How to Land a Data Analyst Internship

Will Hillier

Seeking an entry-level data analytics job but facing competition? One way to stand out is to complete a data analytics internship. Read on to explore what you can expect from a data analytics internship, and how to land one.

As data analytics grows in popularity, the field is growing ever more competitive. To land the job you want, it helps to know which area of data analytics you’re most suited to. But how to find that out? Graduates, especially, face a common conundrum. How to break into a new industry with no prior experience?

For many entering into the data industry, data analytics internships are the way forward. In this post, we’ll cover the basics:. Why you might want to consider an internship, what you can expect from one, and what kinds of companies are hiring. Finally, we’ll explain how you can land a data analytics internship. By the end of this post, you should have everything you need to proceed.

We’ll cover:

  1. What is a data analyst internship (and why do one)?
  2. What can you expect from a data analyst internship?
  3. What skills and experience do you need to demonstrate for an internship?
  4. What kinds of companies are hiring data analytics interns?
  5. How to land a data analyst internship (step by step)
  6. Making the most of your data analyst internship

OK? Then let’s get cracking.

1. What is a data analyst internship?

A data analyst internship is a period of work experience at a company or other organization. It may last anywhere from a couple of weeks to a year. Data analyst internships are usually (but not only) offered to students and recent graduates. That said, in an emerging field like data analytics, it’s not unheard of for post-grads to carry out internships, too.

Whatever the length, and whatever your age or experience, the goal of an internship remains the same. It’s there to provide you with the skills and expertise you need to take the next step in your career. Internships are by no means mandatory for newbie data enthusiasts, but an increasing number of people are choosing to go down this route. This begs the question: Why? Let’s find out.

Why do a data analyst internship?

A successful internship will provide you with what you need to enter this exciting arena. Before applying, you should consider the type of company you’d like to work for, your short- and long-term career goals, and which area of data interests you.

Internships aren’t for everyone. But presuming you’re set on this route, here are some reasons you might want to land one:

  • They’re a great way to transition from study to the workplace. An internship lets you work on real data projects straight out of the gates. They’re less high-pressure than full-time jobs, which come with greater responsibilities.
     
  • They’re a good way of testing the water. Which industry (or company) interests you? Find out before making a long-term commitment. Do you want to be a healthcare analyst? A business intelligence analyst? Want to work in big tech or city mobility? Internships allow you to explore all these options.
     
  • You’ll have an expert mentor. What better way to learn practical skills than from somebody with experience? Interns often work under senior data scientists. You’ll get to learn about different career paths and sample niche aspects of the field. Perhaps you’ll even touch on topics like machine learning or computer vision.
     
  • Build specific skills with hands-on learning. Learn new data analytics skills aligned with your career goals. For instance, maybe you want to break into predictive analytics (currently in vogue in the data job market) or to work in a particular domain, such as finance, HR, or product design? If you have specific career goals, internships are invaluable for building the necessary skills to follow your ambitions.
     
  • You might get a job offer. Employers often recruit new starters from their internship programs. Exhibit great statistical expertise and a problem-solving mindset, and who knows? You might get a full-time job. At the very least, you’ll make business contacts for the future, and gain some real-world expertise.

OK, so these are some of the benefits of a data analytics internship…but what will you learn?

2. What can you expect from a data analyst internship?

What skills will you pick up during your internship? Honestly, this varies wildly depending on the organization you’re working for, the business area (e.g. sales, business operations, or product design), and the type of analytics you work with. For instance, you might specialize in descriptive, prescriptive, or diagnostic analytics. Or you could learn about big data infrastructures, like Apache Hadoop.

Whatever you learn, most data analyst internships won’t be too high stakes. You’ll still get to explore interesting topics, but to do so in a failsafe environment. A good mentor will stretch your boundaries without overburdening you. While the exact responsibilities you’ll have will vary for the reasons described above, here are some common activities to expect.

Working with different team members

Online data analytics courses (or self-study) can often give the impression that data analysts work in isolation. In reality, they must communicate and coordinate with a wide range of disciplines. Engineers, product designers, and managers are all common roles you’ll work with. Plus, you’ll apply your skills in various business areas, such as sales, marketing, IT, or finance. An internship offers a real flavor of data analytics diversity. By assisting management, you’ll get unique insights into the way a real business is run.

Cleaning data and maintaining databases

Taken an online course or worked on sample projects? If so, you may have carried out data analyses using existing datasets that you found online. But these datasets are often carefully structured and pre-sanitized. An internship will give you access to real-world data, in all its messy, unstructured glory! You’ll get to practice cleaning data with tools like Python and Excel. You may even learn the ropes on big data structures used to collect and store information. A structured course teaches you vital theory, but the real world is the place to put what you’ve learned into practice.

