Science to Data Science

 Dataknowledge Science may be a combination or mixture of mathematical and technical talent, which can need business vision additionally. These skills area unit won’t to predict the long run trend and analyzing the info.  Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" in order to "understand and analyze actual phenomena" with data. A data product is a technical asset that utilizes data as input, and processes that data to return algorithmically-generated results. For more Information please visit our website  https://www.gologica.com/course/data-science/ 

Pros:
During this program I worked in a team of four with a company in a commercial DS project.

It is a very intensive program with very interesting webminars, panel discussions and tecnhical support from internal and company mentors.
At the beginning I was reluctant about how to work remotely in this kind of projects (as I have done all learning/projects by my own in the past), but the countinuous meetings with the team and mentors made the communication and team work being a success.
Also, I tried something called 'pair-programming' that, at first, sounded like a very difficult thing to do when people are not sitting together, but after the experience, I am planning to add it to my way of working( it is a great way of moving fast in the project at the same time of learning from others).
Another thing to highlight is that S2DS community is great! Lots of activities are organised every week to know each other as we worked remotely and there was even a 'Day out' in London that was excellent!

I would recommend this course, without hesitation, to anyone struggling to move into data science or who wants to find out what it’s like to work as a data scientist.

I took part in the S2DS Virtual Program in October 2019 while based in Germany. The application process consists of an online application on the Pivigo website, followed by an interview which tests basic technical knowledge as well as asking general motivational questions. About 3 weeks before the program we were given access to several program-specific videos covering things such as programming in Python and R, data visualisation and git. The videos are very clear and great to get started with any topic you are unfamiliar with. Also provided is a document suggesting additional learning resources as well as interview preparation materials.

On starting the program there are several introductory talks for the first two or three days; however, the majority of the program (which lasts 5 weeks) consists of working on a real data science project from start to finish with a client. My team was working on an interesting project with the UK Food Standards Agency (FSA). On assigning projects, individual preferences are taken into account. The virtual format of the program works extremely well and is of great benefit, especially with regards to changes in working habits in recent times. The majority of the teams made use of Slack, Github, Zoom and Trello for communication and team organisation. The team is generally left to its own devices; however, regular meetings are organised with a company mentor (in our case from the FSA) and technical mentors from Pivigo to keep the team on track. Another nice touch is regular individual meetings with a personal mentor at Pivigo, where additional more private matters can be discussed. 

Aside from the project, virtual networking sessions as well as Q&A sessions are organised to get to know the whole cohort. These sessions are not only great for practicing networking skills but also make you realise that everybody is in the same situation. A series of panel debates are also organised regularly with data scientists from startups, corporations and former S2DS alumni. These are very informative and give a good overview of the breadth and depth of roles available in the world of data science as well as allowing you to ask any questions to the panel.

A very helpful careers presentation was also given towards the end of the program, which really made me think about changing my approach to the job search.  Immediately following the program individual CV and Linked In profile reviews can be requested at any time. You are also given access to the whole Pivigo S2DS alumni network (over 600). This is useful not only for access to privately shared job roles, but also as a knowledge resource for any technical challenges faced later in your career.

At the end of the program final team presentations are given in the form of a webinar in additional to the requirement of a written report for the client. Not only was there a great sense of achievement on completing the program but a real sense of community with the cohort. The program was an extremely useful topic in a recent interview I had: here the journey of doing a project from start to finish offered by S2DS was key. The interviewers were also impressed by how so much was achieved in just the space of 5 weeks. 

I can wholeheartedly recommend the program to anybody interested in a career in data science. I would emphasise that for an academic thinking of moving into industry (like me), the experience is invaluable even if you do not end up pursuing a pure data science role as a lot of the things learnt here are transferrable to many related technical roles.

Let me begin by stating that I applied for S2DS,  but was not accepted. The people that I interacted with during the process seemed to know what they were doing, so I will assume that they were right in rejecting me. If I do not fit in the group or am not fit to be a data scientist, I can only agree with their decision not to waste a valuable spot on me. What I do not agree with is the way that information on my rejection was provided to me. 

In April 2018 I applied for S2DS London. The programme looked great and I was very enthousiastic and eager to join, as I was at the end of my neuroscience PhD and was thinking of a career in data science. I was invited for an interview and had a pleasant (but definitely not flawless) conversation with the woman on the other side of the skype connection. I was given her e-mail address for any further questions and I was told that I would receive information on acceptance by April 30th latest.

Being very eager and nervous whether I would get accepted, I barely made it until May 1st before I couldn't take the excitement any more and I e-mailed the person I had my interview with. I never got a reply from her. Two days later, I decided to e-mail the person that had invited me to the interview. She finally replied a week after the self-applied deadline of May 1st with the information that they were very busy accepting people and would get back to me soon. 

Two weeks later (May 21st), I still very much wanted to join and decided to be the one to open the conversation again. I got the reply that I was not immediately accepted, but rather placed on a waiting list with the information: "There are always a few who decline their places or drop out and then I would like to be able to make you an offer". I thanked her for the place on the waiting list and decided that all I could do was wait and I did. About three weeks before the course was supposed to start, I decided to let them know that I would still be very happy to accept any open spots in the course. I never heard from them again.

 

Sorry for the long post. In summary, I assume the course is great and very worth taking. However, the rejection procedure is not very professional and may leave you with a slightly bitter taste, given the time you probably invested. 

This course is a great way to get soaked in the world of data science. It provides a great overview of the data science scene, at least the English one, and offers great opportunities to start your career.

The training is not a course where you learn to program, that should be had beforehand. This is a training where you learn a few new tools and methods, but the most valuable part of the training, besides the project experience, is the business talks they offer, they give a great introductions to understanding the motivations of the science you do in industry. Having the experience in a company (of course this depends on the size of the company) is by far the best thing of the course, since you get to see how things move "out there".