When you feel like it's time to finally make the push to step up your analytics game, you're faced with a pretty big tangle of options. Should you look to apply to a higher ed program in a relevant field? Are sites like Udemy or Coursera that offer classes and certifications enough? What about a bootcamp, or MIT OpenCourseWare? Are free resources sufficient or should you pay for data analytics training?
There's no one, linear path that works for everyone who wants to dive into analytics and data science, and it can be hard to know what's a waste of your time and money and what's actually going to help you achieve your goals.
The good news is that there's something for everyone who wants to become better with data. It all depends on what your background is and where you want your analytics expertise to take you.
Deciding on your goals
From those gunning for a PhD in statistics to those who barely remember their college calc course, many people want to work with their data smarter.
Maybe you want to become a project manager who can easily liaise with engineers. Maybe one day you know you'll take on a role where you need to understand the basics of analytics, but not necessarily get into the nitty gritty of scraping, compiling, and processing data every day. Or maybe you want to go whole hog and become a data scientist.
Whatever your goal, it's important that you are honest with yourself and assess where you can go by studying analytics more closely. Some questions to ask yourself:
- How much experience do you have with working with data now?
- What level of expertise do you need to make your next career move?
- If you have access to and training on the right tools, can you ask analytical questions and get what you need?
- Do you need to have knowledge of advanced statistics, modeling, and machine learning?
- What is your background with statistics, computer programming, etc? How quickly did you pick up these fields?
- How much time, energy, and money can you put into learning more about working with data?
Knowing the answers to these questions will help you figure out what is an achievable goal before you spend a bunch of your resources pursuing something that is never going to quite work out.
So you want to work in tech as a data scientist...
As much as we talk about data accessibility and data-informed decision making being important for everyone at a company, data scientists are often still the go-to people for working with data. Many tech companies have started relying more and more on data to make decisions, and data scientists are in fairly high demand.
To be considered a data scientist, or have that leading data role at a company, you'll need to have:
- A strong grasp of mathematical and statistical concepts that provide the basis for working with data, probably from a higher ed degree, like a Masters or a PhD in a relevant field. Statistics is the obvious choice here, but other fields where you work with data and data modeling, like physics or even economics, can also be a good base.
- A mastery of requisite programs and techniques for gathering and processing data, like R, SQL and Python.
- The ability to work well with a variety of people with varying degrees of technical knowledge, and the ability to communicate complex concepts to people regardless of their data expertise.
- A passion for solving problems with data and an understanding of how to approach any problem from a variety of qualitative and quantitative perspectives.
- The ability to design and execute large-scale data projects.
Choosing a degree program and then spending 2-5+ years working on classes and a dissertation is something you have to be sure of before jumping in. Although you're bound to get something useful out a higher ed program, even if you don't complete it, the drive to explore a subject in an intense academic setting has to be of interest to you to pursue this option.
If not formal training, the other major experience that you need is supplementary work in tech. Even if you already have a degree in a relevant field, you will need to have worked with data in a real business to push your career forward.
- Ask for expanded responsibility with data in your current job.
- Consider entering a bootcamp or challenge that requires you to process data in a business-type setting.
- Freelance to build a portfolio of wins you've gotten for businesses.
- Make a horizontal move before a vertical one. You can gain important experience working with data in a business setting if you're willing to take a step sideways — or even backwards — before you go up.
In all likelihood, you can't self-teach your way to becoming a data scientist, especially in tech. If it's something that excites you, then go full steam ahead. But be honest with yourself about your ambition and your intentions — it takes a serious commitment to becoming an expert.
So you want to be analytical... but don't want to be a data scientist
There are a lot of roles for people who are data literate and analytical, but aren't necessarily considered a “data scientist." Here are some ways to become more analytically minded:
- Cherrypick classes from your local community college. This is a great option if you need to build up a strong base for statistics and working with data. Pros: There is a more built-in incentive structure than you'll find in online courses — like attending a physical class and a higher cost. You'll also have other students to work with and a professor to pester. Cons: Less flexibility and availability, and more expensive than other options.
- Take classes online with an instructor or progressively structured course. Many sites have courses on data methods, analytics, CS, and other relevant subjects that you can take for free or for a small fee. Pros: Some courses come along with a certification, which can boost your resume. Working along with a timeline and structure helps you keep up your motivation. Cons: Quality and content varies across different sites and instructors, and you can't always ask for help when you need it.
- Follow an open courseware syllabus. Many colleges and universities offer “open classes”, where lectures, assignments and reading lists are uploaded for anyone to access. Pros: Free, unlimited access to some of the smartest professors from anywhere, anytime. Cons: With no structure or support, this is a poor option for people without a strong background in data science, and it requires strong self-discipline.
- Seek out hands-on experience. There's no substitute for actually working with data in the real world. Maybe you can jump into some analytics at your company, or maybe you can help out a friend who owns their own business. Pros: If you already have a strong background, real-world experience is a great way to learn on-the-fly and helps build your credentials. Cons: Opportunities to test your skills can be few and high risk, and your learning is mostly trial and error.
- Start your own project. Like getting hands-on experience, starting a data science project in your free time can be a great way to build skill. Pros: You can design an experiment using your hobbies or anything that makes you excited, and you'll develop critical thinking about asking questions and managing projects. Cons: It can be difficult to jump into and finish a project with little direction, no deadline, and no accountability — if you're easily frustrated, this is not the option for you.
- Last but not least, research the industry. Lots of analytics companies are trying to enable people — and not only those who are technical — to be more analytically minded. If you're already working at a data-informed business and looking to improve how you explore your data and get insights, see what other analytics companies are saying about data best practices.
At the end of the day, you need to prioritize building up your weakest skills. If you have little experience working with programming and analytics, take some structured classes. Then, when you've got a handle on the basics, find a way to get some real-world experience with data to transform your raw knowledge into a marketable asset for yourself.
There's no need to pay through the nose for analytics training, but make sure that you're committed to investing in your skills and your future, not shooting yourself in the foot because you don't want to shell out for a class or two.
Bottom line: you have to hustle
Now is one of, if not the most exciting time to be getting into data. Our technology, job opportunities and ability to share knowledge has never been better, especially where data and analytics are concerned. Making a choice about what to pursue is not a black-and-white decision, so weigh your options carefully before wasting your money or your time. Remember that even if you only get through an online course or two, you'll be adding valuable critical thinking skills to your arsenal.
If you're self-motivated and excited to learn about data science and analytics, there is something out there for you.