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5 Steps To Switch Your Career To Analytics

... Piyanka Jain/Forbes:

These lessons are part of Aryng’s Analytics career transition path series for individuals. 

If you devour all things analytics, even to the point of setting up Google alerts to help you begin or progress in your analytics career, then you’ll find this five-lesson blog series helpful.

These lessons are part of Aryng’s Analytics series for individuals looking to transition to a career in analytics or who are new to an analytics role. I hope to answer all the questions I have received from readers of my blog on “Three Steps to Identify the Analytics Training You Need”. Before we go further, understand your fit to an analytics role by assessing your own analytics aptitude. If you don’t have high analytics aptitude, you won’t have fun being an analyst.

Lesson 1 – Understand the analytics landscape and identify your ideal analytics job

So, what constitutes an analytics job? Is it the same as big data job?

The analytics landscape is fraught with over-hyped and over-used terms, so before we go further, let me briefly clarify some of the terminology. (This subject is discussed in-depth in my book, “Behind Every Good Decision”, so feel free to start there as well. You can also download Chapter 7 of the book FREE here, which discusses analytics talent requirements in detail as part of the leadership toolkit.)

Believe it or not, “analytics” is not synonymous with “Big Data” even though these days it is often mentioned in the same breath. Let’s discuss that in a moment.

First let’s define “analytics” vs. “business intelligence” (BI). Business intelligence and analytics are actually two distinct processes that involve different tools and serve different purposes.

When a user interacts with a system (such as when you checkout groceries from your local supermarket), data is produced, collected, cleaned and stored using data solutions including Teradata, Hadoop and Oracle. Data is then accessed via reports and, increasingly, via graphical dashboards. BI includes all components of the operation, from when data is collected to when it is accessed.

Analytics, on the other hand, is the process performed on data that has been delivered by BI for the purpose of generating insights to drive decisions, actions and, eventually, revenue or other impacts. Data is converted to insights using analytics tools such as SAS, R and Excel.

Now let’s talk about Big Data. Big Data’s ever-increasing volumes, variety and velocity (known as the Three Vs) create issues of storage and visualization that make traditional business intelligence systems unstable. Big Data is thus a business intelligence issue, not an analytics issue. Our focus for this lesson, then, must exclude Big Data

What analytics jobs interest you?

Once you know you are interested in analytics, the question is, “What kind of analytics job is right for you?” Get an idea about the analytics jobs out there by typing “Analyst”, “Analytics” or “data scientist” in job forums such as LinkedIn, or Monster. Below are some of the key job titles you will find, mapped to three major job categories. I will discuss differences in these job categories a little later. Note: If the title includes “Analyst” but the job doesn’t require analyzing data, then it is not an analytics job. For example, a “Business Process Analyst” does not have an analytics job and we will not be talking about those careers here.Slide3

From the chart above, take for example, Marketing Analyst. Most jobs with that title fall in the Business Analytics Professional job category. Some of these positions need advanced analytics skills and thus fall under the Predictive Analytics Professional category. Data Scientist, on the other hand, is used very broadly and vaguely with jobs falling under all three categories. Some data scientist job descriptions seem to seek applicants strong in all.

Lesson 2 - Identify Your Analytics Skill Gaps

Now that you know your target jobs, let’s dig deeper into the skills needed for these jobs and map them to your background.

Think of the job like a big Lego® ship and your skills are the pieces. The more pieces from your background that fit into the ship, the better you’ll fit that role—and the more likely you’ll interview for and land the job.

Analytics job skills sought by employers can be broadly classified into seven buckets. Watch for these requirements in the job description, along with some common verbiage to describe each skill:

  1. Analytics Skills: “Passion for data analysis supported by personal and professional experiences”, “Strong familiarity with statistical concepts”
  2. Tool Skills: “Comfort with Excel and other standard productivity tools”, “Technical skills such as SQL, Python, R/SAS/Stata a plus”, “Knowledge of Omniture, Google Analytics required”
  3. Education: “Bachelor’s degree in a quantitative or technical field”
  4. Problem-Solving Skills: “Troubleshoot and prevent technical issues”, “Creatively solve problems while operating in a dynamic environment”
  5. Communication Skills: “Immaculate written and verbal communication”
  6. Functional Background: “Previous work experience in performance marketing, media buying, lead generation, or related spaces”
  7. Industry/Work Experience: “Previous work experience in financial services, consulting or other quant/data driven fields”, “Up to 3 years prior work experience”
  8. Now, select a few job descriptions that interest you and create a matrix of job requirements against your background to identify the gaps. If you followed my advice from Step 1: Identify Your Ideal Analytics Job, you would likely be applying to very similar jobs with very similar requirements.

