Have you ever wondered how to get started in Data Science and Analytics? The field of data science and analytics has become one of the hottest and most in-demand job opportunities in the world today, thanks to the growth of Big Data and the increasing demand for people with data skills across industries and sectors. If you’re interested in a career in data science and analytics, you’re going to need to start at the beginning. Data science and analytics have become massively popular industries within the last few years, which means that competition for jobs can be fierce if you don’t know what you’re doing. Over the past few years, data science and analytics have become two of the most valuable skillsets to have in the workforce. As companies continue to amass huge amounts of data, the need for professionals who can make sense of that data has grown exponentially as well. To build a career in this field, you’ll need to learn many different kinds of analytic tools and techniques and master one or more programming languages like Python, R, or SAS. Fortunately, if you know how to build a career in data science and analytics, there are plenty of jobs waiting for you when you graduate from college or university and enter the workforce. In this guide, we will go over ten steps that will help you to get started building your data science and analytics career.
1) Pick Your Industry
Pick an industry in which you’re interested. If that’s not possible, look for industries with interesting, complex or growing problems that are amenable to data science solutions. Think about how your data science education can map onto those industry problems and find an organization within that industry where you can start building expertise with real problems. A good way to think about industry applicability is by looking at job postings—see what skills they are looking for, then build those skills over time. The goal should be a portfolio of transferable data science skills applied toward solving interesting business problems within or across industries. As a rule of thumb, companies want to hire people who know how to solve their business problems (regardless of background), so it’s important to make sure you’re acquiring data science skillsets relevant to solving their business needs. This will help ensure that once you’re hired, you’ll have an easier time finding ways to add value from day one. This isn’t as straightforward as it sounds: many companies use outdated or generic job descriptions, often failing to highlight specific challenges faced by teams within those organizations (or even what kind of work these teams do). So take some time on Glassdoor and other review sites like Vault and Payscale to figure out which companies are doing exciting things with data science and analytics today.
2) Educate Yourself
The first step is always to educate yourself. Now that so many colleges are offering data science programs (including my own Northwestern), there’s never been a better time than now to start learning how it all works. In addition, conferences like Strata + Hadoop World feature extensive tutorial tracks on everything from machine learning, big data technologies, and data science for good. Personally, I try not to miss an opportunity to learn new things – these days I attend several conferences every year simply because they interest me. The key is staying connected with people who do similar work – Twitter (and other online forums) are great for that! And of course, one of your best resources will be those already working in your field: once you have a job offer or some experience under your belt, go out of your way to meet others doing what you want to do. There are few greater resources than real-life mentors. Don’t hesitate to reach out when starting out – most experts are happy to talk if you just ask them! With our world becoming increasingly digitized, more opportunities exist today than ever before for people interested in pursuing careers in analytics and data science. If there’s something specific that interests you (say security analytics or sports analytics), check out companies within those industries and see if they’re hiring.
3) Attend Data Science Meetups/Events/Conferences
One of the most popular ways for people to the network is through meetups and conferences. By attending meetups/events/conferences, you will have access to an abundance of networking opportunities where other data science professionals are looking for assistance on their projects or just someone they can bounce ideas off of. If you’re looking for your first job as a data scientist or analytics professional, it’s absolutely imperative that you start building your network right away. It doesn’t matter if nobody knows who you are when you attend these events; after all, no one knows who anyone else is at these events either (or at least, not yet). You will be shocked by how many great connections await those who put themselves out there. The only way to make meaningful connections with others is to go out and do so yourself. Don’t wait for others to come find you! Attend local meetups, join LinkedIn groups related to data science and analytics, reach out directly via email/phone call/etc., etc., etc. The more people you connect with, even if they don’t seem like a good fit now, could turn into valuable contacts down the road once you’ve established yourself in your career field.
4) Get Involved With Data Science Communities Online
One of the most important places where you can start building your professional network as an aspiring data scientist is on sites like Kaggle. Kaggle is a platform for data scientists who want to compete with one another on various analytics challenges. Be sure to check out its resources section where it lists free books, tutorials, videos, guides, and more that will help jumpstart your career. Another great place to find many useful datasets is on GitHub, which hosts all sorts of reports with open-source datasets that are waiting for someone like you who knows how to analyze data well. Check out CrunchBase if you’re interested in what other startups have been working on recently — just be warned: It’s addictive! Finally, don’t forget about Quora; it’s a great place to ask questions about anything related to data science or analytics. You’ll be surprised by how quickly you’ll receive answers from people around the world who work in these fields every day.