Conducting data analyses

Of course, the main point of data analytics is carrying out analyses. An internship offers the chance to apply all the statistical techniques you’ve learned. To land the job, you’ll need some theoretical knowledge of statistics, as well as various analytical techniques. But once you’re in, this is your chance to shine. You can finally apply all the ‘book smarts’ aspects of what you’ve learned in a real setting.

Creating visualizations

Visualization is about presenting results in a way that makes them easier to interpret. A high-stakes analysis may be considered too risky for an intern. But creating visualizations is a safer task for someone with limited experience. You’ll likely get to apply your own techniques and play with the company’s internal data tools. These data viz tools are often enterprise level (i.e. paid tools) that you won’t have used before. Common ones include Microsoft Power BITableau Desktop (there’s also a free version if you want to try it out), Salesforce Einstein Analytics, and SAP Analytics Cloud.

Writing and delivering reports

Last but not least, you’ll get to practice the key task of writing and delivering reports. This is your opportunity to shape the narrative of data analysis into a story that informs key business decisions. You might get to play with tools such as Jupyter Notebook (a presentation software for Python),. oOr perhaps create slide decks using software like MS PowerPoint. You may simply be required to present information on GitHub. Either way, this is arguably the most important part of the entire data analytics process. Sharing your findings effectively will help drive the business forward. This makes it a vital skill to learn.

3. What skills and experience do you need to demonstrate for an internship?

While you’ll need basic data analytics skills before landing an internship, don’t fret about prior experience. This is the whole point of an internship: to gain expertise. It’s very unlikely that companies will expect you to have any prior data analytics jobs under your belt.

If you do have prior work experience, ideally in an office environment, this certainly won’t hurt your case. But it’s not vital. In fact, as the world moves toward remote working, you might find that your internship takes place from home.

As for the key skills you’ll need, again, this is why you’re doing the internship—to pick up new abilities. That said, knowledge of the following is vital.

Basic coding skills

You don’t need to be an expert coder, but you should at least understand the basic principles of coding. Python is an easy language to start with. It has loads of libraries and pre-existing code to play with. It’s also easy to learn and much sought after in the field of data science. We’ve covered how to learn Python in this guide.

Knowledge of statistical analysis and probability

Statistics know-how is fundamental to data analytics. At the very least, you’ll need some theoretical knowledge of topics like descriptive statistics (e.g. mean, median, mode, variance, and standard deviation), probability (e.g. mass function, normal distribution, the central limit theorem), and inferential statistics (e.g. correlation, confidence intervals, and hypothesis testing). If you don’t know what these are yet, this is a good place to start your learning.

Data cleaning skills

Having basic data cleaning skills is vital. Even if this only means de-duping datasets using MS Excel formulas. If you have more sophisticated experience, that’s great, but don’t worry if not. Often, companies use tailored software designed specifically to clean their own data sources. Part of your internship might involve getting to grips with these proprietary tools. So long as you have a solid understanding of data cleaning and why it’s important to the data analytics process, that should be enough to start with.

Soft skills

Data analytics isn’t all about data. It’s also about your ability to communicate with others, time management, cognitive functioning, interpersonal skills, and so on. These are important in any job. However, when you’re inexperienced in a new field, companies will be on the lookout for these skills in particular. That’s because, at this early stage in your career, they’ll have little else to go on. 

An understanding of the terminology

If you’re new to the field, nobody will be expecting you to be an expert. You don’t need to know how to create machine learning algorithms or build recommendation engines. Nor is it likely that you’ll be applying natural language processing (NLP) or other artificial intelligence techniques. All these things require specialist knowledge of things like linguistics and deep learning. However: you should have an understanding of what these things are. So do your research, especially in areas or fields that you know the company works in.

This last point sums up the mindset you should adopt when embarking on your data analyst internship. In short, you need to know the principles of data analytics but you won’t be expected to do everything. Don’t put too much pressure on yourself, either, or the whole experience will be stressful rather than useful.

4. What kinds of companies are hiring data analytics interns?

Medics work in hospitals. Politicians work in government. Data analysts work, well, everywhere! Data analytics is a booming business. This can be a double-edged sword when looking for internships. It means you’ll find no shortage of them available, which is, of course, good news. The challenge is: where to apply? Here are some of the categories of companies looking to hire data analytics interns.

Big tech companies

Global tech firms like Google, Facebook, Amazon, Airbnb, and Uber all use big data in a big way. You’ll often find internships available on their websites. Of course, for big names like these, expect stiff competition, even for low-paid internships. The prestige of having worked for one of these companies is part of their appeal. But they also offer niche experience in areas like HR, product design, marketing, and sales.

