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Software Engineer/Developer/Programmer is essentially same thing, but with different level of cockiness applied. So “Engineer” would apply more for senior position. “Developer” is your classical mid. “Programmer” is the word neither “Engineers” and “Developers” like, due to possible implication this word hints -> there is no creativity and thought process, simply “instructing the machine”.Let you be inspired that the very best “Developers” I met/respect never came from Comp. Science background. “English and Literature”, “Astrophysics”, “Electronics” are few example degrees done by clever people I had a chance to speak/work with it.I mostly agree what User says, yet I would use more friendly form. However you have to keep mind that people like Justus are very common in our “dick culture”, extract the value from his answer, ignore the rest. Such people in the end become victims of other developers jokes, being positive and considerate about other people are qualities greatly valued among employers. It is easy to be a dick.From my side, I will split suggestions into several sections:Terminology and show off languageI have an impression that you make attempts to stand out mm in somewhat naive way :-) This is easy when you have commercial experience and you worked in certain exciting niches which rest of the world does not know about but hard when you are 1st time junior. Calling your python program “senior” will make people chuckle. Calling your python solution as “program” is also another giveaway as it sounds like tiny project/exercise. Combination of both, well you understand :-) Call your ‘programs’ as ‘projects’. Correct terminology is important. Projects do not give immediate visibility to the size/complexity on what you’ve done up until the time when you are asked about it . If it was really tiny peace of work you can always say during interview “small project”. Another one, “ongoing /advanced/ C++ socket communication program …” Why did you decide that it is /advanced/? The reality is that when you’ve worked with some trendy and complex and cool stuff , it would not need such words at all as paragraph describing what you did with them will give reader confidence that you understand the tech.2. Ongoing projects with no purposeWhen people put projects without purpose on their CV it makes me cringe. We don’t write sockets for the sake of sockets, we don’t connect to databases for the sake of connecting to databases. “Python database project”, “Client/Server model”. What was your main goal while developing your python database project? Did you try to create a flight booking system to save customer details? Did you try to develop new Quora? Did you find a way to have mini-facebook with some unique features? Maybe you tried to simulate life-support systems for future Mars expedition? Business drives development, when you talk about naked tech not only you exhaust your conversation with perspective employer very quickly but you also demonstrate that you found no application for knowledge you just learned. CV is not a tech blog post, remember that. Learn to bullshit here, in some scenarios you might just talk about Mars expedition for 20 minutes on your interview instead of answering what is the difference between ‘Vector’ and ‘ArrayList’ style questions, all thanks to the interesting project context.You can’t have 3 ongoing projects, I know that you have finished none and you are probably not going to finish any of them when I read it.3. Questionable sections in CVUnless you are applying to companies dealing with China market I find ‘language’ section as CV space eater, while it might not be precious to you as you are starting your development career it will be when you will have few years of dev experience.Platforms as mentioned by Justus are not critical, you will be asked of your shell experience anyway if it is relevant. People are rarely getting rejected a job for not knowing Linux, well, unless you are sysadmin or devops.Tools again something you can discuss in interview anyway.CV is not a story of your life, which screen you use, which platform you are familiar with or that you code in Visual Studio or IntelliJ.CV is :A. An honest display of your work experience supported by evidenceB. A demonstration of your passion and willingness to adopt and learn new technologies4. CV formatIt is good to see that you understand the value of bullet points, but your CV does not visually stand out. No one will tell you how exactly you can distinguish yourself, but to remember your CV from dozens/hundreds an employer need some associations with your CV in their brain. Black and white style is hard to remember.5. Website - irrelevant.If you create a web application which demonstrates maze generation and solving algorithms then your chances being hired suddenly raise dramatically. If you create a static site where we see videos you fooling around with your friends with only github link being relevant then you are wasting recruiter time. It is more likely that recruiter won’t even go beyond the animated button you created as extra step to enter your site.You will be quick to realise that developers recruitment is pretty serious process, don’t add something which brings you no credits for position you are trying to apply for. Keep your Facebook, Twitter or whatever locked to external world and keep a decent photo of yourself at all times. Any information you want to share with recruiters company should be only present on LinkedIn account. Don’t say more than you have to.Put single Github link to your !!! PDF CV!!! and send that to recruiters instead. If you absolutely want to have it online then be original, develop something exciting with it! Such as the French guy who created a site which looked like Amazon product page where he was selling himself.6. Github - no tests.I am JVM languages background dev, but can see that you have no tests for any of your projects. Learn about TDD(Test Driven Development) and why it is important, chances are that you will only use it to pass interviews, as some companies masturbate on TDD just too much so you have no choice.7. Choose your nicheThis is somewhat debatable but it will help to secure job easier. Which language you have more experience with? Which language do you like to work with more? Which language ecosystem(libraries, tools) you are more familiar with? It is hard to know whether you want to develop in Python or C++ by looking at your CV. While there is a lot of hype about full stacks and polyglot developers your entry position should focus just one so you gain experience and strength at least in one. What juniors do in universities is laughable at most, code quality, code structure, re-usability etc. most of the times non existent. Polyglot developer is somebody who is very mature. Those immature who try to code several languages at once most of the time end up as annoying people which break our code bases and give heart attacks to maintenance people who are sweating to read cryptic libraries original purpose of which frequently is a simple as to “print elements in collection to string”. I don’t know US market but I presume Python is more popular, giving you more interview opportunities and chances to secure your first “Software Developer” job.P.S. I thoroughly understand where your naive attitude comes from and how universities babysit students by inspiring them and going easy on them with job seeking advises. Well in fact all career fairs were BS time for myself. We’ve all been there with pink glasses thinking that industry waits for us with open arms.After you will get your job the first thing you want to learn is “How to ask questions to get help from your peers.” And don’t forget to listen what they say.You’d think that’s easy, but I cant reemphasise how many juniors don’t bother to formulate their questions while annoying just about everyone in the process:“Junior: Hey, could you help?”“Me: Yes, whats up?”“Junior: ‘It’ does not work (sad face <:-( ““Me: What does not work?”“Junior: ‘This’ stupid code does not compile”“Me: O.o”“Me: What is the context?”“Junior: I am trying to parse ‘this’ file but ‘it’ does not work”“Me: (Deep inhaling, slow exhaling) Which ‘this’ file.”“Junior: (Expands dir tree and points at file)”“Me: What exactly are you trying to do?”“Junior: Well..(scrolls through class with 1k lines of code half of which in single method)”“Me: Try to decompose it to several methods for better readability and then write tests so you can verify behaviour of your code against requirements. After you do this we will try to split the code across several classes.”“Junior: (exhales, looks at me in grumpy way, this is my favourite moment)I go away and 30 min later I come back.“Me: Hey how is it going?”“Junior: (sweating, panting, even if he did not know how to debug now he uses shortcuts like rambo going through every line like a blood-thirsty terminator with 2 rpgs in each hand, hunting the poor bug [which is why this is ‘my favourite’ moment], I am thinking that if I would give him a rifle right now and send to war somewhere distant there would be zero hesitation and full resolve)”“Junior: I still can’t find the issue… (leans back on the chair)“Me: Did you decompose your code?”“Junior: Yes”“Me: Did you write tests?”“Junior: No”“Me: Why not?”“Junior: I am so close, it is just this stupid method”“Me: Ok, without tests you will spend lots of time tracking the issue, call me when you write them OK?”“Junior: Okay (grumpy face returns back to rambo position)”“Me: (thinking “I probably should give up on humanity as people are so stubborn”, returning back to my desk).20 Min later I hear “YES, OH YEAH”“Me: (curiously approaching the guy)”“Junior: Found the bug, it is this typo in my constant which is why ‘x’ wasn’t matching to ‘y’.”“Me: How did you found this?”“Junior: (sneaky smile), first test I wrote revealed the bug”“Me: Sooooooo?”“Junior: Thanks! I now understand the value of tests tralalala bla bla bla bla, they opened my world and reason for existence” (exaggerating of course, but something along those lines).Then I lecture how time spend on entire task could be shrunk twice. After a year juniors come with ‘unpleasantly’ valid questions such as: “I tried to do query x in hibernate to return me a list of y but I see a number of duplicates and according to stackoverflow advises this may happen when there are duplicate results in joins. I have tests proving that the case but I cannot figure out where the duplicates coming from, but they behave differently between test and stage environment as there is high data volatility rate and we left technical debt ‘G’ which could potentially explain this behaviour today but we discussed the debt in 2016 on Christmas party when you were slightly drunk so I am not sure how much you remember, but I still believe that you are such smart/good/helping guy(because I need help from you right now), so all hope depends on you.” - This is the time when I understand that I am getting old and new generation is ready. So I look at the ceiling and sometimes say this:“I think you can solve this problem yourself in one hour and then show off how you did it, as honestly I have no clue right now”In one hour most of the time they come in a proud, back leaning manner with legs marching ahead of body and hands in pockets asking me for a cigarette knowing that I can never refuse a ‘ciggie’ on such a wonderful occasion.

