Recently, we have seen a surge in students asking questions about how they can break into a career in Analytics and Business Intelligence.
If you’re someone I have had this discussion with, then you’ve likely already heard me say something like this:
Your first Data Analyst job is the hardest one to get. With some experience, your second job will be a lot easier to land. By the time you are looking for the third job, it will feel like a relative cake walk, and you’ll be able to choose from only the most interesting opportunities.
A career in Analytics and Business Intelligence can be a wonderful path. It can be rewarding in many ways, and once you have some experience, you’ll be on the right side of supply and demand.
All of this is true. However, it does not help answer the question on many students’ minds at the moment…
How can I get a Data Analyst job if I don’t have any experience?
That is what we are going to talk about today. The advice in this article is written specifically for the aspiring Data Analyst, trying to land their first role in the field. That said, if your goal is to get your first “Data Science” job, or to land your first role in any field, a lot of this information should still be useful.
First we should break the broader problem down into some distinct areas we can discuss...
- Which skills can you acquire that will make you more valuable and attractive?
- How can you make sure you look great when you are seen?
- How can you make sure more employers are seeing you?
- How can you prepare yourself to turn an interview into a job offer?
In this article, we will focus on 1, 2, and 3 - the things that you should be doing to increase your likelihood of being invited for a job interview.
If you are interested in job interview advice, check out our Analyst Interview Prep Guide.
Let’s dive in.
Which skills can you acquire that will make you more valuable and attractive?
Here, we will focus specifically on which technical skills, or “tools”, are the most attractive to employers. Keep in mind though, that technical skills are only part of the bigger picture. Below is a list of some of the areas employers will be looking to assess in any Data Analyst candidate. We discuss these in more detail in the Analyst Interview Prep Guide, so in this article, we will really hone in on technical skills:
- Cultural fit
- Quantitative problem solving ability
- General business acumen
- Ability to self-teach
- Enthusiasm for the opportunity
Let’s talk about tools. If you want to be an Analyst, where should you start learning?
There isn’t necessarily a wrong answer, but my personal suggestion is that you should master Microsoft Excel before you spend time anywhere else.
Why Excel? There are so many reasons…
Excel is everywhere. I would challenge you to find a competent organization that isn’t doing at least something in Excel. With Excel, you can guarantee your skills will work at nearly any company.
In my opinion, Excel is the perfect entry point to a data career. For someone who doesn’t have coding skills or database experience, learning Excel is still relatively easy. It also gives you so much runway to add value and to pivot into other areas down the road...
- Spreadsheets are a great way to get exposure to “column and row thinking”, which will come in handy later if you ever want to jump to SQL, Tableau, or Power BI.
- Learning Excel formulas are probably the easiest way to dip your toe into logical “programmer thinking”, because it is so easy to tweak formulas and data inputs.
- Pivot tables are a powerful and easy way to start slicing and dicing your data into segments you can analyze, all without a single line of code.
- Excel’s built-in graphing tools give you an opportunity to learn about data visualization
- If you start to find yourself repeating the same tasks in Excel, you might pursue the use of VBA for automation
Excel works great as a standalone, and pairs extremely well with a number of other tools. With all of the features listed above, there are Analysts who get so good at Excel they can build their entire career around it. Others will pair Excel skills with other tools, but I guarantee they will always find use for Excel even if they move on to become a “SQL guy” or a “Python guru”.
If you think Excel is a good fit, check out Maven’s Excel Specialist Path for a good framework of the various Excel skills you should aim to learn. Even if you aren’t interested in Maven’s courses, it can still serve as a great outline to guide your study.
Where should you focus after Excel?
If you feel like you really have mastery of Excel, and you are looking for the next area to expand into, any of these will be good choices…
- SQL for data retrieval and manipulation
- Dashboarding tools like Tableau and Microsoft Power BI
- Web Analytics tools like Google Analytics or Mixpanel
- Modeling tools like R and Python (avoiding SAS and SPSS)
Personally, I think these are listed in the correct order, but there is also a very valid argument to be made that it depends on exactly what you are excited about. If you have a strong passion for one of these buckets, bump it higher on the list. There is no better predictor of success in any of these than genuine enthusiasm, which will keep you going when things get challenging.