    Below is the beginning of a sample job skill gap matrix. Use this as a guide to build yours, based on jobs you are targeting. Note: Yours may look very different than the sample here! Also make sure to include not only the analytics skills and tools requirement but all seven categories.


    It’s likely your own matrix will identify gaps for Analytics and Tools Skills requirements as your experience in these areas may be limited. You may already meet Education requirements, since they are often fairly broad. However, if you don’t have the precise education required, you may be able to document course work you’ve completed and meet the requirement. Also, the education job requirement becomes less relevant as your years of experience increase. For deep dive on advanced stat skills vs. business analytics skill, check out the 80/20 rule of analytics.

    Finally, I propose one other item to consider as your map your skills against job requirements. As you look to transition your job, I would highly recommend finding a job within your current organization. Doing so is typically the easiest move and gives you the best chance of a lateral move without compromising your seniority or compensation. If that is not possible, I would recommend looking for job with overlapping industry and functions. Doing so would allow you to check off the boxes for the remaining four items on the job skills list above. For example, if you are currently an IT professional at a university supporting operations, looking to transition to an analytics job, look within your current university or a role in operations in similar educational institute. Once you have 2-3 years of experience in analytics, it will be fairly easy for you to move to other industries and functions.

    If your own matrix reveals more skills gaps than you expected, don’t worry. Those gaps can be filled by a wide range of resources to help you prepare for your career transition. In the next blog, I talk about how and where to acquire new skills to transition your career to analytics based on your own skill gaps. Meanwhile, check out Aryng’s comprehensive Analytics Career Transition packages which includes an analytics aptitude assessment, analytics and testing training,  experience with a real-time project, mentoring sessions and optional career coaching to find and land your analytics dream job. If you are finding value here, you might also enjoy reading my book, “Behind Every Good Decision, which covers how to make decisions based on data in detail. You can also download Chapter 7 of the book FREE here. If you have specific questions for me, feel free to book 15 minutes FREE on my calendar here.

Step 3 - Choose The Right Analytics Training

Now, let’s discuss how to remedy those gaps by getting trained for your future career in analytics. And if after reading the blog, you have specific questions on analytics training you need, feel free to book 15 minutes FREE on my calendar here.

Tools are easy to learn. Mindset is not!

As you look to acquire new skills, my strong advice is this: you can learn tools from any number of online resources for little or no money (we will talk about those shortly). But learn analytics (and the mindset) from experts who have done it before. Eschew the theoretical academics for those who have worked with and used data to drive real impact in real organizations. To find real stories of real business impact from analytics, you might want to check out my book ’Behind Every Good Decision‘.

Then, get hands-on experience with a real-time analytics project, where you get to practice how to work with the stakeholders, how to lay out your analytics plan, get buy-in, execute the analysis, and influence stakeholders with your insights. Here again, be wary. Analyzing publicly available data sets is not a substitute for a real-time analytics project. Analyzing a dataset is just a subset of the analytics skills you need on the job. Analytics, when done correctly, never starts with datasets because data doesn’t speak, it responds. It responds to intelligent questions posed by stakeholders.

With those guidelines, I’ve expanded the sample needs/gap matrix from Step 2 to include my recommendations on how and where to find the right analytics skills and tools training.


Step 4 - How To Make An Awesome Analytics Resume

By now, you know the jobs you want and have the skills you need. What’s in your way? What gets you the interview?

Right Job + Right Resume + Right Time & Place (aka Luck) = Interview

You can’t control luck. But the other two, you can. And here is how can (a) find and apply for the right job and (b) write the right resume.

5 tips for finding and applying for the right job

  1. Don’t apply randomly, be focused. In the first step of this 5-step series, you did the hard work. You nailed your ideal job profile. Don’t let that work go to waste. Stick to the plan and apply only to those jobs that match the profile, build on your skill matrix and capitalize on your new training. If your newfound skills expanded your interests, that is fine–add them to your list of second priority jobs to be looked at once you’re done applying for those on your top priority list. If you want to change your top priority list, start the process from Step 1 again. But make sure, whatever you do, to target jobs identified in Step 1.
  2. Internal is the best. If you are currently employed, there is no better place to find your next job than where you already work. You have the inside knowledge and already know the products and company culture, so the only moving part is finding a new job. This approach is not only more manageable; it significantly increases your chance of landing the job.
  3. Search the right job sites. Don’t apply on random sites. Use well-established job portals with fresh job postings such as,,,, etc.
  4. Use your contacts. If you have a friend working for the company posting a job that interests you, there is nothing like sending him/her your resume with the job ID/link so they can forward your resume directly to the recruiter or the hiring manager. You can also use your contacts to get an informational interview with the hiring manager to assess your fit to the job or even the job profile before applying.
  5. Make direct contact with recruiter or hiring manager. Using your contacts isn’t the only way to get directly in front of the hiring manager. LinkedIn’s paid premium service enables In-mailing the recruiter or hiring manager for some job postings. Many positions on icrunchdata and craigslist include an email for resume submissions, which allows you to follow-up on your submission later.