Unfortunately in most cases, no.Stage 1: Psychological TestUpon being shortlisted, you would be contacted by HR in Langley, VA by phone (never by sms or fax, rarely by emailing you back due to encryption, cia.gov emails could end up in your spam mail and ignored indefinitely).Shortlisted applicants will be called for an assessment, primarily a thorough behavioral science or psychological test (MRI, Psychometrics). There is a consent agreement form you'd have to sign, us explaining how your information is being handled, the assessment commences upon you agreeing to proceed). If you make it to this round, there are several other stages you'll have to pass, they are:Stage 2: Background Checks & Security ClearancePolygraph Test followed by Security Clearance. Requirements concerning your sociological, political, religious and behavioral conduct or misconduct within the American community, governmental agencies and institutions which consist of rigorous individual background checks, familial, career, education, political and religious affiliations, social media interactions - part of CIA's assessments on top security clearanceStage 3: History of Drug AbuseWill not be considered for hire due to your history of Drug Abuse for the past 12 months prior or during the period of acceptance of conditional offer and/or you’re currently either medically required to consume prescribed medications due to an illness or deliberately intoxicating yourself for leisure, where any forms of drug abuse or the use of brain or behavior altering substances are highly prohibited in the CIA.No Drug Policy explained:It's more about self control, integrity, health, professionalism, sobriety and work ethics than the agency being draconian with you and your drugs or how you prefer to spend your money. Operatives and officers od the CIA possess high sense of self control and are very self composed, observant of his or her surroundings, in any clandestine mission involving data and intelligence collections to receiving orders from superiors, and these cannot be accomplished if one is constantly under the influence of intoxicated substances, where an operation or mission can be jeapordized, the safety and security of fellow operatives and officers compromised, hence, the agency has zero tolerance for drug use, including the use of decriminalized marijuana. These requirements can't be lifted or overcome by our state/federal laws in (WA, OR, CA, CO, AZ, NV, NM) and being born in any of these states won't change anything.Stage 4: History of criminal or mental illness recordsPast criminal records and reports on mental illness history will appear during recruitment process immediately eliminating your chances to being considered for further assessments and interviews.If however you passed all these requirements, you will be considered for the role, there will be verbal, group assessments such as;• In person interview with several personnel from Directorate of Operations or a designated department depending on which position you applied for• Exams - Multiple choice or Essays, subjects or topics on law and economics, international relations, history, war, ancient civilizations, sociology and social sciences, government, language, political science, anthropology, cryptology, topography, geography, astronomy, mathematics, applied sciences, engineering, philosophy, military science, technology and more - varies depending on your educational background and/or the position you applied for• Grouped Assessment - Scenario based exercise mainly for the elite/Tier 1 Operatives consisting of non commissioned or commissioned officers (NCO/CO), veterans or enlisted members of the Special Forces Operations interested in serving the CIA. Positions - Specialized Skills Officer, Paramilitary Officers, Targeting Officers, Collections Officers, NATO endorsed Military Analysts, Tactical Operations Officers, Geospatial Officers among other experienced special forces operatives often work together in a unit being observed and credited for teamwork, mental agility, physical endurance and performance, agreeability, tolerance, loyalty, dominant-submissive traits among other combative skills or exceptional qualities required, also depending on the position you applied for.However, if you did not make it pass these requirements and assessments, generally, you may not be allowed to re-apply or may be excluded from applying for 2 consecutive years (cooling off period). The FBI, NSA, Interpol/Europol practices the same strict recruitment process whereby law enforcement and national security are prioritized before a civilian-applicant's academic or career achievements.Application instructions:Central Intelligence AgencyApplication process:Central Intelligence AgencyHope this helps.

