Just landed your first data analyst job? Congratulations! Take a moment to appreciate the long journey it took to get here. And if you’re feeling a bit uncertain about where to start, that’s totally natural. It’s a big leap from skilling up in order to land a job and using those skills in a real-world context.
To get the ball rolling, the following 9 tips on how to excel in your first data analytics job will set you up to thrive. While you’ll no doubt be busy learning the ropes, focusing on even one or two items from this list should help build your confidence as you start your exciting new career in data. Without further ado, let’s get cracking.
1. Connect with your colleagues
Ideally, your new employer will have an onboarding program with a senior data analyst to help you get up to speed. But let’s be honest, this isn’t always the case. To minimize the learning curve, ask your manager to connect you with relevant people in the organization. If you can, aim to have a short meeting with the following:
- The most senior data analyst and/or data scientist at the organization.
- The system administrator or engineer who created or oversees the systems and databases you’ll be using.
- Key stakeholders, e.g. executives, product managers, engineers, heads of department (whoever will benefit from your skills and insights).
- Somebody with clear oversight of the organization’s roadmap, commercial targets, and KPIs, e.g. Head of Business Development.
- A gregarious networker who is known for being well-connected to others, both inside and outside of the organization.
Many of these people are likely to be more senior than you, but you can start with a short meeting. Briefly introduce yourself and explain what you do. Then ask them to tell you more about their work and what value they bring to the business. By having these meetings early on, you’ll quickly meet new colleagues, build rapport and get to know the business better. This will all provide you with much important context about what’s expected of you.
2. Look, listen, and take notes
There’s an old saying: “We have two ears and one mouth so that we can listen twice as much as we speak.” Keep this in mind. It can be tempting to show off by going in with a big plan on day one. However, this is especially problematic for data analysts who should always stay open to new approaches driven by facts, not intuition.
During your first few weeks, attend as many meetings as possible across the business. Take notes. Get to know existing processes. What problems does the organization face? Maybe sit in on project management updates, production, or marketing meetings. What common themes or hurdles crop up? Where could the company’s procedures be improved? What frustrations do team members have most often?
As you start unpicking the organization’s strengths and weaknesses, ask yourself where a data-driven approach might improve things. For instance, could the marketing department be capturing better data from customers to boost lead generation? Do multiple teams all require access to the same metrics? If so, could you avoid duplication of work by building a dashboard for all of them? Before devising solutions, fully understand the problems.
3. Get to know the industry
While it’s important to get to know your company, you should also understand the broader sector you’re working in. Look beyond your immediate organization to see what common themes or problems competitors and clients face, and how they’re solving these.
This might involve attending industry trade shows or speaking with experts at other organizations. Use a bit of initiative; if you have colleagues who previously worked with a competitor, talk to them. Ask what common pitfalls they faced or what worked well.
In addition to learning about the industry itself, familiarize yourself with industry-specific tools. For instance, if you use Python, there are many libraries of open source code specific to individual sectors. For instance, PyAlgoTrade is a Python library commonly used in Fintech, while PyHealth is popular in healthcare. You can find many more by simply searching for your industry on the Python Package Index.
4. Connect with peers outside the organization
Nobody can thrive in isolation. If you’re one of the few data analysts on your team, aim to connect with data analysts elsewhere. You can easily do this using forums such as Kaggle Discussions, Data Science Central, or the StackExchange data science community (to name a few).
To inform your thinking, follow prominent leaders on social media. On Twitter, you’ll find Dr. Fei-Fei Li, a renowned computer scientist and expert in artificial intelligence. Another woman leading in the field is Hilary Mason, a data scientist and founder of the tech startup Fast Forward Labs (which helps companies build their internal data analytics capability). There are plenty of people out there…who can you find in your industry?
If you prefer meetings in the flesh, connect with other data analysts at meetup.com. There are over 7,000 data analytics meetups available, occurring all around the world. Maybe there’s one near you. Even if there’s not, it’s 2021—there are tonnes of online events you can attend to develop your thinking.
5. Develop your technical skills
Landing your first data analyst job often involves taking a certified course (such as CareerFoundry’s Intro to Data Analytics Program.) But the learning doesn’t stop there. While basic skills like SQL are prerequisites for getting your first job, you’ll quickly need to broaden your skillset to keep up with the demands of the role. Luckily, there are lots of free tutorials you can find to get started. Many employers also offer training scholarships. Either way, try to take charge of your own continuing professional development.
Some areas you might want to work on include:
Python: You’ll likely need basic Python skills just to land a job, but why not specialize in a particular area? For instance, if you’re particularly interested in data viz, you could learn the basics of the Pandas graphing library.
R: You could also branch out into R—another programming language commonly used for statistical computing (mostly in the sciences). If you don’t know it already, it might not be worth learning, since Python covers many of the same functions and is easier to pick up. But the point remains: stay open to new tools.
Hadoop and Spark: These distributed computing architectures are both used for working with big data. As you start modeling datasets too large to process on your own computer, cluster-computing features are ideal for wrangling. In general, databases (and related software) are vital skills for any data analyst.
