Craft your Attention-Grabbing Resumé for a Career Change into Data, Tech, and AI | Jesper Dramsch, PhD | Skillshare

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Craft your Attention-Grabbing Resumé for a Career Change into Data, Tech, and AI

teacher avatar Jesper Dramsch, PhD, Scientist for Machine Learning

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Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Watch this class and thousands more

Get unlimited access to every class
Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Lessons in This Class

12 Lessons (1h 7m)
    • 1. Introduction

    • 2. Class Project

    • 3. What makes a good CV?

    • 4. Where to find Resume Templates

    • 5. Structuring your data CV

    • 6. Executive Summary

    • 7. Professional Experience Section

    • 8. Education Section

    • 9. Project Experience Section

    • 10. [Work Session] Craft actionable Resumé bullet points

    • 11. Filling the Skill Gap

    • 12. Conclusion

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About This Class

Are you looking to transition into a career in data science but unsure how to showcase your skills on your resume?

This class is designed for professionals who have important tech skills but need help translating them into the "tech lingo" needed to land a data science job.

Changing careers can be overwhelming, but many people don't realize how much relevant experience they already have. This class will help you identify and highlight your transferable skills on your resume, making it more appealing to potential employers in the data field. Whether you're interested in data science, data engineering, business analytics, data analytics, or machine learning, this class will provide valuable advice for all of these areas.

As a bonus, this class also comes with a free mini e-book, "Craft Great Resumé Points for Data Jobs" that will guide you through the structure and tips for each section of your resume, including:

  • Executive Summary
  • Professional Experience
  • Educational and College Experience
  • Project Experience

We'll also cover key skills you need to add to your skill set in addition to the knowledge and data skills you already have. By the end of this class, you will have a polished resume that will help you stand out from the competition and land your dream job in the data field. Enrol now and take the first step towards your career transition!

The project section contains the e-book "Craft Great Resumé Points for Data Jobs"



Who am I?

Jesper Dramsch is a machine learning researcher working between physical data and deep learning.

I am trained as a geophysicist and shifted into Python programming, data science and machine learning research during work towards a PhD. During that time I created educational notebooks on the machine learning contest website Kaggle (part of Alphabet/Google) and reached rank 81 worldwide. My top notebook has been viewed over 64,000 times at this point. Additionally, I have taught Python, machine learning and data science across the world in companies including Shell, the UK government, universities and several mid-sized companies. As a little pick-me-up in 2020, I have finished the IBM Data Science certification in under 48h.

