How to Get Into Analytics: 5 Steps to Transition Careers

By Kelsey Miller|August 14, 2019

In most organizations, there are very few members who have the skills to understand and analyze data. Those whoaredata-literate are needed and valued across all industries, departments, and seniority levels, making a career in analytics a potentially lucrative one for people of all backgrounds.

One question that is shared by nearly everyone interested in pursuing a career in analytics—whether your career is just starting out or you are looking to transition into something new—is:How do I get into analytics?

If you’re consideringa career in data analytics, the information below can help you make the transition.

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Why Work in Analytics?

According toprojections from IBM and Burning Glass Insights,it’s estimated there will be more than 2.7 million job openings for professionals with data skills by 2020, up from 2.35 million in 2015.

And because nearly 40 percent of these jobs will require amaster’s degreeor higher, it’s likely companies will not be able to fill open positions as readily as they would like. The same study shows that data science and analyst jobs already remain open for five days longer than average.

For those interested in transitioning into a career in analytics, this is good news. Qualified professionals looking to break into the analytics field can leverage their skills in order to earn handsome salaries. According to Robert Half’s2019 Technology Salary Guide, individuals pursuing an analytics career can earn anywhere from $77,000 to $219,500 annually, depending on the job title and other factors.

Data Analytics Careers

If you’re considering a career in the analytics field, you may be wondering about the specific opportunities available to you. While the title ofdata analystis a popular one (and likely the first to come to mind), it is not the only option.

Here are other in-demanddata-oriented careersthat may align well with your interest and skills:

Big Data Engineer:$127,250–$219,500
Database Manager:$108,000–$183,000
Database Developer:$98,250–$167,750
Database Administrator:$77,000–$159,250
Data Analyst/Report Writer:$81,750–$138,000
Data Architect:$111,500–$187,750
Data Modeler:$79,000–$164,500
Data Scientist:$102,750–$175,000
Data Warehouse Analyst:$77,750–$160,000
Business Intelligence Analyst:$85,750–$178,000

(All salary data sourced fromRobert Half。)

How to Jumpstart Your Data Analytics Career

1. Assume an analytical mindset in your day-to-day life.

Great analysts use data to draw conclusions; they don’t approach a question or problem with a preconceived answer. (It is true, though, that they may have several hypotheses to test.) This is one of the most important truths about a career in analytics, which you should embrace before making a career change.

If you haven’t had much exposure to working with data or using data to draw conclusions, one way to gain some practice is by 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.编译所有相关的、客观的数据n 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.

Then, challenge yourself by incorporating that methodology into your everyday life.

2. Research how analytics are leveraged in your industry.

Analytics isn’t a stand-alone field. Analytics can be applied wherever data is collected, and analytical roles can vary depending on the context of the industry, department, and role. Thus, great analysts come from a range of different professional backgrounds.

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 datasets from places like theKDNuggets directoryorKaggleto see what metrics are commonly recorded. Imagine an analyst role in your chosen field, and try to answer these questions:

How is success defined in this field?
如果成功是defined qualitatively, how can it also be measured quantitatively?
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 spoiling in transit. In that scenario, you might consider distance traveled, type of produce, method of transportation, crop yield, etc.
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 companies or organizations do you admire? Look at their blogs, hiring policies, social media accounts, and mission statements. What value do they place on data and analytics?

By answering these questions, you’ll be better equipped to understand how to leverage data in your current role, as well as how data may influence future positions that you pursue.

3. Develop your skills.

Now that you’ve assumed an analytical mindset and understand the context of how data is used in your industry, start learning the skills and tools that will make you invaluable to potential employers.

First, data analysts must possess a number of soft skills in order to be effective in their roles. Skills like communication, organization,project management,leadership, and critical thinking are important regardless of industry.

In addition to those soft skills, you will also need to learn the technical skills necessary to perform the job.

Some of the most in-demand technical analytical skills include machine learning, predictive analytics, data visualization, MapReduce, and a general understanding of big data and data science. Additionally, many employers seek to hire individuals who have expertise working with specific tools, such as Apache Pig, Apache Hive, Apache Hadoop, and MongoDB.

If you would like to transition into analytics from a non-analytics career and don’t possess these skills, consider whattransferable skillsyou might already possess which could translate well into your desired career.

4. Learn to code.

One of the most essential skills to effectively work in an analytical role is the ability to read, write, and analyze data and code.Today’s most in-demand analytical tools and languages includeExcel, SQL, R, and Python。Although these 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, so choose which level of education is right for you, such asgraduate certificatesormaster’s programs校园或在线。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?

5. Create a portfolio.

When it comes to technical roles, employers want concrete evidence of your abilities. Once you attain some technical coding knowledge, you’ll want to start building the greatest weapon in your career arsenal: yourePortfolio。Not only will an ePortfolio make a greater impression than a resumé, but it will also require you to regularly refine and update your skills.

If you don’t have a portfolio, here are a few ideas to help you get started:

If you are currently employed,start working 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 build up your portfolio and create goodwill at work, but you will practice combining your new analytical skills with your previous industry experience.
Sign up forKaggle, a crowdsourcing 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 ananalyst collectivefull of resources for data modeling and analysis. (Here’s anarticle for beginnerson why you should make a Github, and how to get started.)

6. Network.

Whether you love it or hate it,networkingis always going to be on every list of career advancement strategies. The longstandingweak ties theorysays that weak acquaintances, not close friends, will be responsible for impacting major events in your life.Expanding your networkis important, and there are ways you can do it that don’tonlyinvolve 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 onQuoraand reach out to them for advice, connections, or an interview. Regular Quora posterswantto 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 amock analyst interview。分析师的采访可能会不同于我nterviews you’ve had in the past. If you find a great company with no open roles, you can still ask for a mock interview.

Transitioning to Data Analytics

Career transitions don’t happen overnight, and there is no shortcut to becoming a data analyst. While these steps may seem daunting, you might start to realize that you’rehaving funsolving data problems, learning new tools, and crowdsourcing from others. When this happens, you’ll know that you’ve chosen the right path.

One of the best ways to prepare yourself tobreak into the field分析的是菲尔特er your education, whether through online courses, bootcamps, or an advanced degree.

Northeastern University offersmany degrees and graduate certificatesfocused on teaching students the knowledge and skills they need to be successful in an analytics-focused career.

For example, theMaster of Professional Studies in Analyticsis designed to prepare students for the in-demand and highly competitive field of data analytics. The program helps students build portfolios of real-world projects demonstrating competency with key technologies, visualization and communication techniques, and the ability to translate information into recommended actions.

If you’re interested inbuilding a career in analytics, take the first step by downloading our free, comprehensive guide below.

This post was originally written in March 2017. It has since been updated for accuracy and relevance.