One word of caution...if you are really excited about modeling and data science tools like R and Python, I strongly encourage you to pick up SQL first. Trust me, I get it. Modeling and data science work is a lot of fun with the prepared datasets you will use in a data science course. However, in the real world, on the job, no one hands you that dataset. You are going to need to learn how to dig for yourself and create the dataset for your modeling work if you want to add value. Being good at R or Python alone, without knowing how to access data using SQL makes you somewhat impractical, and not very likely to create value on your own. There may be some exceptions where a person like this could be valuable if paired with someone else who can aggregate data sets for them, but consider this the exception and not what you should be aiming for. Aim to be “full-stack”. Be able to aggregate the data, package it in the right form, do the modeling work, and then translate your findings to actionable business recommendations. That’s someone I would hire. That’s SQL + R or SQL + Python.
If the above doesn’t sell you, here is a little more on why SQL is so valuable to learn…
SQL is used by almost every company that works with large and complex data sets. Data is typically stored in a relational database, and accessed using SQL. Like Excel, learning SQL gives you a skill that is extremely portable between companies.
SQL is relatively easy to learn. As far as coding languages go, SQL is fairly simple… you want to select the data in the address column from the customers table? Type SELECT address FROM customers. Done. The syntax is pretty simple to pick up. I’m not a “code guy” myself. I didn’t study computer science. I picked up a SQL book and learned. You can learn too. It’s not too bad at all.
Supply and demand for SQL talent is extremely attractive. Like Excel, almost all companies need to use SQL. So you would think the number of people who have mastered SQL would be up there with the number of Excel jockeys, right? No way! Far fewer people are truly great at SQL coding and analysis. When you know SQL, you get bucketed into “technical talent”. Demand is high, and supply for talent is hard to find. Salaries go up. This is where you want to be!
If you are looking to beef up your SQL skills, check out our MySQL Specialist Path. Again, even if you are not interested in Maven’s courses, seeing the curriculum can serve as a guide for what you should aim to learn in your studies.
After SQL, I recommend you spend some time with data visualization software like Power BI and Tableau. There are other platforms out there as well, but these two are the most widely used, so I really wouldn’t advise spending time learning another platform, unless your current employer uses something else and you can learn on the job.
Students will often point out that Tableau and Power BI serve the same function, and ask whether it makes sense to learn both. It is a great question. Where Excel and SQL are used by almost every employer in some capacity, the dashboarding tools market is more split. Together, Power BI and Tableau dominate the market, but it is a bit more rare to see one company adopt both platforms. They typically choose one or the other. I do not see either of them going away anytime soon, so learning the basics of both is probably the best move to maximize your attractiveness to the largest employer base. Plus, learning Power BI will likely make you a better Tableau user, and vice versa, as you’ll see how things are done in different platforms and learn the nuances more deeply.
Check out our Tableau Desktop course and our Power BI Specialist Path if you are interested in picking up these dashboarding skills. We also have an Advanced Tableau course in the works, which we hope to launch by January.
The next category of tools I mentioned learning is web analytics platforms. There are lots of them out there… Mixpanel, KISSmetrics, Clicky, the list goes on. These platforms generally all serve to help businesses understand what is happening on their websites and where their traffic is coming from.
With all of the web analytics platforms out there, the market is much more fragmented. So for the most part, learning one of these platforms does not give you much appeal to employers, because it is unlikely that they use the same platform. There is one exception to this… Google Analytics. “GA”, as it is often referred to, is the most widely used. The free version of GA is a great platform, and it is extremely easy to implement. For these reasons, many employers have GA running in some capacity. If you learn Google Analytics, the concepts you learn will also apply to other web analytics measurement platforms.
My advice on web analytics tools? Spend the time to learn Google Analytics, and ignore the other platforms until you work for an employer who uses one of them, then pick that one up.
At the time of writing, Maven does not currently offer a Google Analytics training course. We are actively looking for a great instructor. If you know someone who loves GA and would get excited about becoming an instructor, we would love to hear from you. Feel free to message John on LinkedIn, or share your recommendation in the chat on the Maven website.
The last skillset group on our list is data science and modeling tools like R and Python. If you are interested in this, I strongly recommend that you pick one of these and that you pair it with SQL so you can retrieve and manipulate your own datasets. They are each great, and while there is some advantage to learning a second one, there are diminishing returns. I also recommend avoiding SAS, SPSS, and Stata. These are great tools too, but they do mainly the same things, and not all employers have licenses for these programs, so your skills with these are not quite as portable from job to job. Stick with R and Python if you want to be a Data Scientist.