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Jesper Dramsch, PhD

Scientist for Machine Learning


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1. Introduction: Transitioning your career is scary. It was the scariest thing I've done in recent time. And honestly, I could've used a lot of guidance. That's why I'm here. My name is yesterday. I am a data professional. I work as a scientist for machine learning, have experience as a Machine Learning Engineer. And I teach data science on Skillshare. I have taught data science in Fortune 500 companies like Shell, the UK government, and in universities. I now work with machine learning and data every day. And this transition out of oil and gas into something with data has made me very happy. I know that a lot of people are looking for jobs and tech. And I want to make this possible by taking your experience that you already have as an applied scientist or as someone that worked in academia. And translate that into something that will get your jobs in data professions. Whether it's data engineering, data analytics, data science, business analytics, or all the other jobs. I want you to go away from this class and have a CV or resume and get a job with that. 2. Class Project: Welcome to class, welcome to the other side. In this class, we'll have a look at how we can translate your specific experience that is outside of data science, outside of the data professions. And really get that into something that will hopefully get you a job in tech. For this class project. I found it a little bit difficult because I'm aware that you are translating your personal experience into something data. And I think this can be a bit uncomfortable to share. A little bit too much to share sometimes. Yeah. But I want you to do a project. I want you to finish this course. And also, if you do a project, this means this gets shared with more students and recommend it to others. If you do a project, essentially what I want you to do is take one example of an experience that you have and use the methods that I teach in this class. What we'll go through a specific way to write bullet points for your CV and translated for data professionals. And take what you had before, how you kind of describe your job before. And then share with the class how you describe it after just one bullet point, put it in a little picture so you can upload it to our class Products section over here. That would be amazing. Then also others can get some inspiration of what you did and how you translated your experience. If you want feedback on your class projects, you can write that in the comments, then other students can have a look. If they find some other way to translate it. If you don't want feedback, that's totally okay. But yeah, share your project, share your kind of translation, how you got something that you did before that wasn't data specific and translated into something that data professionals will absolutely understand. Without, Let's have a look what a good CV looks like. 3. What makes a good CV?: One of the questions we have to answer here is, what is a good CV? And this is a bit difficult to answer because there are three entities that we'll take a look at your CV. The first one is going to be an automated system. Most companies use these. It's an ATS or applicant tracking system. This one is checking for keywords and they are very annoying, but they used everywhere. And it's really important for you to, they'll match this. There are websites online that essentially used this ATS system that check for you how your CV or resume is evaluated according to ATS. And I highly recommend that you use these. Once you've set up your CV. Then it goes to recruit. Someone in HR is having a look at your resume as having a look if the ATS is doing well, did a good job, and is going to evaluate you based on the job description that you applied for. So here it is good. Hit the relevant keywords. And oftentimes also match keywords that are actually in the job description. So if you are working with the Microsoft Office Suite, so Excel Word PowerPoint, and they are asking for XL specifically. You shouldn't just use MS Office in there. You should also use excellent there. And this is just an example. There are so many different, many different softwares that are very specific and recruiters don't always know that this is part of that software suit. What Microsoft, it's easy. But how about Python? We're saying we can work with the Python psi pi stack. That is a word that you can know, like not even all data scientists know what that is supposed to do. But a lot of people will say Yeah, but this encompasses NumPy Pandas and often scikit-learn and of course I pi. But if they're specifically asking for pandas experience, then you should write that you'll have pandas experience in your CV. The recruiter will also look for your experience. Obviously that the numbers match up and everything. Here, it's important that you match your local standards. For example, when I was still applying for jobs in Germany, Germany needs you to have this is changing but often still needs you to have a photo on your CV. If you apply to English speaking countries like in the US or in the UK, this will actually put you on the discard pile right away. In the US, specifically, if you attach a photo of yourself, you will often land on the discount pile simply because this will avoid will accompany that way you can avoid a discrimination lawsuit if they went ahead with your application. It's a bit silly, but look at your local standards. So if I'm giving you some advice and your local standard is something different, then definitely take the local standard because you have to get past the recruiters first. And then it goes often goes to a technical person, someone in the company that actually does the job, usually a little bit more senior. Having a look at your CV and checking if your CV make sense. If it's a good fit technically, if your formal positions and if your skills that he lists do make sense in the position that they actually need. So essentially what you're doing is writing a CV for three people, which is incredibly hard. Which is also why you have to apply to so many jobs today and it sucks, but we'll do our best to get this around. So a good CV is hitting those three points. It makes sense to a technical person. It makes sense to recruiter that is mostly looking at keywords and at the formalities. And at times prestige, all those things. And it goes through the applicant tracking system. And our next class, we will have a look at how you can make a nice savy and templates. 4. Where to find Resume Templates: Now that we know who we're writing the CV for, we can choose our formatting appropriately so it gets best passed in an applicant tracking system. It still looks nice for the recruiter and it can contain all the necessary information for the technical person. So I have a couple of recommendations here. Take something that is fairly simple actually. So recently there was this trend of having these really beautiful, visually appealing CVEs that have like skills and little button boxes, or have even like little points scales next to, next to you the skills and put everything in two columns, sometimes even three column layout. And I highly recommend against those. I recommend you take a single column. Nice CV, often written just in Google Docs or Word or something like that. And really stick to the simple. So even the applicant tracking systems that aren't that good can pass your CV as good as possible. Because then you send them this PDF and you have two columns, so it's next to each other. It doesn't know that the column is there, so it keeps reading and methods everything out that you have in there. And then you add kick to the discard pile despite having all the skills and all the experience that is perfect for this job. So stick to a single layout. And let's have a look where we can get templates because you don't have to write everything from scratch. You can use help. So the first one is right here. This is Google Docs. You can simply use one of these templates. Change some of the colors so you like it, change the font so you like it. Don't get too artsy, don't get too fancy. Keep it professional, but these are good. These are excellent. I have my CV from one of these templates. So the second one is going to become, now Canva gives you a lot of flexibility, which is one of its downfalls. It's more for the artsy people. But you can find some good CV template, resume templates in here that are pretty, that are often a little bit easier to use for a lot of people. And you can get a real good CV out of this. So choose one of these and then translate your experience. Maybe you have it on LinkedIn into something that is appropriate for a data professional. And this is what we'll get into now. Now that we know the form factors, we have to think about how to translate your experience into something that a data professional and the recruiters in the field can actually understand. 5. Structuring your data CV: Let's talk about the structure of our CD. This one's a little bit interesting because it varies locally. So if you have information that is from professionals local to where you are, like whether that's the US or India or somewhere here in Europe. There are slight differences in having a CV, so definitely be aware of this. And then then we can go ahead. The local information is usually better than what I have here. I'm going to talk about general ideas. So when someone has some local information that is more important, like I said in Germany, a lot of times you have to attach photograph, which is absolutely not okay in the US. And a structure here usually is that you have executive summary in the beginning. Now this is a little bit controversial. I've heard a lot of people that don't do this, and a lot of companies that don't like this, recruiters either love or hate them. But like an executive summary is a great place for you to say, this is who I am and this is why my experience is relevant to this, especially if you're someone that is changing careers into a data profession. This is a great place to kind of give a little bit of context for your CV. Now of course, your cover letter can give a lot more contexts, but a lot of times the cover letter is skipped. And people look at your CV first, so they have an idea of what your skills are and then go to the cover letter. So giving a little bit of context, oftentimes a really good idea. But once again, listen to your local, local information because that is what counts where you are. They don't do executive summaries and don't add one because this will probably put you on the discard file, discard pile. When recruit actually has a look at it. Then you have your professional experience and your education. And here it depends which one you did last. So if you're already a seasoned professional with a lot of jobs under your belt or a few jobs under your belt, this should come first and then education, if you're fresh out of university, education usually comes first. The order changes depending on how much and what kind of experience you have. Within these. You want to have it chronologically ordered, but descending the newest 1 first, like whatever you're doing right now or what you've just finished has to be all the way on the top, and then you add whatever below. Here's the thing though. Not all your experience is relevant to this position. Especially if you think about that you are changing into a data proficient profession. Not every job and every degree you hold is going to be relevant to a data job. You can make them sound like that. Definitely, you can change them in that. And that is something we go into Definitely because this is one of the easiest fixes. I see an a lot of CVs. But just be aware that sometimes you don't have to add all your jobs, especially if their internships or if the smaller side gigs. You don't have to add them all. The most important part is, and wherever I was in the US, in the UK, in Germany, I've never seen a CV over two pages, unless you're an academia where they get really long. But usually leave your CV even if you're a seasoned professional, like a senior, whatever you did, usually you want to cut it down to two pages and only leave the most relevant information in there. They can always go to your LinkedIn profile later and have looked at it. Ask you more questions, especially if they're asking questions while you have gaps in there. It's like, Oh, I had a job there, but I didn't find that this was so relevant to this position. That is completely okay. But sell yourself with the most relevant stuff. I do that myself like I don't add all the jobs I have. If I have degrees that aren't relevant, maybe I mentioned them but I don't like ad but it didn't those degrees. It's just the line. And also in the next section that is really important, the product experience only add the most interesting, most relevant stuff in there. So this is really a whole section that will go into as well, but your product experience is extremely valuable. This can be different parts. So if you did my other courses here where we do build products, you can include those other courses like on Coursera that have certificates. You can add those. And of course, open source contributions as well. Let's go more into detail what you can do in each section, in a separate lecture for all of them. 6. Executive Summary: Starting out with the executive summary. Executive summary is really a few sentences about what you want to do and how your experience that you have from positions degrees is going to influence your well, your suitability for this job. Really what you want to do is write a few