Last year of the university, quite hard days you might imagine. Every second pass with the anxiety of future when you turned off the screen of any device and moved away from social media or anything else that killing your time. I was in one of these times. Nausea had begun. But it wasn’t a physical sickness as you thought. I’m about nausea of existence that Jean-Paul Sartre describes very well in his books. I felt like I must find a job directly after graduation and I mustn’t delay it because I’m already in my last year. You often feel that kind of moments if you live in a country which struggles with unemployment. I turned on my pc and started searching about departments I can apply for work after graduation. But there were lots of business departments I can apply to. So better to understand which is belongs to you and make a search on the internet. Then I saw an article titled “The sexiest job of the century”. The title seems like click bait enough and it got me. That is how I met with data science. I felt like this is the right way to go on but how could it be possible with a business administration degree? Here is the progress which brought me to the achievement.
Once you decided to become a data scientist, you should review your background to complete shortcomings.
Data science is a multi-disciplinary area which includes statistics, business domain knowledge and computer science.
Only good that I was lucky about the first two. If you don’t have any experience with the programming, you must start from the beginning. There are two most using languages in data science, Python and R. You should select one of them in the beginning. Both of them good languages in their area and have own specifications. You can find a comparison between of them easily just google it. When you decided, it is time to learn and practice about it. Thanks to open source languages, people can reach billions of websites, video tutorials and communities to learn about. These ways good to practice but if you have an irrelevant university degree about the topic like mine you should have a few certificates about data science and machine learning. Because you must prove your skills on some way to the recruiter to increase your recruitment chance. I had got three courses on Udemy. Two of them about Machine Learning and other is about Excel. Yes, Excel! I can say through my experience and complacency, you will complete most of your analysis on Excel. You should understand that data science is not only about modelling every day. It might be the last step of your job. Also, I can offer you getting a SQL course. You mustn’t become a data scientist to learn that. From my perspective, each student should learn SQL who wants to play with the data. And I cannot consider a company which doesn’t collect data in the 21st century.
Only buying the courses won’t help to achieve your goals. You should practice every day by scheduling your plans.
If you don’t have a life which you don’t need to schedule in university life, you are so close to f***** up in the future.
As I experienced, programming languages are very similar to communication languages. You can forget if you don’t use that. Because languages are ungrateful. This is why I repeat that you should always practice. You shouldn’t hurry up on that progress.
“Digesting must be slowly, nobody can be a data scientist in a week. “
Even while I was making progress, I forgot some of the course sections and returned back. It is good that having case studies in your courses. Small quizzes you will take with case studies would help to measure your success and detect the deficiencies.
Okay, let’s assume you became successful on your courses and took your certificates. You can apply to job advertisements but it may not be a mindful idea. Because only certificates can’t be the right way of proving your knowledge. People can take many of certificates by skipping the sections only and create resumes with fake skillsets. In order to prove your knowledge and create your portfolio, I recommend making projects on Kaggle and Github. Also, boot camps can be a very useful way to improve your skills.
What have we got so far? Data science tools, certificates, boot camps and the portfolio. It seems quite enough to apply for a job. But you mustn’t forget that you still don’t have an academic background in computer science. We only supported the knowledge through courses. I applied an academic master degree after graduation to assure myself on the academic side. When I research about master programmes, I met with Management Of Information Systems (MIS). Its contents of the lectures were familiar to my wishes. Also, it was the best choice which can cover my computer science degree deficiency in my university. By the way, I could practice my SQL and Python skills and make a few small academic projects about them.
I believe that healthy communication is the best way to achieving your goals. Because humanity is based on it. The reason for pointing out “Healthy communication” is to make you understand that it is different from social media relations. Purpose of saying this is that I found my current data science job in one of that way. I would never have known the job application If my classmate couldn’t inform me. I’m so grateful for having good relations in my university time. The application was about data science camp which you learn about it and apply in two weeks. It was a very tiring process. Finally, twenty determined young students selected for camp after interviews and ten out of them hired after the camping process.
And here I am writing this article as one of the ten people and one who does not have computer science or any other engineering degree.