 Hello, everyone. It's Monica Wahee here from our Tips and Tricks for Job Seekers in Data Science. If you are looking for a job in data science, you might have noticed that you tend to find yourself in a lot of interviews. Sometimes you are being interviewed one-on-one, and sometimes you're in a group interview. Either way, did you know that there are a few actionable steps that you can take to prepare for all these interviews? That way you can get good at having snappy, succinct answers for anything that comes at you on the fly. It is very important to prepare for all the interviewing you will have to do as part of landing a job position in data science. On my blog post, I talk about what I do to help my customers prepare for job interviews. I provide three tips. Here's tip number one. It is, make sure you have at least one project to talk about authentically. You will likely be talking about project experience you have had as a way of answering a lot of the interview questions. So that means you better have some authentic experience on a data science project, or you won't know what to say. Actually, you will want to have more than one authentic data science project that you really worked hard on to talk about, preferably several of them. So what are authentic data science projects? We do a lot of projects when we are learning data science, but we have to be careful to make sure that the projects we are planning to talk about in interviews are professional enough to count. So what counts? You might wonder if homework projects and courses count toward authentic data science projects. I'm talking about homework projects from college courses or courses from online platforms. Most of them do not count. The only ones that would count would be where you actually do your own analysis of a data set and write it up. Some college capstone or honors courses have you take a data set and invent your own research question to go with the data set. By the way, that's also supposed to be what you are doing in a master's thesis or when you get a doctoral degree. Then you independently analyze the data set and answer the research question in a report. Or you might find yourself in a course where you design your own survey and do your own data collection and analysis. So only in those circumstances where your homework involves doing a completely independent data project can it reasonably count as an authentic project you can talk about in your data science job interview. The other things that count as authentic projects are where you just independently posed a research question and answered it with data you somehow accessed, then wrote up your findings. Often this is called a portfolio project. I did an example for my learners on my blog. Let's go look at it. If you watch my other videos, you will realize I refer to this blog post often. It's for two reasons. One is that I made it specifically for my learners to show them examples of how to do a portfolio project online that looks very attractive to a prospective data science employer. The second reason I made it is, well, it's about casinos in Massachusetts and my FinTech data science friend and I want to contract with casinos and be promoted by the Massachusetts Gaming Commission to do their data science. We are women owned, minority owned businesses and huge casino fans. So I thought maybe if I started doing white papers about casinos they might hire us. No luck so far, but I'll keep you posted. Let's turn back to the features of this blog post example portfolio project I want to highlight for you. First, everything about this data analytics project is in the public domain and easily accessible. A prospective employer or the Massachusetts Gaming Commission, if I'm lucky, can just go to this blog and see the analysis. Second, I made a video of our findings. Before the Gaming Commission interviews me and my friend, if they watch this video, they will learn how good I am at data science and making videos that communicate what I did in my project. The question I asked, the methods I used to answer it, my results and findings, and my recommendations. Why not impress with all my talents? And the rest of the blog post allows me to demonstrate how good I am at explaining the business case behind the data and the analysis and showcase all my appealing and communicative visualizations. I also get a chance to show the Gaming Commission how creative I was with finding these public data sets, putting them together, then asking a research question and answering it with just these data available. I want them to hunger for more. Maybe then they will contact me and make a contract and share their private data with me so I can do some even more powerful analyses for them. Here I succinctly present my recommendations and offer them my white paper for download. Now they can see the entire range of my skills and abilities. And many of you know me only as a health data scientist. But now, after this project, I know what to talk about if the Gaming Commission interviews me. I admit, before I did this little project, all I knew about casinos and data was from studying gambling addiction. Not good for a casino job interview. And that's why you do these authentic projects. You want to have something to talk about in the interview. But warning, you really have to do an all-out project like I did with those casino data. There are a lot of things my learners think count as projects, but don't. These include most homework assignments and projects where you do not solve a problem independently. My learners bring up Kegel often. Kegel is good for data sets and use cases, but if you compare Kegel to my casino white paper, there's really no comparison. The amount of independent work you contribute to a Kegel analysis is very little. After all, they give you the data. Having to find and put together your own data is half the battle. Also, I see a lot of data scientists putting up dashboards and visualizations online. Those are pretty, but analyses out of context, meaning without a stated aim or question, followed by findings, recommendations, or some sort of application, isn't a real project. It's nothing you can really talk about in a data science job interview. On my blog post, I give you advice for getting started with setting up independent authentic data science projects for yourself. My next tip, tip number two, is to manage the interview from the bottom through repeating back the question the interviewer asks, but reframing it when you do. You want to read about this, but I'll admit it's hard to explain in a blog post. It's easier to coach someone to do this, which is why I have tip number three here, which is practice interviewing with someone. And when you do that, practice actually answering questions in real time, just the way you'd practice a PowerPoint presentation. Just do it with the questions. Here are some pointers for setting up practice interviewing. First, you have to find a pretend interviewer who will practice with you. I encourage you to find someone who is really invested in your success, like a colleague, teacher, or family member who really wants you to get your dream job. If you happen to know someone who is a manager who interviews job candidates who can do this for you, all the better. Then help them out. Get some example job position announcements that you would apply for. Write out some questions you think you will get and give those questions to your pretend interviewer. Also, prepare answers for the questions yourself. Educate your pretend interviewer enough on the topic and what's going on so they can do a good job role playing. I see this as analogous to scrimmages in sports. We often play against our teammates in practice to get good at responding during actual gameplay. With that in mind, we want your pretend interviewer to try to trip you up, put you under pressure, and get you ready for anything in your interview. If you look at my blog, you'll see I provide a few tips for preparing effective answers to interview questions. I give this advice to a lot of my customers and learners, but I will be honest, some have trouble applying it. If you have trouble doing independent portfolio projects, please contact me. We can meet for a free consultation and I'll try to give you some advice. Also, if you ever need anyone to do a mock job interview with you, please contact me and we'll set up a video chat so you can practice with me. Thank you for coming to my channel and watching my video. If you found it helpful, please consider liking it and even subscribing to my channel. Also, if you are good at job searches and you have any advice for people looking for data science jobs, it would be great for you to leave a comment with your advice so we can all benefit. Thank you for watching this video and I hope you have a good week and a lovely weekend.