Like SQL, the supply and demand dynamic for people with these skills is fantastic. Lots of employers are looking for skills here, and there aren’t enough people who are true masters. One thing to pay attention to - if you see a company looking for “Python” skills, you should try to clarify whether they want you to be a Python Programmer, or a Python Data Scientist. Python is an interesting language, in that it can be used for both programming and data science. However, the skills are quite different. Just make sure you are thinking about this if you are looking at job descriptions.
At the time of writing, Maven does not currently offer any Python or R courses. They are quite valuable tools, but for the time being we have chosen to prioritize going deep on the core Business Intelligence tools and wait on adding any Data Science courses to the library. This could change in the future.
If you’ve stuck through this long-winded discussion on technical skills, good for you! We’re ready to move on to our next section…
How can you make sure you look great when you are seen?
Alright, so now you know the skills you need to master to look attractive. Next, you need to start thinking about how employers will start to see you.
In general, you’ll want to be thinking about two things - your online presence, and your resume. I would recommend you focus on them in that order.
Before you read any further, go to Google.com and type in your name. What comes up?
Try to put yourself in an employer’s shoes when looking at the search results. Do you look like a bright young professional who is all about Data and Analytics? Or is your Facebook account public with plenty of inappropriate pictures that make you look like a party animal?
If anything seems questionable, see if you can remove it. Make the Facebook and Instagram pictures private. Remove anything you’ve posted somewhere that you wouldn’t want your employer to see.
If there just isn’t much information on you, that is actually okay. It’s better to have nothing show up than to have results that are “strikes against you”. This isn’t the time to try and beef up your results. We are just talking about damage control right now. Remove the bad. Don’t worry about adding right now.
Next, let’s go to LinkedIn and do the same exercise. LinkedIn is where every recruiter or hiring manager will look to check you out, in addition to looking at your resume. Search for your name. You should be able to find yourself. If you find yourself, skip the next paragraph. If you don’t, read it!
Don’t have a LinkedIn profile? Make one. LinkedIn is the new resume and business card. If you want to be in business, you have to have it. If you’re a brilliant Software Engineer who loves being described as “quirky”, then you may be an exception and can ignore this advice. But if you want to be a Data Analyst or anything close, you are firmly in the realm of “business person” and you have to have a LinkedIn profile.
Next, check out your LinkedIn profile page. Make sure to look at the view that other people will see, vs your signed-in account view. If you don’t know how to do that, Google it.
How does your profile look? Again, try to put yourself in the shoes of an employer looking to hire a Data Analyst…
- Does this person seem to have the right education and skills?
- What are this person’s hobbies and interests they list? Are they related?
- Is this talking about relevant things? Are they data-obsessed?
- If they post, what are posts like? Are they posting negative energy out into the internet? (this is a very bad sign. No one wants to hire “the complainer”. No posts is far better than being an internet whiner)
Some things you should do to improve your LinkedIn image…
- On your Profile, use your Featured section to pin posts you are proud of which are relevant for your job search. Take a look at Santiago’s Featured section as a great example. He posts a full project he did, complete with SQL code, Tableau data visualizations, and business recommendations. He also shares 2 certificates he earned for SQL course work. His Featured section screams “Data Analyst”. Does yours?
- On your Profile, make sure everything seems as relevant as it can be to Data Analysis. Do you have previous experience? Internships? Can you discuss any data-related projects you did here in the descriptions of those? Don’t lie, but choose to focus on the specific things you did that best apply to what you want to do in the future.
- On your Profile, make it clear you are open for work. You can even put ‘Data Analyst - Seeking Opportunities’ as your headline.
- Share fun data posts regularly. When people look you up, they see your activity. You want them to think “this person seems really interested in data”
- Following people who talk about data. This is a great way to learn, and to see the types of things they are interested in. Maybe you’ll start talking about similar topics as you learn.
- Comment on other people’s posts if they are relevant, when you can add value.
If you do all of the things we discussed above, then when a hiring manager looks you up online, you’ll look like a great candidate.
The next place we want you to look great is your resume. A lot of the same concepts we talked about for LinkedIn apply for your resume too. In general, the goal is to make your resume scream “Data Analyst”. For a detailed walkthrough of how to improve your resume, check out our Data Analyst Resume Tips article.
After you take care of your LinkedIn profile and your resume, you’ll be looking good when someone finds you. Next, you’ll want to focus on being found more often.
How can you make sure more employers are seeing you?
At this point I will make the assumption that you’ve built some valuable skills, you are putting them on display in the right ways, and you’re ready to focus into getting more attention.
