Data Science: Pros and Cons

Key Takeaways

what's up guys get here back with another data science video for you today I'm coming to you from Belize where I'm on vacation and kind of celebrating some of the good things in life in that same vein I thought it would be cool to make a video about some of my favorite things about data science the pros and also some of the some of the drawbacks of the profession please bear with me it's a little windy out here so I don't know how that's gonna affect the audio but hopefully you can still hear me so as you can imagine one of the perks of data science is the pay is pretty good and usually you're working in technology companies and so there are pretty good benefits for example at my company we have on limited PTO we have free haircuts in the office really neat perks that I really enjoy the second perk that comes to mind is that as the data scientists you get to learn for a living now I've gone to school a lot you know to master's degrees I'm constantly continuing to learn and that being part of my work is also something that's really cool that's a part that's something that helps me grow as a person and that's something that I'd like to have in any endeavor that I do well part of that learning process is the ability to glean knowledge from other people as well in data science you generally work with other really intelligent people and that accelerates your learning you're able to understand different concepts because you can bounce ideas off of people and you know I can't celebrate that fact enough I mean that's something that that I get a lot about that on a day-to-day basis so the next thing that I think is really important as a data science that you get to that you get to have is that you got to work on challenging problems at work one of the number-one drivers for happiness in a job is the ability to basically be stimulated by what you're working on and that's very common in data science you're always having to look on Stack Overflow and try and understand new different things you're always having to ask people to figure out how to work through a problem and you're always wrestling with something trying to figure out how you can do it better or how you can implement it more efficiently another big component of job satisfaction is the amount of autonomy that you haven't worked with data science you're generally work on projects individually or in small groups and your subject area so you do have a lot of control over when you're working on to a certain extent and you have the ability to implement solutions how you see fit so one of the things that data science does require is a pretty big upfront investment you really have to learn the skills either through formal education or self teaching you have to prove that you know this yells a bunch of projects to be able to get into the field so there is a lot of upfront time cost to getting your foot in the door to getting into having a successful career another challenge with data science is that the field is so new that and sometimes unclear what the job is Commission is and the definition of what a data scientist is is very different across different jobs different companies expect so you really have to think about especially when you're interviewing what data science work you're gonna do at a specific company and if that man is what your idea of data science is there's a lot of people that go in there like oh I'm going to be implementing all these solutions but they're really doing mostly data engineering and if your work does not match your expectation of work that is a recipe for dissatisfaction at your job when you work as a data scientist there's a good chance that you're being managed by someone else who might not be a data scientist and there's a good chance there will there's some chance that you're actually smarter or you have a better subject area expert eat a subject area understanding then this person does about data science the capabilities are acceptable and so there might be opportunities for any about heads because we might see something that your superiors do know and you know it's important to understand that not everyone is a scientist there are some business decisions that need to be made outside of the data science realm and that all these people at the end of the day are on the same team but if you're not prepared to work for someone might not be up to speed with all the latest and greatest techniques and have a fundamental or really advanced understanding of data science make sure when you're interviewing you're actually interviewing the interviewer or whoever's gonna be your boss so you can make sure that the knowledge matchup is something that you're comfortable with one more thing is that you know for me I found that a lot of the projects that that I've had to work on on a day to day basis they weren't exactly things that I was really and so coming out of grad school where you got to choose all the projects who are focusing on the work world the work as the data scientist is not necessarily like that you're gonna have to work on projects that you didn't start you have to pick up or further people already started it or you're gonna be told to work on things that you might not have a ton believe have a ton of merit so just be prepared for that you can make any project fun and interesting because again there is that autonomy element where you get to do it your way but you probably will not always work on projects that you are ecstatic about the last thing I'd like to touch on is the feeling that your work is not valued valued by business because data science is a relatively new field a lot of companies even really high tech companies don't necessarily know how to capitalize on their data science teams efficiently so your work you might do some really cool stuff and people very well might be too scared to implement it because it's new and they don't necessarily understand it so that is something to be wary of there's a lot of times where you work really hard on something you think you have an elegant solution but for example if you're using a neural net or a random forest where it is a bit of a black box that's very scary even though it might produce good results who might not actually be able to sell the the decision-makers on that solution because it's not super comprehensible by by humans or someone who is not initiated in the data science room so that is definitely something look out for and to keep in mind if you're considering this career path thank you so much for watching I hope you enjoyed the video please hit that like button if you did and if you enjoy content like this regularly please subscribe to my channel