Try and find some certification program, course, or something that signals to companies that you are serious and able to apply your skills to meet their needs. Finally, getting your foot in the door in any industry can be hard. If you have (or are about to have) a Ph.D., you probably know something! You are more qualified than you likely give yourself credit for, and should not let yourself forget that. That’s what makes data science into a “data art,” and that’s what makes it fun! Third, especially if the student is coming from a graduate program, know your value. Be prepared to combine solutions together, modify code, or apply technologies in ways they may not have been initially intended. While many people have attempted to solve almost any problem (Stack Overflow is proof of that), few have likely tried to solve the problems you will be facing with the exact intention that you have. What advice do you have for students considering a career in data science?įirst, as I mentioned earlier, data science is much more an exercise in mathematical and statistical reasoning than anything else, so don’t neglect your mathematics! Second, be prepared to be a pioneer. No one can stay informed on every topic, so there will inevitably be times when you have to learn on the fly to use the latest or best techniques to solve a problem. Data science is a rapidly evolving field methods change, new techniques develop, and there is always something relevant to discover, understand, and integrate into new or even existing projects. People who have a strong grasp of mathematics and statistics and can learn and apply new techniques rapidly. What type of person does well in this role? Even after getting my first data science role, I felt much of the same imposter syndrome that plagues many people, especially those coming from academia. While I am likely not in a good position to comment on career highs and lows (I have not been in data science for that long), I can say that the biggest challenge I faced in data science was believing myself to actually be qualified. What are some high and low points for this career? What challenges might a data scientist face? They also help to match their students with prospective employers, which enabled me to get my first job in data science at Cova Strategies (which later transitioned to a role at NNData as senior data scientist). The Data Incubator specializes in taking candidates with strong academic backgrounds and helping them to learn how to conduct and communicate data science effectively in the private sector. That is why I chose to enroll in The Data Incubator. While I was highly educated, I had no specific certifications or qualifications that many jobs were looking for. My first problem once I decided to switch careers was how, exactly, to transition. I also felt that data science, as a fast-growing, dynamic field, would allow me to expand my skills and insights faster than in academia.Ĭan you describe your path to a career in data science? Data science offers a way for people with strong mathematical and statistical backgrounds to apply their industry knowledge and research acumen to problems in the private sector in a much livelier job market (also for substantially better pay than is offered in academia). But I (and many of my fellow young Ph.D.s) gradually realized that the academic job market has serious problems that prevent it from absorbing and properly utilizing all of the talented candidates who are getting their doctorates. Prior to data science, I was a professor. Why did you choose to become a data scientist?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |