentiate them from the other European bootcamps.

When I wanted to leave academia and find a new career in industry, I struggled to know what to apply to and how to present myself and my skills.

My experiences in the difficulty of the career transition, and his experiences starting a healthcare recruitment business led us to the idea of Pivigo.

We realized early on that commercial work experience is everything, especially when applying for jobs in the UK, and we were inspired by other PhD-to-industry programs to create the S2DS program.

My role today as a CEO is to overlook all the work we do at Pivigo, from reviewing applications and interviewing applicants, to setting up the events, mentoring teams and of course taking care of our business partners.

let’s create a way for these amazingly talented people to gain the experience they need, as quickly as possible, to allow them immediate career transitions.

The by far most important aspect of the program is the project work which is completed in teams of three or four over five weeks, and the projects are real projects, with real data, and mentored by someone from our partner companies.

The program has three components: theoretical learning done via lectures and video tutorials, practical learning done in project work and networking aspects via events, panel debates and alumni meet-ups.

The by far most important aspect of the program is the project work which is completed in teams of three or four over five weeks, and the projects are real projects, with real data, and mentored by someone from our partner companies.

100 participants, whereas the Virtual event runs several times per year and takes circa 15 per program.

: Jason and I were eager to support scientists like myself to make the transition, but we acknowledged that the typical lack of work experience was holding them back from being hired.

I read about the data science bootcamps that were being set up in the US and thought that was a perfect way to create an environment where companies looking to hire, and PhDs interested in new careers, could meet.

We have clear evidence that S2DS does get our participants into new jobs and new careers, but we have also seen that the partner companies gain enormous benefits from partaking.

Minimum requirements are a PhD (for London event) or an MSc (for the Virtual) in an analytical topic, strong maths/stats skills, intermediate programming skills in a mainstream language and great communication skills.

Other than that I think we have taken people with some 20 different disciplinary backgrounds, with no work experience, only academic work experience or some commercial experience.

The cohorts are really very diverse but what they have in common are great skills and super strong motivation and drive to start a career in data science.

: Beyond the technical skills mentioned earlier (maths/stats/programming) we look for strong communicators who can explain technical concepts really easily.

A great sign, for example, are when the applications have already taken courses, joined hackathons or solved challenges before joining S2DS, as it really shows motivation.

The fact that they have a real project to talk about, showing they understand what it is like working in a commercial environment, is absolutely key to securing the job.

Beyond that we have special career coaching sessions, and give the Fellows any and all support we can give after the program finishes, including introductions to hiring companies.

It is definitely a majority that go into Data Science roles, a small minority go back to academia and the remainder split between Data Analyst roles and other engineering roles.

The fact that they have a real project to talk about, showing they understand what it is like working in a commercial environment, is absolutely key to securing the job.

Beyond that we have special career coaching sessions, and give the Fellows any and all support we can give after the program finishes, including introductions to hiring companies.

If your next hires have motivation, drive, curiosity and maturity, then they will learn whatever skill or tool they need to learn to complete a task successfully!

Of course the hottest topic right now is deep learning, and the hottest tool Spark, but that doesn’t mean that I think any aspiring data scientist should rush out and learn about these.

A typical day probably starts with meeting up with the team in the morning to talk about the latest progress, and what is going to happen that day in the project work.

Then off to working on the data science project, with occasional coffee breaks and interspersed with conversations with other cohort members about their work.

: We ask every team to give us direct feed-back during the program, and we also send out an anonymous feed-back form to all the participants after the program, to canvass any suggestions for improvements we can get.

: There is so much emphasis on machine-learning and algorithms today, but no one tells you before you get into it that 70-80% of your time as a data scientist will be spent on cleaning and preparing data for analysis.

: Of course the hottest topic right now is deep learning, and the hottest tool Spark, but that doesn’t mean that I think any aspiring data scientist should rush out and learn about these.

Very few bootcamps offer real company project work, and if you add in the diversity of our program (both on the participants, and the company side!) and the strength of the network you are included into post-S2DS, I think we are unique.

What we have actually seen is that they are extremely well supported also by their fellow team and cohort members, who all look after each other and pick up the slack.

Each project uses the tools and technologies that the partner companies sets as preferences, although in some cases the companies have no preferences and the teams end up choosing their own set-up.

: On a typical day, how much time do your fellows usually spend in formal lectures, working on problem sets, listening to guest speakers / networking , etc   ? A

: The program varies a lot during the five weeks, but averaged out across the program I would expect circa 75% hours spent on project work, 15% on lectures and guest speakers and 10% on networking.

It is nice to see S2DS has identified lack a experience  as one of the impediments for academics making the transition to Data Science and then work with fellows to address that.

The tools used in industry can change as rapidly as every six months, and Universities are not capable of changing their curriculum at such a short pace.

We were grateful to have a prestigious company like KPMG believe in the concept and become our Principal Partner for 2014, thereby securing the support we needed to run the first program which, of course, ended up being a great success.

We are actually very proud that our partner companies come from a diverse set of sectors (Technology, Retail/E-commerce, Healthcare, Consultancy, Fintech, Not-for-profits etc.), as it means there is a project for everyone, and there is more shared learning when projects from different sectors get solved side by side.

The Life of a Data Scientist

They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them.

Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.

For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging.

A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

$163,132 Broadly speaking, you have 3 education options if you’re considering a career as a data scientist: Academic qualifications may be more important than you imagine.

To avoid wasting time on poor quality certifications, ask your mentors for advice, check job listing requirements and consult articles like Tom’s IT Pro “Best Of”

This includes the framing of business and analytics problems, data and methodology, model building, deployment and life cycle management.

Requirements: The EMCDS certification training will enable you to learn how to apply common techniques and tools required for big data analytics.

Related SAS certifications include: Some data scientists get their start working as low-level Data Analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations or analyzing A/B test results.

you could think about building/engineering/architecture jobs such as: Companies of every size and industry – from Google, LinkedIn and Amazon to the humble retail store – are looking for experts to help them wrestle big data into submission.

data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.

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