Other global corporations

Older corporates, e.g. accounting firms like KPMG or PwC, often use business analysts. Big pharma is another sector placing data analytics at the core of their business. These more historic firms can offer interns a different insight. Namely, how is data being used to support businesses that haven’t traditionally used these kinds of practices? Companies like these have an extensive corporate reach, too. This means you’ll find internships across a variety of business functions.

Start-ups

Big tech has embraced big data. Older firms are starting to incorporate it into their business models, too. Meanwhile, start-ups tend to include data analytics as part of their business from the word go. However, being small, start-ups often only hire a single data scientist, supported by interns. You’ll learn more on the hop, with greater responsibilities and variety in the type of work you do. You’ll also have to help out across the business, rather than focusing on a particular function. This is ideal if you want a broader taste of how you can apply data analytics in a variety of contexts.

Dataanalytics software providers

With the big data boom, many IT companies are cashing in. They’re creating slick new products that make the whole data analytics process far easier. Many of these are start-ups with a specific analytics focus. Others include large tech firms like SAP or IBM, who have moved into the field. Either way, companies like these are one popular way to break into the industry. Ideal if you love coming up with new tech solutions to big data problems!

Government and non-profit

National and local government and educational institutions are all starting to embrace data—some as part of their business functions, others in specific scientific disciplines. For instance, big data is increasingly used to help shape government policy. Meanwhile, if you have a degree in the sciences, such as biology or astronomy, data analytics is key in universities and research bodies, too. 

The best way to find data analytics internships is to search online. Check out company careers pages, and browse jobs on sites like LinkedIn or Glassdoor

5. How to land a data analyst internship

Now to the crux of the matter. How can you land yourself a data analytics internship?

Step 1: Build an online presence

Before you start applying for roles, try to build your online presence. Create a portfolio website and put any sample projects you’ve done on sites like GitHub. You can learn how to create a data analytics portfolio here. If you enjoy social media, sites like Twitter are great for finding and sharing articles you’ve read, or even for sharing your own blog posts. Social media is also a great way to follow and learn about companies you might be interested in working for. Which takes us to step two…

Step 2: Do your research

Cast the net wide and see what positions are out there. If you’ve done a data analytics course (a structured course or free tutorials) you should already have an idea of what sparks your interest. Keep an eye on the types of companies that are seeking data analyst interns. Research what they do. Focus on applying to the ones that you feel most passionate about.

Step 3: Compile a list of applications

Searching for internships can be a bit overwhelming. To make life easier, compile a list of positions that catch your eye. If you’re organized, it helps to set up a spreadsheet. Include the company name, application deadline, and list the documents they require. You can also add details like what the role pays (know your worth—avoid unpaid internships!) and some notes on the company. When applying for numerous roles (which is necessary…it’s a numbers game) it’s good to have all the info in one place.

Step 4: Tailor each application

Once you’ve done some background research, it’s time to start filling in applications. This is the tedious part of the process, but it’s vital to get it right. Craft each application individually, and avoid using generic text where possible. Since you have little or no experience, highlight your soft skills. Show that you have some insight into what the company does, who their clients are, and so on. When you’re done, triple-check that you’ve provided all the information they’ve asked for. There’s nothing more annoying than to be automatically disqualified on a technicality!

Step 5: Cover letter and resumé

Next up: time to polish your data analytics resumé, and if necessary, write a cover letter. The latter isn’t always required, but if it is, aim for no more than 500 words (or whatever limit the company specifies). Keep it punchy, professional, and maintain focus on the data analytics skills you have and what value you’ll bring. When you’re done, get someone to check your entire application over. When you have, you’re ready to submit. You can learn more about how to write a job-winning data analytics resumé here.

Step 6: Patience!

The hardest part of any application is waiting for a response. Keep in mind that the recipient is usually very busy. They’ll get back to you in their own time. If you haven’t heard anything within two weeks, it’s OK to send a polite follow-up email. Be prepared for silence, though. It’s frustrating when people don’t reply, but sometimes it’s the way things go. If they do reply in the negative, be sure to ask for feedback. They might not offer any, but if they do, use it to help with future applications.

Step 7: Interview

Invited to interview? Great news! But it’s important to prepare. Make sure you swot up on your statistics knowledge and general data science topics and themes. You’ll also need some solid examples of projects you’ve worked on. In an ideal world, these will be data analytics projects or visualizations from your portfolio. But non-data-related projects will also do. What’s important is to have examples of your approach to problem-solving, which is a key part of data science and data analytics. If you’re doing an online course, or have recently graduated in a relevant field such as math or computer science, speak to your mentor or tutor. If all goes well, you’ll hopefully get the job!