sentences. How everything you've done relates to the drop description that you read. Here, it's really important that you related to the job description and to the job that you are applying for. And it should be fairly short. Like advice I heard is keep it under three lines. Really have it skimmable, like a crispy, nice short paragraph where you write, I'm applying for this job. You don't write I'm applying for this job, but I am looking for this in data science because I liked doing this. And this is my experience that is super relevant for it as a geoscientist, which is my background all the way in the back. I was always formulating it in a way that my experience with real-world data is influencing how I am great data scientist or a great machine learning engineer. Knowing inversion from physics is great for a machine-learning position. Really help recruiters and technical people get context for, for the position because they don't always know what place you, especially if it's your first position or where you're changing into if your data profession, you really want to help people understand how your experience really relates to this job. You may be the odd one out, but you can make it easy for a recruiter to say, Okay, I'm going to put you forward because I think there's something in here that we can work with. And you'd be surprised. There are so many people that have experienced in some of the applied fields like biology, ecology, geology, all those ODEs that really, really give you relevant, relevant experience with working with real-world data, with messy data, and a lot of data scientists and machine learning people love this experience. So don't sell yourself short. You can definitely find a position. And this executive summary, if it is used wherever you're applying, applying for, is really a great way to get your first foot in the door and say, This is why I'm great for this. This is the context for my experience. So let's go on to the professional experience now and have a look how we can reform it. A couple of experiences for a date profession. 7. Professional Experience Section: In this section for your professional experience, you put the jobs that you help that are relevant for this job. Usually that should be your last job, then whatever is relevant before that. And here, what I like to do is have a little won't have all the details. So what position where you how long from when to when? Include a month and the year. So people can deduce how long you were there for and then also the company. That is my heading. And then under that, I put bullet points. And there's a really nice formula how to make these bullet points really stand out to recruiters. So the way this works is you start out with an action word like produce or developed, or really led. Things that are powerful action words that are like, oh, you are someone that is doing things. Really be, be creative there and try to mix them up as well. Don't be like produced, It's produced that developed this, developed that. Try to be a little bit creative with it. So you use a different password for each bullet point, and then you want to go, action words are developed a thing. Developed open-source software x. Then to solve, why did thing x to solve y? And then you want to say achieving that developed open-source software x to solve the needs of the stakeholders and achieving whatever that is. And here, the achievement, like for me, it's really hard. I come from academia. So it can be really, really difficult to translate your experience into something tangible, into something where you're like, Yes, I achieve this thing. But if you can try to put a number, is really good. Achieving if you saved money, that is fantastic. If you sold more, that is great. But also like since I came from academia, something measurable is that many publications. So if I, if I worked in a certain postdoc or something like that, I can really go into detail with. I analyzed the data of that to do this. And this resulted in five publications. That is fantastic. So this is really putting it into context, how productive you were, what you achieved, and what you did, for which reason. Here, you can use a couple of tricks. These tricks on disingenuous, these tricks are legit, but you have to translate your experience into something that both fruits and technical, personal will understand. Keep jargon to a minimum. But translate your experience into a data analysis framework. If you work with Excel, that is fantastic. Did you do any of the formulas that they have equations, however, they're cold and your your version. That is good, that is already experience in data processing. Did you do any wrangling of data where you got data per email or over some stream or wherever. That is great because you were essentially doing data pipelining and data cleaning. If you extract the data from databases and then changed it into different formats, help load data into software. This is commonly referred to as ETL, Extract Load, extract transform load. And really those are terms that you should try to somehow get into your description if you did them here. Now it can be a little bit difficult because you don't really know what these people expect. But if you have any analysis experience doing any analysis and specialized software or an Excel, that is fantastic, right? That you don't even necessarily have to write the software name because no one's coming. No. But you can say in specialized software, analyze data to achieve this in software x and achieving that. And then. Also, if you did any visualizations, that's fantastic. Visualizations are stakeholder communication. You can also talk about, first of all, your data visualization because this is a great skill. You can also talk about stakeholder communication because this is an important skill that especially data scientists that come out of boot camps that are fairly junior don't have, they don't have this experience of communicating difficult results, difficult data to stakeholders that come from all kinds of different areas. So those can be technical as well. Those can be C-suite. If, if you can translate your experience in a job into, into this, this wording that recruiters understand that will change everything. Because suddenly your job working as a biologist is a data science job because essentially you already did this. If you worked with your specialized software to analyze the data, then right at that way. But also if you, well, if you had to get your microscope data in a weird format and change that into CSVs. You can load it into XL or however you did your data analysis. This is already really valuable. Even if in that job you didn't use Python and Pandas and NumPy and only learned that in your free time with your products. That doesn't mean that this job is entirely irrelevant because it's still taught you how to deal with this