8 tips for the making a strong analytics resume

  1. Tell one story. Your resume should tell the story of you as an analyst. Granted, you are transitioning your career to analytics from, let’s say, being a developer. But your resume should not read like a developer-turned-analyst. Paint one clear, coherent picture of your strengths as an analyst; everything else needs to fold under and support your analyst story. It’s a matter of what you highlight and what you don’t, while maintaining accuracy.
  2. So what? Don’t list your projects; list the impact of your projects with numbers and provide context so the reader cares. You can use PSR format – spelling out Problem, Solution, Result for each project. Instead of writing “Did A/B testing on the homepage to understand drivers of login”, tell the reader you “Drove incremental revenue of $5M by testing variants on homepage that resulted in higher login.”. Note how much stronger is the latter rendition. If you don’t know the actual impact of your work, do a guestimate and describe it as potential revenue gain. And going forward, always calculate the potential impact of the project before engaging in any assignment. Use the Sizing/Estimation technique from the hands-on business analytics course.
  3. Create a 2-second resume. Your resume should tell your story in 2 seconds. That’s about the time I spend looking at any given resume as a hiring manager to decide which pile it goes into – yea or nay. Here is how to tell your story in 2 seconds:
    1. Use a Request for Quote (RFQ) format where you list job requirements in the left column and your own experience in the right to demonstrate how you fit the role. Voila! The hiring manager or recruiter needs only 2 seconds to decide if you meet the job requirements. Here is an example. And yes, I am recommending at least part of your resume to be in this format. Mine is. rfq_resume_sample
    2. Use boldface to highlight words that add to the story, such as “5 years work experience”, “SQL”, etc.
    3. Try to fit the resume in one or no more than two pages and make sure to include a summary at the beginning to capture the most important elements.
    4. Chronology is less important at the screening stage, so the first part of your resume (even the first full page) can be a functional resume, perhaps in an RFQ format. The chronological work experience can be listed on a second page.
  4. Cover all relevant requirements. Make sure your resume demonstrates your interest and exposure or proficiency in all the requirements from the job posting. Big gaps leave big questions in the screener’s mind and often send your resume to the wrong pile. The Top 7 requirements were discussed in detail in Step 2 of the series.
  5. Show me the numbers. Always use numbers wherever possible to show scale to the reader. Instead of saying “Managed large cross functional team”, say, “Managed a team of 20, cross functional….”.
  6. Don’t use empty words. Saying you have good problem-solving skills is not going to sell others on to you as much as briefly recounting an instance when you demonstrated superb problem solving. Give examples with details, while being brief.
  7. Show the fire. Your resume should show enthusiasm and passion for what you do. That counts for more than having all the hard skills.
  8. Use active verbs. Instead of saying, “I was involved in a market research project to understand sentiments driving promoter score”, write, “conducted market research to understand customer sentiments that resulted in….”.

For more general and in-depth help writing a strong resume, you can refer to this guide from Rockport Institute. And if you are ready for a career transition to analytics and need some help, feel free to book 15 minutes FREE on my calendar here.

If you need more hands-on help on your analytics resume as part of career transition to analytics, be sure to sign up for Aryng’s analytics career transition premium package which includes assessment, training, and mentoring, as well as career coaching services including resume writing, interview prep, etc. And to get started on your career transition path, remember to pick up my book Behind Every Good Decision on Amazon or your local bookstore today. Next, we will talk about interviewing like a rock star.

Step 5 - How To Ace The Analytics Interview


Let’s talk about how to prepare for and nail the interview. (My book, “Behind Every Good Decision”, is a good companion as you complete your transition into an analytics job. And if after reading the blog series you have specific questions on the analytics career transition path, feel free to book 15 minutes FREE on my calendar here.)