5) Be Active On Linkedin
One of the best places for staying up-to-date on job openings is Linkedin. The company has taken significant strides in recent years to foster its community of professionals, including developing some really interesting career features. One feature that I’ve used is their Who’s Viewed Your Profile tool, which shows you who from your network has viewed your profile—even if they haven’t connected with you yet. Another fun one is their introduction feature that allows anyone looking at your profile (or vice versa) to send an introduction request. You can also search by location, industry, or title to find great contacts near you or that match jobs you want. For example, if you are interested in working as a data scientist but don’t have any experience yet, look through people’s profiles who have similar titles and reach out to them directly. They might be able to offer advice about getting into analytics or share any tips they have about landing a job. If it sounds like a cold call, don’t worry: LinkedIn makes it easy for users to connect without being creepy or overbearing. It’s not just about collecting business cards: In addition to networking events where you’ll likely collect plenty of business cards, consider reaching out via email after meeting someone at an event. This can make it easier to follow up later down the road when you’re ready for your next move in your career path.
6) Work On Projects That Are Similar To Your Career Goals
It doesn’t have to be as complicated as doing data science full-time, but working on projects that are similar can help you develop skills that will make you more marketable. As an example, if your goal is to do data analytics for a sports team, try taking a course on statistics or building your own website for fantasy sports. As another example, if your goal is to use big data in health care, try taking courses on Java and SQL or creating an app with real healthcare data like PatientsLikeMe. If it seems daunting, just start small—you don’t need funding or experience (and likely won’t get it) when starting out. Do what you can and work hard at it. You never know where it might lead! The next thing you should do is a network: A solid network of connections goes a long way toward helping your career, no matter what industry you’re in. While it may seem clichéd, meeting people who share your interests can be incredibly helpful down the road. For example, perhaps someone shares a problem they’re having and then offers to help solve it for free because they want to practice something new themselves. Or maybe someone has read about a book or software package that looks interesting but isn’t sure whether they should buy it yet; ask them about their needs and then share whatever information comes up in conversation. The best part about networking isn’t even getting new opportunities from people; it’s learning from others by asking questions.
7) Do More Than What Is Asked Of You At Work
Whether it’s taking on extra projects or networking with others, doing more than what is asked of you shows that you’re an ambitious person who’s passionate about their work. Even if your boss doesn’t always recognize these things, other people will take notice. Being able to demonstrate an ability to do more than what is asked of you also means that when given more responsibility or asked for a favor, there will be no doubt as to whether or not you can handle it. It shows people that they can rely on you (which is important in any professional setting). Bottom line: do more than what is asked of you, even if your boss doesn’t directly acknowledge it. You never know who might see it and what doors might open up because of it. And, of course, always strive to go above and beyond. That’s how great careers are built! If you’re interested in making a career out of data science and analytics, you should always keep an eye out for opportunities to build your skills and experience, particularly through completing side projects. One way to do so is by finding cool datasets on sites like Kaggle or scraping data from relevant websites like Wikipedia.
8) Develop Leadership Skills
You’ll need leadership skills if you’re to have an impact as a data scientist. From crafting data solutions for your company, to teaching business leaders about how data science can be used across various departments, great leaders aren’t afraid of sharing their knowledge with others. To develop your leadership skills, start by becoming more self-aware. It’s crucial that you have enough confidence in yourself (without appearing egotistical) to convey ideas effectively and confidently with those who need convincing about your solutions. In addition, having strong problem-solving skills is vital—otherwise, you won’t be able to solve complex issues related to analytics or data science. And lastly, when it comes to leadership qualities, emotional intelligence is just as important as intellectual intelligence. Leaders who are emotionally intelligent are better at understanding people and reading situations. They’re also better at managing conflict and motivating employees toward common goals. If you want to become a leader in data science or analytics, practice these three qualities on a daily basis!
9) Go Out Of Your Comfort Zone
As much as we’d all like our careers to be free of obstacles, it’s just not realistic. In any role, with any company, there will always be moments when you doubt yourself or wonder if what you’re doing matters. These are good things! Don’t shy away from them—embrace them. You should always strive for more responsibilities, new challenges and opportunities that push your skillset (and comfort zone) beyond your current boundaries. They might not come often, but when they do…learn from them, grow from them. Your career depends on it. And so does your future. So go out of your comfort zone. Take risks. Be brave. Get uncomfortable because that’s where growth happens. That’s how you become great at what you do. It won’t always feel comfortable, but it will always feel worth it. Because it is.
10) Stay Away From High-Pressure Jobs and Be Happy!
When starting a career, it’s important to remember that your job is what you make of it. If you find yourself unhappy with your position or role, it’s time for a change! There are thousands of data science jobs available at any given time; here are some strategies that will help guide you as you work towards landing one 1) Stay away from soul-sucking jobs: This may seem obvious, but stay away from roles that offer little opportunity for growth or creativity. You don’t want to waste years of your life working on tasks you hate simply because they pay well (or have a nice title). 2) Identify your strengths: Use resources like LinkedIn and Glassdoor to identify other people who share similar backgrounds and experiences (think educational background, previous employers/positions held, etc.). Research these individuals so you can learn about their career paths—where they came from, where they are now, how long it took them to get there—and determine whether their journey aligns with yours. If you are interested to learn new coding skills, the Entri app will help you to acquire them very easily. Entri app is following a structural study plan so that the students can learn very easily. If you don’t have a coding background, it won’t be any problem. You can download the Entri app from the google play store and enroll in your favorite course.