messy real life data. And a lot of ways taught you communication with relevant stakeholders, like your lab had, or with professors, or with your managers. This, this experience isn't for not a lot of ways, you still have to translate it. And like I said, keep jargon to a minimum. If you did certain lab cultures, I don't think it's that relevant to put what kind of cultures, but it is relevant to put the analysis that you did with them. So don't don't go into too much detail because this will have most people checkout that aren't in the field. This is better for if you have a really relevant I'm job in biology then of course include this. If this is going into a data science position in biology and biology experience different story. But if you're going into like a general data science position, then keeping a little bit lighter on those deep details and translate your experience more into something that these recruiters and technical people on the data science side of thing Understand. Next we'll have a look at education, which is fairly similar. But still we have to translate that as well. 8. Education Section: Let's talk about the education section. The thing with education is that it's highly individual. So even if you think people know what you learned in a certain degree because you spend a big chunk of your life in it. So it's second nature to you to know what is in this degree. And you think everyone knows, especially if you're from some prestigious university, you think it is implied about your null. But it really helps to tell people what you learned there. So what I like to do, especially if education is my last experience, tell people what you learned. So I did my PhD as the last thing when I had to write a CV for that position. So I was like, I did this analysis. I apply these kinds of softwares and I rode Python packages. And I had this number of publications out of this one book chapter. And I was really productive. So it was really helping people see the kind of impact that I had. Especially with PhDs. People usually have no idea what you're actually doing an a PhD. And the thing is also, it's very variable over different, different countries. Every country has different prerequisites for you to get a PhD. If you're in the US. It's so, so, so different from Germany, it's so different from the UK, which is again so different from Denmark, where I did my PhD. Really tell people what you did. Did you get teaching experience? Great. Put that if, especially if it's relevant to that job, if you did presentations, here's the thing. A lot of people, when they hire junior, junior folk, they don't know that you have extensive presentation experience. And especially if you're a PhD who was presenting work at workshops and conferences. This is actually really valuable because people that go the more normal route, the, the path well trodden. You don't really get the chance to present that much until you are really in a job where you have to check out what kind of experience you're half. And there are fantastic videos on how to translate. Well, how to translate your experience, especially in education, into something that people in the industry will understand. Because like I said, most people have no clue what a masters, especially research masters and a PhD and tail help people put bullet points, use the same kind of method that I described the professional experience section. Here's an action word, thing x, using why. Achieving that formula is really, really good. You can also put a couple of courses, but the titles of the courses should be descriptive. And again, keep it on the light side with jargon. Really, if you did presentations, That's fantastic. Teaching experience is really great because that means you can communicate and you're not just some lofty scientists in the ivory tower. So really think how you can make others understand what she did, even if they have a good idea. Try to step it up a notch and make it really easy for recruited to see this and say, Wow, I had no idea, this is really, really good. And this is individual to the kind of job we have. If you did analysis during your research, if your data collection as well, this is great. And most people who have no idea that you do, like the cliche is that you're some person just reading, reading, reading, and then writing about something. That's it. That's the impression that people have of university. So, yeah, sitting in lectures obviously. But the higher you get in university, the more wrong this assumption is. Yeah, I know I'm repeating myself here, but help people understand what you did. And use this formula with the action word to get bullet points that people understand. And again, it's the best if you only add relevant experience. But if you don't have that much experience, especially if education is the last thing that you did. It's fair to kind of expand on your PhD, bachelors, masters. So people can see you what kind of experience you gain that if you did field work, all those interesting things. So that's the application section. This one will get smaller. Usually the longer you are in an industry position, the more positions you have had in the industry, people tend to reduce their education section. I think in my last CV that I applied with, I actually put my bachelor's and my master's into one bullet point, essentially just saying I have those, then not that relevant here. Look at my professional experience and yeah. It's all a little bit fluid. Depending on your experience, really play around with it, whatever makes sense, and send it to someone that doesn't know what you did in your PhD, which is probably almost everyone that you know, like a family member, a friend outside and see if they can understand better what you did in your PhD. If they have questions, The recruiter will have questions as well. So take those as feedback and explain better what you did and really get a nice feedback loop going And that way and improve it so people can understand how, how you experience relates to this data professional right here. In our next section, we'll have a look at products which is going to be extremely valuable and a little bit different from the usual savy advice that you see on more corporate positions. But it's extremely important. So don't skip the next one. We'll have a look at projects. 9. Project Experience Section: This section is about projects. This is one of my favorite parts of a CV. I've probably hired the most on this one. This was the biggest decision because a lot of people, like a degree can mean a lot of things. Position can mean a lot of things. But if you have a project, you have something very tangible, something that people can see, often provide a link and really understand what you have done already. What do you have experience with, maybe how your code quality is as well? So I like to do is look at my past, what I've done, whether it's research projects or hackathons or contributions to open source. And dig through what you've done already. Even if it's at another job and it was open. This is project experience. So if people can see it somewhere or if you can talk about it and it's not under NDA, this is great because this is tangible experience and gives context for your knowledge and what you've done. So oftentimes I like to have a look at some, some experience with coding, some software experience, whether that contribution to an open-source project or a small project that you did yourself. This can sometimes even be finishing project for the course, but I wouldn't not always recommend this. I actually have a YouTube video on this of projects that I recommend not doing. So have a look for that. But essentially, if it's something original, you found some data that is really interesting, or even some data from yourself. You analyze that data, you make like a nice visualization. Or you presented somewhere. Maybe it's even during a hackathon, then this should definitely be under your project experience. There I usually put the project name, put the year where it happened, and then write a little sentence about what this is and how this relates to being a data professional. And this is really, for me, this is the most interesting part in a CV. It's not always standard way. A lot of people also have looked at status. So did you work at Google? That is probably really, really impressive. But knowing how you work, providing links to your GitHub or maybe to your Kaggle. Where you have one, something where you have achieved something where you have published something that is really, really valuable. For a lot of people can be difficult finding projects. I've heard from a lot of people that they haven't done projects. Oftentimes that's not true. Most people have some kind of project experience. Now, one question is whether you can talk about it. If it's for work. Check if there's like a non-disclosure agreement. But usually you have some thing that you've done for something. So students often have class project. When you get to a point where you have more project experience, those probably fall out first because they're too simple. But yeah, when you've done something, when you've participated in events, there's usually some kind of project experience that you can put into a nice little package for that. So go through your past and be open with what might count as some, some products that came out of it that is now existing and that you can show to people are explained to people. Not everything has a link in the Internet, I'm aware. But if it's a little checkbox that you worked on, if it's a data scraping project or any of those kinds of things where you just did something for fun. Is that an automation project like you amazing at using Apple's shortcuts. I know this is a little bit funny, but essentially if you get some kind of data, which is sensor data on your iPhone and you have some really cool shortcuts that are running on that and automatically triggering other things. This could be a small project until you have bigger ones that you can show to people. But this kind of automation shows that you can build pipelines out of, out of real-world data source. Now, this is fairly simple, I'm aware. But especially for junior positions, we don't have to have these huge projects that would be worth millions because then you don't have to apply for a junior position anymore. Go through your experience. And when I say be creative, I'm not saying be creative with the truth, but I'm saying be creative with what you count as a project because I'm sure there is a project that you have that you can put on your CV and probably more. So really go through, have a look. Is there any code and the visualization, even if it's an Excel, that's okay for the beginning. And put that there. In our next class, we'll have a short discussion about skills that are really important, that you might still have to do a little bit of learning, especially if you're coming from one of the more applied sciences, but which are fairly easy to learn if you have a good basis already. 10. [Work Session] Craft actionable Resumé bullet points: It can be difficult to be specific and find specific examples. So in this small lecture that I'm actually recording after, but I wanted to add this to make it really usable for you. I want to talk about translating my old Cv into something new, using these action words actually created a little, little e-book that you can pick up in the resources section, then pick out my friend action words. And C also links to more action words and some ways to differently do this kind of CV and resume work. And those lists have up to in 200 words and I picked out my favorite ones in different sections. I basically split this into four sections. So general responsibilities, then, your technical and data experience, and of course, leadership and management and stakeholder communication. And let's see how we can translate some of these skills into that format of action skill thing using why achieving zed in our general responsibilities. If we don't want to make our CV too technical or too focused on different things. Here we can talk about how we expanded the documentation, for example, for my teaching skills. So I expanded the documentation of teaching material. This guilt right there where with Jupiter to accelerate on onboarding of new teachers. So now you can see how this is very clear what I did. Of course, there's a little bit overlap with communication here. But yeah, really see what, where you put everything and you don't have it's not that key account to be quite honest. So what else did I do during my time as a post grad and as a PhD, I did organize the journal club, for example, the journal club. What we can go for years to disseminate knowledge and facilitate cross disciplinary. You could also say across department. I'll think that is a little bit more broad, a little bit more like talking about oh, yeah, I got all these different departments involved. Then of course, what did you achieve? So I don't know, in this journal club, I think a lot of people learned and networked. And I would put that down actually because this is quite tangible. Why do you do a journal club? Well, people learn and people can network. So this is how you can take some of them are general things. And when we want to go technical as simple one right here, that you can take a refactoring. So if you have worked with code before, that is a really good example how you can translate this. So you can say reflected existing code phase using pytest, like eight example. So those are two Python libraries that are good for code quality. One is for testing, the other one is for linting. So making sure everything looks nice and is in a format that works. And what