Other ways to find data analyst internships

Feeling confident? An alternative to applying for listed roles is to approach companies directly. Here’s one approach:

  • Make a list of companies you’d like to work for.
  • Visit their website and find their case studies section.
  • Select a topic that interests you and look for similar datasets online.
  • Carry out your own statistical analysis on this data.
  • Write a report and submit it, along with a cover letter, resumée, and portfolio, asking if they’d be interested in hiring a data analyst intern.

This is a high risk, but high gain approach. It’s high risk because it involves a lot of upfront work with no guarantee of a response. But it’s high gain because if you catch someone’s eye, it may result in an internship that avoids the competitive application process. At the worst, you’ll have an industry-relevant personal project to add to your data portfolio. And that will help for future applications.

6. Making the most of your data analyst internship

So, you’ve landed an internship? Congrats! Now to make the most of it. As is the case throughout the application process, preparation is key. This is true for data analytics in general. You can never have too much practice.

Your goal may be to finish your internship with an offer of full-time employment. Here are some tips to increase your chances of this happening, while picking up important skills along the way.

Set clear goals

You’ll meet your mentor early on and throughout your data analytics internship. In your first meeting, they should help you set goals for what you’d like to achieve. If they don’t, take the initiative and suggest that you’d like to set some. For instance, maybe you want to become more proficient using Python libraries? Or to learn a new programming language altogether, like R or Ruby? By setting some targets, you can create a plan. This will greatly increase your chances of success.

Listen carefully and focus on the job

The Greek philosopher Epictetus said: “We have two ears and one mouth so that we can listen twice as much as we speak.” This is a good maxim to keep in mind. Basically, don’t go in guns blazing as if you know it all. Ask questions as needed, but spend time listening, observing, and practicing new skills. Take notes during meetings. Keep to-do lists of your tasks. Observe the company culture, and job-specific practices, such as how they approach data storage. 

Complete a project to practice as many skills as possible

The aim of your data analytics internship is to gain as much experience as you can while honing key skills. So long as you aren’t overloaded with work, volunteer to pick up tasks as they become available. Conducting a project from start to finish, you’ll learn all the different aspects of the process and how they connect. Despite what you’ll have learned on a course, you’ll find it’s not as clear-cut as sourcing data, cleaning it, analyzing it, creating visualizations, and reporting your findings. Going out of your way to take on new responsibilities also shows passion and commitment. This is never a bad thing and will likely increase your chances of a job offer.

Build your network

In an unfamiliar environment, it’s all too easy to keep your head down and focus purely on the job. This is no bad thing, but you should reach out to others, too. Get to know your co-workers. Even a chat on a coffee break will help you build a rapport. Perhaps you can then ask them a bit more about their journey into data analytics. Did they start with a data background? Or perhaps they began in another field, such as software development? Learn more about the company, and share your ambitions. This doesn’t mean you’ll be offered a job, but you’ll have laid foundations for some fruitful relationships.

Reflect on what you’ve learned

Once you’ve completed your internship, be sure to seek out feedback on your performance. This can be hard to do, but it’s important for your continuing professional development. You can also review your achievements against the plan you made at the start. Did you learn everything you wanted to? What new skills can you add to your resumé? Did your internship highlight any key skills gaps that you’d now like to fill? Even if you feel that you’re not interested in this area of data analytics, this in itself is an important thing to have learned.

Final thoughts

In this post, we’ve explored what data analytics internships involve, why they’re beneficial, and how you can go about finding one. You should now have everything you need to start your hunt!

As competition for data jobs increases, a data analyst internship can be a great way to transition into the workplace. This approach allows you to test the water before making a long-term commitment. You can also apply the key data skills you’ve learned in a real-world environment. Finally, you’ll make new business contacts, gain invaluable experience and—if all goes well—even receive a job offer.

Data analytics is a fast-expanding industry. Big tech companies, traditional corporates, start-ups, IT firms, and even national and regional governments are embracing the potential of data. If you’re completely new to data analytics and what to find out more, try our free, five-day data analytics short course. Or check out the following:

What You Should Do Now

  1. Get a hands-on introduction to data analytics with a free, 5-day data analytics short course.
  2. Take a deeper dive into the world of data analytics with our Intro to Data Analytics Course.
  3. Talk to a program advisor to discuss career change and find out if data analytics is right for you.
  4. Learn about our graduates, see their portfolio projects, and find out where they’re at now.