The answer below was taken from How to Build a Data Science Portfolio.The Importance of a PortfolioBesides the benefit of learning by making a portfolio, a portfolio is important as it can help get you employment. For the purpose of this article, let’s define aportfolio as public evidence of your data science skills. I got this definition from David Robinson Chief Data Scientist at DataCamp when he was interviewed by Marissa Gemma on Mode Analytics blog. He was asked about landing his first job in industry and said,The most effective strategy for me was doing public work. I blogged and did a lot of open source development late in my PhD, and these helped give public evidence of my data science skills. But the way I landed my first industry job was a particularly noteworthy example of the public work. During my PhD I was an active answerer on the programming site Stack Overflow, and an engineer at the company came across one of my answers (one explaining the intuition behind the beta distribution). He was so impressed with the answer that he got in touch with me [through Twitter], and a few interviews later I was hired.You may think of this as a freak occurrence, but you will often find that the more active you are, the greater chance you have of something like this occuring. From David’s blog post,The more public work you do, the higher the chance of a freak accident like that: of someone noticing your work and pointing you towards a job opportunity, or of someone who’s interviewing you having heard of work you’ve done.People often forget that software engineers and data scientists also Google their issues. If these same people have their problems solved by reading your public work, they might think better of you and reach out to you.Portfolio to get around an Experience RequirementEven for an entry level role, most companies want to have people with at least a little bit of real life experience. You may have seen memes like the one below.The question is how do you get experience if you need experience to get your first job? If there is an answer, the answer is projects. Projects are perhaps the best substitutes for work experience or as Will Stanton said,If you don’t have any experience as a data scientist, then you absolutely have todo independent projects.In fact, when Jonathan Nolis interviews candidates, he wants to hear about a description of a recent problem/project that you have faced.I want to hear about a project they’ve worked on recently. I ask them about how the project started, how they determined it was worth time and effort, their process, and their results. I also ask them about what they learned from the project. I gain a lot from answers to this question: if they can tell a narrative, how the problem related to the bigger picture, and how they tackled the hard work of doing something.If you don’t have some data science related work experience, the best option here is to talk about a data science project that you have worked on.Types of Projects to Include in a PortfolioData science is such a broad field that it is hard to know what kind of projects hiring managers want to see. William Chen, a Data Science Manager at Quora, shared his thoughts on the subject at Kaggle’s CareerCon 2018 (video).I love projects where people show that they are interested in data in a way that goes beyond homework assignments. Any sort of class final project where you explore an interesting dataset and find interesting results… Put effort into the writeup… I really like seeing really good writeups where people find interesting and novel things…have some visualizations and share their work.A lot of people recognize the value of creating projects, but one issue a lot of people wonder is where do you get that interesting dataset and what do you do with it. Jason Goodman, Data Scientist at Airbnb, has a post Advice on Building Data Portfolio Projects where he talks about many different project ideas and has good advice on what kind of datasets you should use. He also echos one of William’s points about working with interesting data.I find that the best portfolio projects are less about doing fancy modeling and more about working with interesting data. A lot of people do things with financial information or Twitter data; those can work, but the data isn’t inherently that interesting, so you’re working uphill.One of his other points in the article is that webscraping is a great way to get interesting data. If you are interested in learning how to build your own dataset by webscraping in Python, you can see my post here. If you are coming from academia, it is important to note that your thesis can count as a project (a very large project). You can hear William Chen talk about it here.Traffic Cruising Data Science for Social Good Project (https://github.com/uwescience/TrafficCruising-DSSG2017). This is an example of a project I personally find interesting, but there are so many interesting projects out there. Credit (Orysya Stus, Brett Bejcek, Michael Vlah, Anamol Pundle)Types of Projects NOT to Include in a PortfolioOne thing I have found very common (to the point of it appearing multiple times in this blog post) in a lot of portfolio/resume advice is not to have common projects in your portfolio.Jeremie Harris in The 4 fastest ways not to get hired as a data scientist said,It’s hard to think of a faster way to have your resume thrown into the ‘definite no’ pile than featuring work you did on trivial proof-of-concept datasets among your highlighted personal projects.When in doubt, here are some projects that hurt you more than they help you:* Survival classification on the Titanic dataset.* Hand-written digit classification on the MNIST dataset.