Image source: Wikipedia<\/p>\n This post is about how not to be the Halley\u2019s comet Data Scientist i.e. to keep yourselves motivated in your Data Science journey<\/p>\n The views expressed here are my own<\/p>\n Many professionals want to transition their career to AI.<\/p>\n But most will not<\/p>\n Often, we see a specific type of learner \u2013 who I call as the Halley\u2019s comet<\/a> Data Scientist<\/p>\n Like the Halley\u2019s comet .. they shine brightly for a brief time ..<\/p>\n Then streak on into the darkness ..<\/p>\n Never to be seen again for a long time<\/p>\n But periodically after a long time .. they reappear .. bright and enthusiastic<\/p>\n Only to disappear off again ..<\/p>\n And so it goes ..<\/p>\n Comets apart, we can see this phenomenon as a motivation issue i.e. instead of saying the learners are giving up on Data Science we could ask \u2013 \u201cHow could we stay motivated on the Data Science learning path\u201d<\/p>\n<\/p>\n Data Science is a hard and a complex subject ..<\/p>\n \u00a0<\/p>\n From my personal experience, here are things to know to stay motivated on your data science path<\/p>\n \u00a0<\/p>\n 1) Your information diet – Read and talk to experts<\/u><\/strong><\/p>\n One of the most motivating interviews I read is \u00a0Andrew Ng\u2019s interview On Life, Creativity, And Failure<\/a> \u00a0<\/p>\n In response to Can you talk about your information diet, how you approach learning? Andrew Ng says<\/strong><\/p>\n I read a lot and I also spend time talking to people a fair amount. I think two of the most efficient ways to learn, to get information, are reading and talking to experts. So I spend quite a bit of time doing both of them. I think I have just shy of a thousand books on my Kindle. And I’ve probably read about two-thirds of them.<\/em><\/p>\n At Baidu, we have a reading group where we read about half a book a week. I’m actually part of two reading groups at Baidu, each of which reads about half a book a week. I think I’m the only one who’s in both of those groups [laughter]. And my favorite Saturday afternoon activity is sitting by myself at home reading.<\/em><\/p>\n 1000 books and more papers \u2026 we all have a long way to go \u2013 but it shows you how much information you need to absorb<\/p>\n But the motivating idea is .. it\u2019s all there for us .. often free and from many people on the web who are genuinely helpful in sharing knowledge<\/p>\n<\/p>\n 2) Imposter syndrome in Data Science and how to handle it<\/u><\/strong><\/p>\n How to manage the imposter syndrome in data science is very much needed to stay motivated. HERE<\/a> is a good link on this subject<\/p>\n \u00a0<\/p>\n The way that I\u2019ve dealt with imposter syndrome is this: I\u2019ve accepted that I will never be able to learn everything there is to know in data science \u2014 I will never know every algorithm, every technology, every cool package, or even every language \u2014 and that\u2019s okay. The great thing about being in such a diverse field is that nobody will know all of these things (and that\u2019s okay too!).<\/em><\/p>\n \u00a0<\/p>\n 3 People claim to know more than they do<\/u><\/strong><\/p>\n In contrast to the Imposter syndrome .. there is a contradictory phenomenon ..<\/p>\n People will claim to know more than they really do<\/p>\n \u00a0<\/p>\n If you do not realize this, it can make you feel demotivated<\/p>\n Early in my career, I attended a talk where the speaker was very knowledgeable<\/p>\n He decided to explain a complex subject in layers of depth\u2026<\/p>\n He would explain to a point and stop .. and ask the audience .. should he go deeper ..<\/p>\n And what did the majority say?<\/p>\n Yes!<\/p>\n \u00a0<\/p>\n Every time ..<\/p>\n \u00a0<\/p>\n Did the majority really get the talk?<\/p>\n I doubt it<\/p>\n \u00a0<\/p>\n But everyone wanted to seem knowledgeable!<\/p>\n No one wanted to appear to be clueless<\/p>\n But at one point .. everyone was ..<\/p>\n \u00a0<\/p>\n 4 Passion is over rated<\/u><\/strong><\/p>\n Andrew Ng also points out in the same interview that Passion is overrated and says ..<\/p>\n \u00a0<\/p>\n \u201cI wish we as a society gave better career advice to young adults. I think that “follow your passion” is not good career advice. It’s actually one of the most terrible pieces of career advice we give people.<\/em><\/p>\n If you are passionate about driving your car, it doesn’t necessarily mean you should aspire to be a race car driver.<\/em><\/p>\n But often, you\u00a0first\u00a0become good at something, and then you become passionate about it. And I think most people can become good at almost anything.\u201d<\/em><\/p>\n \u00a0<\/p>\n That means that there will be a lot of hard work before you are passionate about Data Science<\/p>\n \u00a0<\/p>\n 5 Women in data science<\/u><\/strong><\/p>\n If you are a woman in Data Science, like in many fields, you face extra challenges.<\/p>\n why you should encourage your daughters to become data scientists<\/a> provides a good overview of significance of Data Science for women<\/p>\n \u00a0<\/p>\n 6) Offline meetups<\/u><\/strong><\/p>\n Offline meetups are great for learning complex areas. I started the Data Science for IoT meetup<\/a> and now we have grown to more than 2000 members. We have also launched an AI lab<\/a> spun out from the meetup. All these are great learning experiences but also a support group of sorts<\/p>\n \u00a0<\/p>\n 7) I get by with a little help from my friends ..<\/u><\/strong><\/p>\n I have a great network of friends who I can sound out to check\/understand technical ideas. Dr Kirk Borne, Cheuk Ting Ho, Dan Howarth, Sebastian Raschka, Dr Brandon Rohrer and others. Everyone needs such friends!<\/p>\n \u00a0<\/p>\n 8)\u00a0 Learning to handle negative experiences<\/u><\/strong><\/p>\n As a Data Scientist, you have to often learn to handle negative experiences to stay motivated on your path. For me, these have included: companies who did not have data but wanted to create a Data Science algorithm, companies who over promised to investors, companies who wanted to use specific tools just because they had done so before, companies who refused to consider a Cloud based model even when they had no resources in house etc.<\/p>\n \u00a0<\/p>\n 9)\u00a0 Persist in coding<\/u><\/strong><\/p>\n Persist in coding .. again many ways to achieve this for example #100DaysOfMLCode<\/a><\/p>\n \u00a0<\/p>\n 10) A vision of the promised land \u2013 quantitatively \u00a0..<\/u><\/strong><\/p>\n You could create a set of quantitative goals over a period. Say a number of applications in github over three years. You could also focus on a vertical by choosing datasets in a vertical. This is all achievable even with very limited resources. \u00a0<\/p>\n \u00a0<\/p>\n And there is one more way to motivate yourseleves ..<\/p>\n<\/p>\n Its Fear ..<\/p>\n<\/p>\n Like it or not AI and Data Science will impact many jobs -a study finds that up to nearly of jobs are vulnerable to automation<\/a>. That\u2019s enough to keep most people motivated.<\/p>\n \u00a0<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n<\/p>\n
Introduction<\/h2>\n
How to stay motivated as a Data Scientist?<\/h2>\n
Conclusion<\/h2>\n