do you achieve well? Test coverage, hopefully, and consistent code quality. So this is a really good one because this shows that you are hands-on. Then you can also talk about how you install things. If you did that. So installed updates, plug-ins for Excel using the Homebrew package manager for example, if you use that or whatever you're using in your company. And what did you achieve? Well, this case, I think with X0, oftentimes you install these plugins to get some kind of functionality out of it. So it runs faster, runs better. And I'm specifically using Excel here too, because most people forget that a lot of work with axle is also data work. Really see what you did that if you worked with some equations, formulas, if you did some data cleaning, shows that you did some teamwork enter through work together, and that you have technical skills that pertain to data cleaning and data wrangling in Excel. But this knowledge is transferable, right? So this is kind of telling people what you did if you just, just ride used Excel. No one really knows what you did if you write a sentence like this. Oh yeah, I installed updates and plugins to do this using this. That really gives you the same keyword. Another keyword if that is necessary. But also these keywords. Data cleaning. You can also do data wrangling here if that is in adopting description, really expanding on these bullet points is great because otherwise you just have these two words right here. I'm fairly certain I have some of these like I could have really expanded on these and made my job search much easier by maybe reducing some of the jobs I have in here. Yeah, give people an idea of what you're doing and then what we can do. Let's have a look at some leadership. Something good is always to have hiring and experience. So hire new staff. Evaluating the CDS if you didn't. And cross-checking project experience, which is something that I did in my last last drawback, actually resulting in two new June your highest. So this shows that you have leadership experience, that you have hiring experience, and that you are more on the SR side of the spectrum. Other ways. You supervised anyone trained anyone, that is great right here. If you cultivate it. The the experience for people so cultivated like a coding, get together. This was something really nice that we had at Harriet right here. At the ELP. Cultivated a Python Meetup to share, share common problems. Daily work with Python and associated packages. Together we solve multiple problems, solving, um, multiple sticking points and problems. Which is really nice if you are the kind of person that gets people together. I mean, this is replaceable. If you don't have that Python experience in a job, maybe you have that Python experience outside. You attended a PI data meet up. Or if you have some, some internal like lunch and learns something like that. These are really great if you have this kind of experience. Yeah, I'll go into what you did in your job and really suss out some of the things. I hope this is some kind of breadth and on different skills that you see. So these are more technical, these are more general, and these are more in the leadership area. And yeah, don't forget that X is some basic data, data experience, but you can translate it to make people understand that you already have some data experience. And yeah, if you, if you worked in the field, then you know where the datas from. So for example, formalize the data acquisition pipeline. In the field. Pulling two teams needs. Data processing needs. Achieving higher overall data quality. When you are at any end of this data processing spectrum, look into it if you can find some of these things. If you are in the field, if you are working with people to standardize how data is reported, you can make this into a data CV and give yourself a leg up. Make yourself more interesting to recruit us rather than just saying, oh yeah, I did some field work because no one really knows what field word means. Make people understand what you do. I hope these examples help. I'm putting some of these examples into that e-book. So definitely check it out. It's free, it's in the resources section. And yeah, I hope this is as applied as you need it. So onto the next lesson. 11. Filling the Skill Gap: Let's talk about some skills for data professions that are very important. These can be skilled that you don't have yet, that you still have to learn. In addition to having experienced with real-world data. You can, if you want, you can skip this part because each, well, if you're reading job descriptions, those are obviously yours, your source of truth. This is only an idea of some of the skills that can be really, really important that I've found in a lot of job descriptions. The first one is Python, libraries in Python that are used in data science and data analytics. So this is pandas, numpy, and often times Matplotlib or seaborne, and some basic machine learning experience using scikit-learn. Having those on your CV is quite valuable. Building a small project that kind of showcases these technologies can be really good and it's great having on your CV. You can also mention these technologies on your CV. And that way you can hit those keywords for the ATS and for the recruiter. Building a small end-to-end product is a good idea to hit those skills. And of course, if you are applying for a more software development heavy job, It's great to have experience contributing to open source software because those are oftentimes tested and well-documented and people can find it and have a look at your contribution. Here. It's really great if you, if you expand on your Python knowledge, either through a project or through a contribution. It really depends what is the focus on the position that you're looking for? Then some jobs are looking for our experience. I haven't applied for any of those. But if this is a job requirement in a lot of jobs that you're going for. This is obviously a language that you should learn. And especially the especially jobs that are smaller in a lot of ways that don't need that. Heavy TensorFlow or PyTorch in bigger machine learning systems are as great. So the next one that is more generally really important is SQL or SQL, which is language to get data out of databases. So this can be a little bit confusing because there are courses with certificates from in Oracle and Microsoft and a lot of other places for database administration. This is little bit different because there are the systems that store databases. And there is the witches often like Postgres, postgres SQL or MySQL and all those different technologies. And there is the querying language, SQL, which is to get data out of them. This one is fairly universal and this one for the administration, which is more on this is probably more on the side of someone that actually is the database