A fairly common thing we see with young professionals is doing the first two steps decently well, and then completely undervaluing this last part. Don’t let that be you.
What good is a top notch resume and LinkedIn profile if no one ever sees it?
Here are the steps you should take (we’ll talk through each one)...
- Make a list of all of your allies
- Make a smaller list of your high value connectors
- Make an initial list of your target companies
- Follow data influencers and relevant hashtags
- Beef up your LinkedIn connections
- Follow the companies on your initial employer list
- Get active on LinkedIn, in a targeted way
- Get good at figuring out where to look for job openings
- Apply to relevant jobs “the right way”
- Talk about data and your job search every chance you get
First, make a list of all of your allies. Basically, just jot down every adult you know who likes you. Family members, friends, professors, former employers, coworkers, etc. Spreadsheets are great for this. I recommend Excel or Google Sheets.
Next, we will refine this list a bit to cut it down to your “high value connectors”. Add 2 column headers to the right of your list of names - “Willingness”,”Ability”.
- In the Willingness column, you are going to put down a number from 1-10, with 10 being the best. This will be a measure of how willing someone would be to make a connection for you. For now, don’t worry about whether they have a network to make connections. Just rate them 1-10 on how willing they would be to help. Think about three factors… A) how well they know you, B) how much they like you, and C) how nice of a person you estimate them to be. Your Mom is a 10. The Professor where you were the ultra vocal standout student might be close to a 10. The business man in your town who doesn’t remember your name is probably a 1.
- In the Ability column, you are going to put a number from 1-10 again. This time, a 10 means they have an excellent network in the field you want to get into. A 1 means they don’t know a single person who would be good for you to speak with.
- Make a third column, adding the values from Willingness and Ability, and then sort the column based on the sum (this is why spreadsheets are awesome for this). This is your “Connector Rating”. The top of this list is going to be your high value connectors. These are the people who like you enough to help, and have a valuable network you can benefit from. We will use this list later.
Now you are ready to make an initial list of target employers. For this, don’t worry specifically about job openings. Just think about companies that you think would be a good fit. Pick companies in areas you want to work(could be close to home or somewhere else), which seem like they have a lot of data and need Analysts. List the companies, and then rank them on how excited you would be to work there. Later we’ll use this ranking. Again, Excel or Google Sheets is a great place to make this list.
Make a list of data influencers and major hashtags you should follow on LinkedIn. Who is talking about data often? Make your list, and follow them. You can learn a lot from following these people, and you can gain exposure by adding value to their conversations.
- Data influencers you should be following: Kate Strachnyi, Eric Weber, David Langer, Maven Analytics (anyone else you find interesting. These are some of my favorites)
- Hashtags you might want to follow: #analytics, #data, #businessintelligence
Don’t be restricted to the people and hashtags above. As you continue to immerse yourself in the data world, you’ll see people sharing content. When you see people genuinely adding value and sharing content that you are learning from, follow them to keep learning, and interact when you get a chance.
Next up, it’s time to increase your LinkedIn connections. This will be valuable to you later, as you will be easily able to figure out which of your allies knows someone at the companies you want to work for.
- Send a LinkedIn connection request to every person on your allies list (the ones who have an account). Use your entire list, not just the high value connectors. The more the better here.
- Include a nice note with your connection request. It’s not begging them for a connection. It’s just “yada yada yada… Looking forward to seeing you in XYZ”. Whatever, something sincere, genuine, and nice. Yes, you’re doing professional networking, but these are people you like. Have a little fun keeping in touch.
Time to start following the companies on your initial employer list. Most of them will be active on LinkedIn. For now, just be passive. Start following them, and learn the types of things they talk about. You might even get lucky and be one of the first to hear about an open position at some point. Later on, we’ll talk about how you can add value to the conversation to get on their radar.
Next, you want to start getting active on LinkedIn. You’ve started following companies you are interested in, data influencers, relevant hashtags. Now your LinkedIn feed will skew heavily toward the data world. Use your feed to learn what the conversation tends to be around. You’ll get smarter just by reading. Do this every day. You’ll also want to start engaging actively. Here’s your LinkedIn playbook at this stage…
- Read every day. Learn the conversations people are having. See how people are commenting on others’ posts. Note the comments that seem to genuinely add value, others that might feel like nonsense, and others still that are just purely trying to sell a product.