* Flower species classification using the iris dataset.The image below shows partial examples of classification of Titanic (A), MNIST (B), and iris (C) datasets. There aren’t a lot of ways to use these datasets to distinguish yourself from other applicants. Make sure to list novel projects.Titanic (A), MNIST (B), and iris (C) classificationPortfolios are IterativeFavio Vazquez has an excellent article where he talked about how he got his job as a data scientist. Of course, one of his tips is to have a portfolio.Have a portfolio. If you are looking for a serious paid job in data science do some projects with real data. If you can post them on GitHub. Apart from Kaggle competitions, find something that you love or a problem you want to solve and use your knowledge to do it.One of the other interesting findings was that you always have to keep on improving as you go through the job hunt.I applied to almost 125 jobs (for real, maybe you applied for much more), I got only like 25–30 replies. Some of them were just: Thanks but nope. And I got almost 15 interviews. I learned from each one. Got better. I had to deal with a lot of rejection. Something I was actually not prepared to. But I loved the process of getting interviewed (not all of them to be honest). I studied a lot, programmed everyday, read a lot of articles and posts. They helped a lot.As you learn more and improve yourself, your portfolio should also be updated. This same sentiment is echoed in many other advice articles. As Jason Goodman said,The project isn’t done when you post it publicly. Don’t be afraid to keep adding on to or editing your projects after they’re published!This advice is especially true when you are looking for a job. There are many stories of successful people like Kelly Peng, Data Scientist at Airbnb, who really persevered and kept on working and improving. In one of her blogposts, she went over how many places she applied for and interviewed with.Applications: 475Phone interviews: 50Finished data science take-home challenges: 9Onsite interviews: 8Offers: 2Time spent: 6 monthsShe clearly applied to a lot of jobs and kept on persisting. In her article, she even mentions how you need to keep on learning from your interviewing experiences.Take note of all the interview questions you got asked, especially those questions you failed to answer. You can fail again, but don’t fail at the same spot. You should always be learning and improving.*If you aren’t getting interviews yet, apply for more jobs and keep on finding ways to learn and improve.Incorporating Portfolio into 1 Page ResumeOne of the ways someone finds your portfolio is often through your resume so it is worth a mention. A data science resume is a place to focus on your technical skills. Your resume is a chance to succinctly represent your qualifications and fit for that particular role. Recruiters and hiring managers skim resumes very quickly, and you only have a short time to make an impression. Improving your resume can increase your chance of getting an interview. You have to make sure every single line and every single section of your resume counts.William Chen, a Data Science Manager from Quora has 9 Tips for making your data science resume. Notice in the brief summary of his points below, that projects and portfolio are points 6, 7, 8, and arguably 9.Length: Keep it simple and one page max. This gives you the most impact for a quick skim. Recommends a simple one column resume as it is easy to skim.Sample Resume used in Video (latex: https://github.com/sb2nov/resume)2. Objective: Don’t include one. They don’t help you distinguish yourself from other people. They take away space from the more important things (skills, projects, experience etc). Cover letters are extremely optional unless you really personalize it.Objectives don’t help you distinguish yourself from other people. A lot of them say very similar things.3. Coursework: Do list relevant coursework that is applicable for the job description.Examples of relevant coursework displayed on various resumes.4. Skills: Don’t give numerical ratings for your skills. If you want to rate yourself on your skills, use words like proficient or familiar or things like that. You can even exclude assessments altogether.Don’t give numerical ratings for your skills5. Skills: Do list technical skills that the job description mentions. The order you list your skills in can suggest what you are best at.Examples of how you can list your skills on your resume6. Projects: Don’t list common projects or homework. They aren’t that helpful in distinguishing you from other applicants. List projects that are novel.7. Projects: Show results and include links. If you participated in Kaggle competition, put percentile rank as it helps the person reading your resume understand where you are in the competition. In projects sections, there is always room for links to writeups and papers as they let the hiring manager or recruiter dig in deeper (bias to real world messy problems where you learn something new).Good example project sectionsNotice that in one of the projects sections above, a person has an additional link to a blog that lets the recruiter or hiring manager find out more. This is one way to link to various parts of your portfolio from your resume.8. Portfolio: Fill our your online presence. The most basic is a LinkedIn profile. It is kind of like an extended resume. Github and Kaggle profiles can help show off your work. Fill out each profile and include links to other sites. Fill out descriptions for your GitHub respositories. Include links to your knowledge sharing profiles/blog (medium, quora). Data science specificially is about knowledge sharing and communicating what the data means to other people. You don’t have to do all of them, but pick a few and do it (More on this later).9. Experience: Tailor your experience towards the job. Experience is the core of your resume, but if you don’t have work experience what do you do? Focus your resume on independent projects, like capstone projects, independent research, thesis work, or Kaggle competitions. These are substitutes for work experience if you don’t have work experience to put on your resume. Avoid putting irrelevant experience on your resume.If you want to know hear data science managers go over portfolios and resumes, here are links to Kaggle’s CareerCon 2018 (video, resumes reviewed).Importance of Social MediaThis is very similar to the Importance of a Portfolio section, just divided into subsections. Having a Github page, a Kaggle profile, a Stack Overflow, etc can provide support for your resume. Having online profiles filled out can be a good signal for hiring managers.As David Robinson phrases it,Generally, when I’m evaluating a candidate, I’m excited to see what they’ve shared publicly, even if it’s not polished or finished. And sharing anything is almost always better than sharing nothing.The reason why data scientists like seeing public work is as Will Stanton said,Data scientists use these tools to share their own work and find answers to questions. If you use these tools, then you are signaling to data scientists that you are one of them, even if you haven’t ever worked as a data scientist.A lot of Data science is about communication and presenting data so it is good to have these online profiles. Besides from the fact that these platforms help provide valuable experience, they can also help you get noticed and lead people to your resume. People can and do find your resume online through various sources (LinkedIn, GitHub, Twitter, Kaggle, Medium, Stack Overflow, Tableau Public, Quora, Youtube, etc). You will even find that different types of social media feed into eachother.GithubGithub profiles of Jennifer Bryan and Yuan (Terry) TangA Github profile is a powerful signal that you are a competent data scientist. In the projects section of a resume, people often leave links to their GitHub where the code is stored for their projects. You can also have writeups and markdown there. GitHub lets people see what you have built and how you have built it. At some companies, hiring managers look at an applicants GitHub. It is another way to show employers you aren’t a false positive. If you take the time to develop your GitHub profile, you can be better evaluated than others.It is worth mentioning that you need to have some sort of README.md with a description of your project as a lot of data science is about communicatingresults. Make sure the README.md file clearly describes what your project is, what it does, and how to run your code.KaggleParticipating in Kaggle competitions, creating a kernel, and contributing to discussions are ways to show some competency as a data scientist. It is important to emphasize that Kaggle is not like an industry project as Colleen Farrelly, mentions in this quora question. Kaggle competitions take care of coming up with a task, acquire data for you, and clean it into some usable form. What it does is give you practice analyzing data and coming up with a model. Reshama Shaikh has a post To Kaggle Or Not where she talked about the value of Kaggle competitions. From her post,It is true, doing one Kaggle competition does not qualify someone to be a data scientist. Neither does taking one class or attending one conference tutorial or analyzing one dataset or reading one book in data science. Working on competition(s) adds to your experience and augments your portfolio. It is a complement to your other projects, not the sole litmus test of one’s data science skillset.It is also true that there is a good reason why Kaggle Grandmasters continue to participate in Kaggle competitions.LinkedinUnlike a resume, which is confined by length, a LinkedIn profile allows you to describe your projects and work experience in more depth. Udacity has a guide on making a good LinkedIn profile. An important part of LinkedIn is their search tool and for you to show up, you must have relevant keywords inyour profile. Recruiters often search for people on LinkedIn. LinkedIn allows you to see which companies have searched for you and who has viewed your profile.Checking where your searchers work and how many times people have viewed your profile.Besides companies finding you and sending you messages on your availability, LinkedIn also has many features like Ask for a Referral. Jason Goodman in his article Advice on Applying to Data Science Jobs uses LinkedIn to indirectly ask for referrals.I never, never, never applied to any companies without an introduction to someone who worked at the company…once I was interested in a company, I would use LinkedIn to find a first- or second- degree connection at the company. I would write to that connection, asking to talk to them about their experience at the company and, if possible, whether they’d be able to connect me to someone on the Data Science team. Whenever I could, I did in-person meetings (coffee or lunch) instead of phone calls. As an aside, Trey Causey recently wrote a great poston how to ask for just these kinds of meetings. I would never ask for a job directly, but they would usually ask for my resume and offer to submit me as an internal referral, or put me in touch with a hiring manager. If they didn’t seem comfortable doing so...I’d just thank them for their time and move on.Notice that he doesn’t right away ask for a referral. While common job advice when applying to a company is to get a referral, it is VERY IMPORTANT to note that you still need a portfolio, experience, or some sort of proof you can do a job. Jason even mentions the importance of a portfolio in that and other articles he has written.Aman Dalmia learned something similar by Interviewing at Multiple AI Companies and Startups.Networking is NOT messaging people to place a referral for you. When I was starting off, I did this mistake way too often until I stumbled upon this excellent article by Mark Meloon, where he talks about the importance of building a realconnection with people by offering our help first.One other point he