administrator. You want to learn the language, the language to get data out of systems in businesses. If you're a data scientist that is applying to a classical corporate job, this is really important because in most companies, you have a SQL database somewhere and retrieving that data to be able to do your analysis on that data. Whichever system you use after that, whether it's Python, whether it's, you have to get the data somehow. Knowing SQL is really, really important here. I'm not sure if it was a requirement in my jobs per se because I was more on the machine learning side. But regardless, I have it on my CV because it shows that you know how to get your data from a certain place. And it's so important. That is a skill that a lot of the people that come out of the sciences don't usually have because our data is coming from somewhere else, not out of a database. So having a specified course that teachers use SQL is valuable and definitely something that you should consider investing in. I personally like the free courses on cattle to get you started. They have to, but there are also courses here on Skillshare and you can also get certificates and app. But like I said, there are, there is a lot of confusion whether it's SQL course or it's a database administration course. So be aware before you pay a lot of money for these certificates because you might end up in the wrong course. Then you can basically expand around what you find in different positions, the data professionals growing. So that is the data engineering part, which is kind of building pipelines between data. So if you have experience with Hadoop, Spark, if you have experience with darker or airflow, these kinds of things that help you make computation possible and also data pipelines possible that is really valuable there. Then you have data analyst position where Excel Tableau, those technologies are quite valuable. Then you have data science positions where oftentimes you go more into depth on then also seaborne and stats models. So hypothesis testing packages that you want to be able to use and all the way to machine learning engineers that have to be able to use also darker oftentimes. But TensorFlow, PyTorch really have a look what kind of position most aligns with what you wanted to do, and then learn those skills. Each part of these professions has different skill sets. And it's only recently starting to kind of separate a little bit. I've had a lot of positions where data scientists and machine learning engineer was used as the same word essentially. And it's really not, those are different professions. There was a different goals, but one is paid less than the other. Sometimes it's described in the wrong way just to get someone for cheaper. Be aware of that. But essentially see what kind of position you want to work in and then acquire those skills. And of course, this list isn't complete, but it gives you an idea of what you might look out for and really scan those job descriptions so you can learn the skills that you need to get that job. Because I'm aware we don't have all the skills necessary and the data professional. But this is the next step after translating your experience. And especially if you're applying for a junior position, you don't necessarily need all those jobs. You might already get lucky with your experience and they might say, You don't know SQL, that's okay. We're going to accept that, put you in a cause for this. Because SQL is really easy to learn quite frankly, especially if you already know programming. It's very, very easy to understand. But it's a skill that a lot of people asked for. So you already having that skill because you already took a course for it. Maybe did a week of work with it, have a project with it. Then you're already one step up. And then you already have, in addition, you have your really awesome applied knowledge with real-world data. That is how you upskill, get those high-value skills and add those on your CV in form of projects, experience, courses, certificates. And that part is a little bit less translating and more filling the gaps because of course, there are gaps. You haven't done this job before. But most of data science, you can learn for free. A lot of the skills are here on Skillshare as well. Look for those courses and see what people are asking for in these job descriptions. And try to up-skill. You don't need to hit everything. But if you have looked at ten different job listings and you see what is equal in all of them, then you know, that is one of the high-value skills. Get that skill. And you'll see v will land much higher on that pile. And you're much more likely to get a job, especially after you translate it, your other sections into something that a recruiter will understand and the data professional will understand and get you in that job. So that's our entire receive. Keep it under two pages. We'll get right into the conclusion. 12. Conclusion: That's how the class, we went through all the sections in a CV. How to translate your specific experience into something that will hopefully get your job. Remember, getting a job is really hard. It's hard work. Don't let anyone else tell you. I I have a pretty good CV. I'm sorry to say it like that, but I find my CV pretty impressive. And most people mural that. And still I wrote hundreds of applications because sometimes my CV just crashed and burned in the ATS. Sometimes I didn't hit the right keywords with a recruiter. Writing a lot of applications sucks. But it doesn't mean that your CV or your skills are bad and it doesn't diminish your worth. As a person. Probably amazing. And really be aware that you are an odd one out. So it might take you on an application more because someone has taken a little bit of a gamble on you. But if you translate your skills, like we discussed in this course, if you fill the gaps with the skills that we discussed, then I'm sure that you can transition your career from whatever you're doing now into a data profession, into tech, and find an amazing career that is oftentimes really inclusive, super interesting, keeps your learning and keeps you engaged throughout quite some time. So I hope this class helped you translate your experience into something data professionals understand. And please leave a review. And if you want that, other people find this, add your project. I know this can be weird with your own CV. So that's why we have the class project section in the beginning was some suggestions to not share all your personal data in the Internet. And with that, thank you for taking the class. Think about checking out my other classes that are about data science. Data science projects. With that, find me on social media. I write a lot about data science and machine learning. And again, thank you so much to making it to the end. And good luck on your learning journey and good luck with your career.