Analytics is a lucrative and high-growth field, and can be a great way to progress your career while building off your prior knowledge. In most organizations, there are very few members who have the skills to understand and analyze data. Those who are data-literate are needed and valued across all industries, departments, and seniority levels.
If you are considering transitioning into a career in analytics, here are the steps you can take to understand what that career might entail, learn the tools to solve problems, and make your next career step a reality.
1. Assume Your Analytical Mindset
Great analysts come from a range of different backgrounds, but all of them use data to draw conclusions, instead of the other way around. Before you jump into your job search, start focusing on the everyday statistics and numbers in your life. Next time you take a stance on a topic at a company meeting or out with friends, ask yourself:
- From what am I deriving this information?
- What numbers support my position?
- What numbers contradict my position?
To exercise this mindset, pick a debate on a topic you don’t know much about. (Maybe it’s Biggie vs. Tupac or Is college necessary? ) Compile all of the relevant, objective data you can find, and use that data to formulate a position in the debate. Better yet, come up with two contradictory arguments based on the same data. See how your final position compares to any preconceived notions you had. Now, challenge yourself by incorporating that methodology into your every day.
2. Research How Analytics Is Used in Your Field
Analytics isn’t a stand-alone field, but rather a tool you can use wherever data is collected. Unlike many other hard-coding careers, analytics builds off your expertise in other areas, and analytical roles can vary depending on the context of the industry, department, and role.
Picture your current or desired field, and become more cognizant of how data is used there. If you don’t have insight or access to reports or datasets, you can look up public data sets from places like the KD Nuggets directory or Kaggle to see what metrics are being recorded. Imagine an analyst role in your chosen field, and try to answer these questions:
- How do you define success in this field?
- If you defined success qualitatively, how can it be measured
- Picture a common challenge or problem being faced in this field. What
metrics would you look at in order to diagnose and resolve it? For example, the food industry suffers from produce going bad in transit. In that scenario, you might want to look at distance traveled, type of produce, method of transportation, and yield.
- Imagine you have multiple new opportunities in this field (i.e. expansions, partnerships). What metrics would you look at in order to decide what to pursue, and what not to pursue?
- What company or organization do you admire? Look at their blog, hiring policies, social media, and mission statement. What value do they place on data and analytics?
3. Learn to Code
Now that you’ve assumed the analytical mindset and understand the context of how data is used in your field, start learning the tools that will make you invaluable. Today’s most in-demand analytical tools include Excel, SQL, R, and
Python. Although tools and languages may change every few years, the fundamentals behind them remain mostly the same.
When you become proficient in one tool, you equip yourself with the skills to learn the next tool more easily. With that in mind, don’t try to speed past Excel and basic statistics in order to learn the shiniest new scripting language. The most important skill in analytics is the ability to adapt to new technologies.
Different people have different learning styles, and you can choose from a number of ways to learn, such as online courses, bootcamps, or master’s programs. To discover what is best for you, ask yourself:
- What level of expertise are you looking to attain?
- Do you learn best online or in a classroom setting?
- Picture a Venn diagram of cost, time commitment, and quality. Which is
most important to you? Which are you willing to compromise?
4. Create a Portfolio
When it comes to technical roles, employers want concrete evidence of what you can do. Now that you have some technical coding knowledge, you want to start building the greatest weapon in your career trajectory: your portfolio. Not only will a portfolio surpass the greatest resumé ever made, it will make sure that you are constantly working on and polishing your skills.
- If you are currently employed, start doing side work with company data. Ask the analysts at your company what they are working on and what problems they face. Use your skills to make a meaningful change in the company, or bring a new issue to light. Not only will you will build up your portfolio and create good will at work, but you will practice combining your new analytical skills with your previous industry experience.
- Sign up for Kaggle, a crowd sourcing platform where employers publish datasets and data-related challenges for competitors to solve. Even if you don’t win a competition, you can use the platform to find relevant problems
and build a case study of data analysis and visualization for your portfolio.
- If you are interested in more technical analyst roles, create a Github profile. Github is the primary open-source site for hosting projects, and even has an analyst collective full of resources for data modeling and analysis. Here’s an article for beginners on why you should make a Github, and how to get started.
5. Network (Without Name Tags or Small Talk)
Whether you love it or hate it, networking is always going to be on any list about career advancement. The longstanding weak ties theory says that weak acquaintances, not close friends, will be responsible for impacting major events in your life. Expanding your network is important, and there are ways you can do that that don’t involve typical networking events.
- Get the word out about your interest in analytics to anyone who will listen. Find out about friends of friends who are in similar fields or roles, and ask for an introduction.
- Look for local city and government data competitions. Not only do these competitions have prize money, but there are public meet-ups where you can meet like-minded analysts and data leaders in the community.
- Find experts and analysts on Quora and reach out to them for advice, connections, or an interview. Regular Quora posters want to help people in their industry.
- Contact your alma mater’s career services and ask for analytics career resources, as well as introductions to alumni, industry partners, or professors in analytics.
- Practice for your interviews with a mock analyst interview. Analyst interviews will likely differ from interviews you’ve had in the past. If you finda great company with no open roles, you should always ask for a mock interview.
Career transitions don’t happen overnight, and there is no shortcut to becoming a data analyst. Regardless of your prior experience and future goals, the Level data analyst toolkit is a next step for anyone looking to leverage their analytics skills into a career move or career advancement. The Level data analyst toolkitincludes:
- Anatomy of a Data Analyst Resume
- Resume Action Verb Cheat Sheet
- Bonus: RStudio Cheat Sheet