had is that LinkedIn is great for getting your content/portfolio out.Another important step in networking is to get your content out. For example, if you’re good at something, blog about it and share that blog on Facebook and LinkedIn. Not only does this help others, it helps you as well.Medium and/or Other Blogging PlatformsHaving some form of blog can be highly beneficial. A lot of data science is about communication and presenting data. Blogging is a way of practicing this and showing you can do this. Writing about a project or a data science topic allows you to share with the community as well as encourages you to write out your work process and thoughts. This is a useful skill when interviewing.As David Robinson said,A blog is your chance to practice the relevant skills.Data cleaning: One of the benefits of working with a variety of datasets is that you learn to take data “as it comes”, whether it’s in the form of a supplementary file from a journal article or a movie scriptStatistics: Working with unfamiliar data lets you put statistical methods into practice, and writing posts that communicate and teach concepts helps build your own understandingMachine learning: There’s a big difference between having used a predictive algorithm once and having used it on a variety of problems, while understanding why you’d choose one over anotherVisualization: Having an audience for your graphs encourages you to start polishing them and building your personal styleCommunication: You gain experience writing and get practice structuring a data-driven argument. This is probably the most relevant skill that blogging develops since it’s hard to practice elsewhere, and it’s an essential part of any data science careerBy writing a blog, you can practice communicate findings to others. It also is another form of advertising yourself. Blogs about Using Scrapy to Build your Own Dataset, and ironically Python Environment Management with Condahave taught me a lot and have gotten me a lot of opportunities I would normally not have gotten. One of the major benefits I have found is that throughout the process of people critiquing my projects and suggesting improvements (though the comments section of the blog) makes it so interviewers aren’t the first ones pointing out these same flaws. The more obvious benefit is that by making a blog you tend to read a lot more data science/machine learning blog posts and hence learn more.As for what platform to blog on, I recommend using Medium. Manali Shindein her blog post How to Construct a Data Science Portfolio from Scratch had a really good point on why she choose Medium for her blog.I thought of creating my own website on a platform such as WordPress or Squarespace. While those platforms are amazing to host your own portfolio, I wanted a place where I would get some visibility, and a pretty good tagging system to reach greater audiences. Luckily Medium, as we know, has those options (and it’s also free).If you don’t know what to write about, I suggest you look at David Robinson’s advice.https://twitter.com/drobTwitterBeing active on Twitter is a great way to identify and interact with people in your field. You can also promote your blog on Twitter so that your portfolio can be that much more visible. There are so many opportunities to interact with people on twitter. One of them as Reshama Shaikh said in her famous blog post “How Do I Get My First Data Science Job?” was,David Robinson generously offers to retweet your first data science post. With 20K+ followers, that’s an offer that can’t be refused.Twitter can be used for other things than self promotion. Data Science Reneehas a post “How to use Twitter to Learn Data Science (or Anything)” that is quite insightful about taking Twitter to learn skills. One other takeaway from her article was how much her Twitter presence helped her network and get opportunities.I have been asked to be interviewed on podcasts and blogs (some of those should be coming up soon), offered contract work, and offered free admission to a conference I unfortunately couldn’t go to, but was excited to be considered for. “Famous” people in the industry are now coming to me to work with them in some way.Tableau PublicNot every data science job uses Tableau or other BI tools. However, if you are applying to jobs where these tools are used, it is important to note that there are websites where you can put dashboards for public consumption. For example, if you say you are learning or know Tableau, put a couple dashboards on Tableau Public. While a lot of companies might be okay with you learning Tableau on the job, having public evidence of your Tableau skill can help. If you want to see good examples of Tableau Public profiles, please see Orysya Stus’ and Brit Cava’s profiles.ConclusionRemember a portfolio is a process. Keep on improving.Having a strong resume has long been the primary tool for job seekers to relay their skills to potential employers. These days, there is more than one way to showoff your skills and get a job. A portfolio of public evidence is a way to get opportunities that you normally wouldn’t get. It is important to emphasize that a portfolio is an iterative process. As your knowledge grows, your portfolio should be updated over time. Never stop learning or growing. Even this blog post will be updated with feedback and with increasing knowledge. If you want interview advice/guides, time to check out Brandon Rohrer’s advice on how to survive a data science interview, Sadat’s interview guide, or Springboard’s advice. If you have any questions or thoughts on the tutorial, feel free to reach out in the comments below or through Twitter.