- Start interacting. Likes are fine, and you should like content you enjoy (people appreciate it) but you want to find places to insert yourself into the conversation and add real value. If questions are being asked, you can always answer. If someone posts a topic and you have some ideas to build on it or some other things they haven’t thought of, weigh in. They will enjoy the interaction, and you’ll start to get seen more. Try to stay within the data community as much as you can. You want to focus these efforts. If you see the opportunity to contribute to the conversations your target employers are having - that’s great!
At this point you have expanded your network, are learning, and are looking like a Data Analyst to anyone who finds you online. Time to start getting great at finding job opportunities. Here are some ideas to get you started…
- Recruiters. Analytics-focused shops are the best. We have an entire article talking about working with Recruiters. Read their advice and Maven’s advice on how to select the best Recruiters to partner with. Figure out the ones that would be a good fit. Follow them on LinkedIn, and send them a direct message selling yourself and telling them you would love to have a conversation with them about finding work.
- Local Publications. I live in Boston, MA. There are a number of local publications that write frequently about local companies that are hiring. Bostinno, Boston Business Journal, etc. Wherever you want to work, start following those companies and their writers on LinkedIn. You’ll hear about a new lot of jobs this way.
- Careers sections of your target companies. This is of course a great place to look, and your target list of companies should expand as you continue to learn more about the industry.
- Job boards. There are tons of them out there. Indeed, Monster, etc. This can be a lot of information. Get good at sifting through to find the jobs that might be relevant to you. If you are a recent graduate, check to see if your school has a job board. Posts here will be looking for people without much experience.
Now you know how to find job opportunities. Let’s talk about how to make the most of them by applying the right way. This is where your beefed up LinkedIn network is going to come in very handy. You see, the unfortunate thing is that a lot of resumes submitted online don’t get much attention, or never even get read at all. It stinks, for sure. You could cry about it, or you could strategize as to how you can submit your resume and application more effectively.
Worst: online application submission (likely never read or quickly discarded if read) Better: referral from someone you know to someone working at the company Best: direct referral from someone you know to the hiring manager/ other department leader
You want to be gunning for referrals. Here’s how you should play this…
- When you find a role you are interested in, check out the company’s LinkedIn page. Search for roles relative to the department. This might be ‘Analytics’, ‘Business Intelligence’, ‘Insights’, ‘Data Science’, whatever. Start looking through the employees of the company until you figure out what they call their department. Then search the department name you find.
- Make a list of the leaders of the Analytics department. You’re looking for VP, C-level if applicable, Director, Manager, etc. These are the people who will be your best “in” at the company.
- Check out the individual LinkedIn Profiles of the Analytics leaders. You should be able to see connections you have in common. This is where your High Value Connectors are going to come into play. When you find the right person at the company who knows someone who is willing to help you… bingo! Reach out to your High Value Connector, tell them why you are so excited about this opportunity and why you are a great fit. And ask them if they would be willing to make an introduction to X person for you. Try to get a direct introduction if you can. This will guarantee the right person is seeing you, and someone they know is vouching for you to some degree. If none of your HVCs are connected to the Analytics leaders, maybe they are connected to someone else at the company, or maybe one of your “less willing” connections is. You can still ask them, they might just not be as likely to recommend you. It doesn’t hurt to try. All of this is easy to see through LinkedIn. If you don’t know how, Google it.
- If you really have no connections, you could try interacting with the company or with any Analytics leaders who are active on LinkedIn. After you do this a few times, if it seems like they like you and you’ve added some sort of value that makes you seem like a “Data Analyst type”, you could work it into a conversation that you saw an open role that sounds exciting. Maybe they will be inclined to talk about it with you and make an introduction.
- As a last resort, apply online through the company’s website. This can absolutely work. Just keep your expectations low.
If you’ve read this far (good for you!) you’re now armed with an understanding of the skills that will make you valuable as a Data Analyst, how to talk about those skills once you have them, and how to make sure you get seen by the right people.
I know this sounds like a lot, but you get out what you put in. Those of you with the most ambition, I suspect you’ll follow all of this advice. For the rest of you, I hope you will at least do something.
You know what you need to do. Now get after it!
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John brings over 15 years of business intelligence experience to the Maven team, having worked with companies ranging from Fortune 500 to early stage startups. As a MySQL expert, he has played leadership roles across analytics, marketing, SaaS and product teams.
John brings over 15 years of business intelligence experience to the Maven team, having worked with companies ranging from Fortune 500 to early stage startups. As a MySQL expert, he has played leadership roles across analytics, marketing, SaaS and product teams.
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