Tips for interview preparation

  1. Research the company. Do your due diligence and research the company to learn about the organization’s business model, revenue stream, customer set, products, locations, rough size, etc. The Company website and searches on google with relevant keywords might be the best place to start digging. The more you understand the company, the better able you’ll be to tailor your responses within their own context.
  1. Practice your story. By now, your resume tells a story in writing, but you must fluently verbalize that story as well. As you share your background, know what to focus on and what to de-emphasize. There’s no need to take your interviewer down a dark alley with you. For example, let’s say your resume states you took a personal year-long sabbatical. If questioned about it, have a short, coherent story ready (e.g. “I wanted to travel the world.”), which doesn’t distract the interviewer. Answer simply and don’t over-share. For instance, don’t reply, “My boss was crazy, so I went on a leave of absence and didn’t want to go back so I decided to travel.”
  2. Prepare for the technical interview. If the job requires technical skills, be prepared to demonstrate your knowledge of a particular tool or an analytics methodology (e.g. knowledge of SQL). That may include using a whiteboard to illustrate your answer (for example, to write a SQL code).
  3. Prepare your questions. Yes, you are an interviewer, not just the interviewee. And yes, you must prepare a list of questions to ask the hiring manager. Perhaps from your research, you understand the overall size of the company but you don’t fully understand their revenue model. Ask questions to validate your hypotheses on how the company makes money. This shows you will make a good analyst because you are interested in the big picture.

Next, ask questions about your job responsibility. Don’t rely solely on the job description. Someone other than the reporting manager often writes job descriptions. Understand your future responsibility from the horse’s mouth (hiring manager) and your other team members. For example, if the hiring manager’s view of your job responsibility is significantly different than a peer’s reporting into the same manger, then there might be issues as you join. Spend some time clarifying roles and responsibilities now rather than later.

Tips to ace the analytics interview

  1. Show your fire. I’ve almost always gotten a job offer after an interview for a job I wanted. I think that is largely due to my enthusiasm. I am one of those folks who found their passion early on (problem-solving with analytics is one of my passions) and could articulate that passion during an interview. I can’t emphasize this enough: Passion for the job often counts for more than hard skills.
  2. Demonstrate problem-solving skills. Analysts are first and foremost problem solvers. Most good analytics interviewers present the interviewee with a hypothetical problem (sometimes a real problem currently faced by the organization) to assess problem-solving skills. Can you take a large problem and break into into small pieces, in a structured way, laying out your assumptions and facts, and then pull it back together as a solution? Wow your interviewers by not only solving the problem, but also sizing it. You can use the sizing and estimation methodology that you might have learned during your analytics course. (As a reference, look at Aryng’s hands-on analytics course syllabus.)
  3. Don’t be afraid to ask questions. If you don’t understand an interviewer’s question or you need some other aspect of the discussion clarified, ask questions—even though it may seem like you should know the information already. Answering appropriately after getting a full picture is much better than answering based on assumptions. You might be way off the mark.
  4. Be respectful and be honest. Show up on time, be fully prepared (with print outs and such), dress appropriately (whatever that means for that organization) and show respect for the interviewer’s time, even if they are going to be reporting to you.  Don’t tear down a former colleague to build yourself up. That tells an interviewer you might do the same to them someday.
  5. Show confidence but be humble. Confidence inspires. If you can show that you can do the job, you remove hesitation from the interviewer’s mind. But while you share your confidence, don’t appear arrogant or superior. Nobody likes to work with an egomaniac. Be honest about what you do or don’t know. Nobody knows everything and lies get caught sooner or later.
  6. Demonstrate good communication and listening skills. Good analysts are awesome listeners—ever-curious about the customers, products, usage, etc. Show your listening skills by letting people complete their sentences; clarify by paraphrasing or asking questions before formulating your answer. Be brief and succinct. Don’t go on and on. You don’t need to fill up the entire hour to show you are good.
  7. Demonstrate impact. Just as you showed the impact of your work in your resume, make sure you reinforce it in your verbal commentary of your background, e.g. “I did a project worth telling about because it drove ‘x’ impact!”
  8. Conclusion and follow up. At the end of the interview, ask for next steps in the hiring process, whether anything additional is needed from you, and how you stand up against the other applicants. Then follow up with nice, specific thank yous to show you enjoyed the interview process and why you would love to work there.

If you need more hands-on help to prepare for your analytics interview as part of your career transition to analytics, be sure to sign up for Aryng’s analytics career transition path – premium package, which includes assessment, training, and mentoring, as well as career coaching services, including resume writing, interview prep, etc.

This Article Origially Appeared on Forbes


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