Proprietary data analytics platforms: Many data analytics tasks can now be automated using commercial software like Tableau, Microsoft Power BI, and Looker. In all likelihood, your organization will already use a proprietary tool. If not, take the initiative to try some out and recommend one to your organization and stakeholders.
- Hone your statistics skills: There’s no escaping it…a head for math is vital for data analytics. From calculating variance in MS Excel to understanding Bernoulli distribution, there’s plenty to learn! Familiarize yourself with all the statistical approaches relevant to data analytics, such as descriptive and inferential statistics.
6. Become an expert in data cleaning
The data analytics process is complex and the areas you specialize in will vary depending on your job. To illustrate, if you’re working in product development, you’ll likely need to become a bit of an expert at sentiment analysis. However, if you’re an IT systems analyst, the ability to create crisp digital dashboards is probably going to be more important.
One area where you’re guaranteed to need solid expertise (regardless of the role) is in data cleaning. High-quality, clean data is vital for obtaining useful and accurate insights. So grab as many opportunities as you can to practice your data cleaning skills. Every data analyst starts their journey with this step and it can take up to about 90% of your time.
However, you should also take any opportunity you can to flex your creative muscles in other areas, such as data visualization, presentation delivery, data engineering, or machine learning and AI. This will not only help you find other areas that interest you but it will keep your skillset open to different possible future career paths.
7. Learn to identify new data sources
You must constantly ask: “Are we collecting the right data?” This is especially important if you’re joining a small team or a company that hasn’t yet invested in its data infrastructure. Many organizations base important metrics on outdated or poor-quality data. Others rely heavily on first-party data (i.e. data they’ve collected themselves). For instance, a marketing department might rely on customer insights collected by its CRM to drive the majority of its activities. But this is likely to offer an incomplete picture. Data diversity is important and additional sources always help.
Keep a spreadsheet to curate data sources that might be relevant to your work. In particular, keep an eye out for possible new first-party data sources, and second and third-party sources that offer a different or more complete picture. The best way to identify useful data sources is to find gaps in your own data or to see where existing sources agree. Conflict between data sources is a good thing—it highlights areas that should be probed more deeply.
When considering possible new data sources, be aware of duplicate data. Just because two datasets come from different channels doesn’t mean they necessarily have different origins. You should also be sure that your data are as up-to-date as possible. For inspiration, sign up for data newsletters like Data is Plural, subscribe to subreddits like r/dataisbeautiful or r/dataisugly, or simply search online for sources relevant to your industry or role.
8. Attend a data hackathon
A great way to build your skills is to attend a data hackathon (or ‘data dive’). Lasting anywhere between a day and a week, data dives bring together data analysts with designers, programmers, and other disciplines to produce creative solutions to data-related problems. They usually involve playing around with new tech, sharing best practices, and cross-pollinating novel ideas. This may sound frivolous, but it’s excellent for trialing new approaches (something which, in the workaday world, is not always easy).
Some recent events include the European Big Data Hackathon and the UN International Computing Centre’s (UNICC) Data For Good: Global Hackathon. While data dives often involve specific tasks, you’ll always learn something you can apply in the workplace. For instance, one of the UNICC hackathon challenges involves forecasting the impact of phased, global vaccination cycles on the Covid-19 pandemic. Despite being very specific, the task will likely offer you insights into how you might use predictive analytics in other contexts.
Of course, not all data dives have to be global affairs. Check sites like Eventbrite, LinkedIn, or Twitter to see what you can find. Hackathon.com also lists many tech-related hackathons in specific industries, from fintech to automotive. If you can’t find what you’re looking for…why not organize your own?
9. Become an internal data champion
While an increasing number of organizations understand the importance of data analytics, that doesn’t mean everyone will buy in right away. As you sweep in to change processes, products, or procedures, you may meet resistance from stakeholders who feel that you’re stepping on their toes. It’s therefore important to promote the data cause. When you’re working with someone new, reassure them that you’re there to help, not to force change without a solid evidence base.
A vital part of championing data is to lead by example. Aim to use data to probe people’s assumptions. If someone more senior than you is confident about the cause of an issue, you might feel pressured to accept their view. However, until you follow the data, you cannot be sure that they are right. Data analytics often involves a tenacious disregard for what people tell you. If you meet resistance, one option is to apply a dual approach.
Let’s say you’ve been tasked with helping the HR department tackle a recruitment problem and they’re resistant to your input. Devise a new, data-driven approach that you can trial alongside their existing recruitment procedures. By collecting data on both, you can definitively identify which approach is more effective. By acting as a data champion, involving people early on, listening to concerns, and constantly showing people how the data shape your work, you’ll gradually bring people on board.
Wrap-up and further reading
There’s no denying it: data analytics is a career with a steep learning curve. But with that challenge comes plenty of rewards. In this post, we’ve offered 9 tips to help you thrive in your first data analytics job.
As you can see, with a bit of initiative and creative planning, learning on the hop can be the best way to carve a niche for yourself. Before long, you’ll be the go-to data expert at your organization!
If you’re keen to learn more about what a career in data analytics can offer, why not try a free, 5-day data analytics short course? And be sure to check out the following posts for more tips on kickstarting your new data-driven career: