You're never going to be ready
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Hey Reader, The other day, a coaching client asked me: "When should I start applying for jobs, I still don't feel I have learned enough?" I get it. The last thing you want to be is sitting like a lemon when the interviewer asks you what linear regression is. Trust me. I've been there. Your hands go sweaty Maybe not Bernie Madoff running a $65 billion Ponzi scheme-level of fraud, but it's close (although you are not ruining people's lives as Bernie did). There is a so-called "Goldilocks" zone where you have learned just enough and should start sending out applications. The problem is that most people overshoot this Goldilocks zone way too much and end up in the perpetual learning cycle, also known as tutorial hell. Do I need to take an AWS certification? Do I need to learn reinforcement learning? Do I need to learn Power BI?
These are just some examples I have been asked by my past coaching clients. The truth is, if you are questioning whether you have learned enough, then you have learned enough. People who doubt their abilities are often better than they think, and vice versa. If you want a more concrete criterion, then if you have taken fundamental courses in Python, SQL, machine learning, and statistics, you should start applying for jobs. No ifs, buts, or maybes. No matter how many courses you take or projects you build, you'll never truly feel ready. The secret is that no one else felt ready either, but they took action anyway, and now they have their dream data science job. So, start taking action immediately. That's how you actually make progress. Speak soon, PS: Speaking of action, last week I announced the launch of the Data Science Launchpad, a programme specifically designed to help you land your dream data science job. The response has been incredible! We've already had over 200 people join the waitlist. For this initial cohort, I am only taking 6 dedicated clients to ensure I deliver incredible results. There is still time to join the priority waitlist here to ensure you are instantly notified and secure a spot when it goes live this week! PPS: You guys would be proud of me, I split the g back-to-back-to-back on Thursday night. Some may call it a 3-peat? #legend |
Dishing The Data
Weekly emails helping you land a job in data science or machine learning
Hey Reader, Let me cut to the chase. You clicked this email because you want to know the best resources for studying data science. Well, like Sergio Agüero scoring the last-minute winner against QPR in 2012 to win Man City their first-ever Premier League title, let me deliver. The amount of books, courses and guides on data science has exploded over the last few years. Some of them are good, and some of them are dogwater. So, how does a beginner decipher what is what? The truth is, there is...
Hey Reader, Let me be upfront with you. I have some correct controversial opinions: I don't think AI is "that" great, and it won't replace data scientists Logistic regression is a regression, not a classification algorithm Lord of the Rings is absolute dogwater (and yes, I have read the books) There is one more view that I have (don't worry, it's not LOTR-related for you die-hards here). I don't see why people care more about their Instagram profile than their LinkedIn page. Think about it...
Hey Reader I am going to be honest with you. Most resumes suck. I've reviewed well over 100 resumes at this point, and it still surprises me how very intelligent people really struggle to craft a decent one. No matter how good your grades are, or how many internships you had or even if you created a robot dog that can walk up walls, none of it matters if your resume is dogwater (pardon the pun). I want to tell you the one most important thing candidates don't do, but it's something recruiters...