By Deepak Mahtani, Pivigo Community Manager
Welcome to my first Pivigo blog! In this post, I address one of the most common questions we hear from budding (and current) data scientists: how can I kick-start my career? I will give you my three top tips for creating a successful data science career, based on my own experience of transitioning from PhD, through S2DS, to my current role at Pivigo. As the community manager here, if you have any questions on this – or anything else – please let me know!
For me, this is possibly the most important of all my tips. All good data scientists use the right tools for the job, but this does not mean they know all the tools under the sun inside and out. My advice is to get a good grounding in at least one programing language (python or R are two of the most widely used). Having this will make learning other programming languages a matter of syntax. I would also recommend being able to efficiently use stack overflow. There is no point in reinventing the wheel, if you are encountering an issue the chances are that someone else has encountered the same issue. Another situation you will no doubt encounter, needing to use a programing language you have not used before. The way to do this is to first write down what you want your code to do. Then use stack overflow to flesh out these comments into code. Being able to quickly use an unknown programing language to conduct an analysis is a vital skill for all data scientists.
The first step is always the hardest
When I was starting my data science career the biggest problem I had was the lack of commercial experience. There are two ways to go about gaining some commercial experience. First there is Kaggle.com, this website is the is full of interesting questions with data sets to not only help you practice the skills you have been learning but also to put them into a more business focused goal. There is also the exciting competition aspect. Entering a few of these will show employers you are really dedicated to a career in data science and gives you something to talk about at interview. The second way, and for me the preferable way, is to participate in a data science bootcamp. These are designed to put you in a real data science position for a short period of time working on a real project. There are several out there to choose from depending on your budget. Having participated in it the one I can talk about with most authority is Science to Data Science (S2DS.org). It is a 5 week bootcamp in which you work as part of team of three or four people to work on a real project that your partner company are trying to solve. The key aspect of this is that you are working as part of team. This is what a real data science career is, it is not a conveyer belt where you do one bit and pass it down the line. You will all work together utilising your individual skill sets to build a solution. Communication is key and if you can effectively communicate your methods and findings you will have a long and prosperous career.
Meet, communicate and learn
It can be hard to understand the current state of any field. Thankfully there is meetup.com There are hundreds of groups that hold regular meetups for almost everything. The Data Science ones are fantastic. They give you a way of meeting current data scientists, understand what they are currently working on and network extensively. This will also show employers that you have the commitment to learning and to better yourself. It will also help you to understand where you want to work. You should only be targeting jobs you will consider taking, and this will help to understand what you are interested in working on during your career. Being able to network well and building good report with people is a key skill to have because as a data scientist you will be meeting with a wide verity of people. This could be a conferences or business meetings so its important to have a head start while looking for a job.
I hope these tips help you in your data science career, if there are any questions you have or topics you would like me to discuss in the future, get in touch. Thanks for reading!