How to Become a Better Data Analyst with Proven Strategies

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Written By Billy Chan

A UK-based senior data analyst with a journalism background. Passionate about teaching others how to become data analysts.

Ever wondered how to become a better data analyst? Let me toss a few nuggets of wisdom your way. Just like many of you, I started my journey without a roadmap, armed with online courses and a boatload of determination. Fast forward nine months, I wasn’t just analyzing data; I was leading the pack as a senior data analyst. So, what’s the magic formula?

Better data analysis skills equal a beefier paycheck and a telescope that sees further into your career galaxy. It’s simple math, really. And who doesn’t like the sound of more zeros in their bank account?

In the data realm, standing still is akin to walking backward. Data analysis is a beast that’s always sprinting forward, and you’ve gotta match its pace or—brace yourself—end up in the dust. Remember, it’s not just about keeping your job; it’s about being the ace in a deck full of jokers.

Getting from good to great as a data analyst isn’t a walk in the park, but hey, who enjoys boring strolls anyway? It’s about embracing the sprint, enjoying the hurdles, and finishing with a high five from success. Keep this in mind, and you’ll not just survive; you’ll thrive.

Foundations First: Strengthen Your Technical Base

Pivoting to the essentials can supercharge your data analysis journey. Are you comfortably nestled in the basics? If not, it’s time to hit online courses. Let’s boot up and reboot where needed:

Talk SQL Like a Pro: If SQL were a coffee, it’d be your double espresso. Mastering SQL isn’t optional; it’s your lifeline in the data world. Dive into the deep end with “SIMILAR TO” patterns and “REGULAR EXPRESSION” wizardry. When others peek at your code and see these, they’ll know you’re playing in the big leagues.

Tableau, Your Canvas: Ever seen a Tableau dashboard so stunning you wanted to frame it? That’s the goal. Since Tableau is the window through which your work is viewed, make it count. Advanced Tableau skills make you the Da Vinci of data—crafting visual stories that stick.

Brush Up on Your Stat Speak: Whip out those z-scores and t-tests! A solid grip on the statistical classics—mean, median, mode, standard deviation, and correlation—is a must. They’re not just fancy terms to impress at dinner parties; they’re the bedrock of good analysis. In a sea of data enthusiasts, those who can deftly wield these concepts tend to shine. Mathematics and statistics are not a must in entry-level analyst jobs, but are crucial when you progress your career! Check out this article to find out more about the role of maths in data analysis.

Now, before you run off to conquer Python and R, let me hit the pause button. While these languages are powerhouses, they often play second fiddle to the show-stopping performances of statistics, SQL, and Tableau in many data analyst roles. They’re the behind-the-scenes crew, important but not always in the spotlight. So, prioritize what gets you noticed and saves the Python wrangling for later.

Remember, being a standout data analyst isn’t about hoarding skills like a squirrel with nuts. It’s about choosing the right tools and mastering them. Fine-tune your stats, make SQL your second language, and let Tableau be your gallery. Do these well, and you’re not just another data analyst—you’re the one they call when data needs to not only talk but sing.

Mastering Advanced Data Tools and Techniques

Diving into the realm of advanced analytical tools is like upgrading your old hatchback to a sleek sports car—it’s a thrilling ride that takes you places faster and with more style.

Advanced Analytical Software Skills

For instance, gaining a deep understanding of SQL databases opens up a world of possibilities, where you can manipulate data with finesse, using PostgreSQL or MySQL to perform complex window functions or craft intricate queries. Imagine the joy of wrangling data effortlessly in R, making predictive models your playground, or harnessing Python’s powerful libraries like Pandas and Scikit-learn to slice and dice data or predict trends. These tools are not just flashy; they’re the engines of modern data analysis.

Now, let’s chat about Excel—yes, that tool we all thought we knew until we discovered its layers. Advanced Excel skills go beyond simple spreadsheets; they’re about mastering the art of complex formulas and array functions. It’s about painting stories with data using advanced chart types and dashboards that respond and interact with your every click. And if you’re thinking of truly standing out, automating your tasks with Macros and VBA is like having a personal assistant who’s an Excel wizard.

Machine Learning & AI Skills

When the conversation turns to machine learning and AI, it’s like stepping into the future. By understanding the nuts and bolts of algorithms—be it the structured predictions of supervised learning or the pattern discovery in unsupervised learning—you become the magician who reveals insights hidden in plain sight. It’s about getting comfortable with neural networks, where tools like TensorFlow allow you to play with deep learning, or diving into the human-like understanding of languages with NLP.

Staying updated is as essential as your morning coffee. Imagine sipping your latte while scrolling through Feedly, where you’ve got all the latest posts from top industry blogs at your fingertips. KDnuggets becomes your go-to for data mining, while FlowingData helps you stay ahead in data visualization. Data Science Central and Towards Data Science on Medium become your daily reads for broader insights, and Analytics Vidhya your companion for practical tips. And don’t forget to keep tabs on The Verge, Insider, and The Information—consider them your window to the tech world’s soul, where trends are born and the future is shaped.

Make the Most of Workplace Learning Resources

Let’s chat about those awesome learning platforms your company might offer. Got a subscription to DataCamp or Coursera through work? Fabulous! Dive into those courses like they’re the last piece of pie at Thanksgiving dinner—eagerly and without a second thought. LinkedIn Learning? That’s your professional development playground. Remember, the skills you can gain here aren’t just about technical know-how; they’re about staying ahead in the data game.

When You’re Going Solo – How to become a better data analyst?

Now, what if you’re part of a smaller outfit where such resources are as scarce as a quiet day on Wall Street? Fear not! The digital world is brimming with online courses and training programs that are just a few clicks away. Commit to a subscription that fits your budget and goals, and you’ll be adding new tools to your data toolkit in no time.

The beauty here is flexibility—you can choose what to learn based on where you see your career heading. Want to decipher the mysteries of machine learning? There’s a course for that. Eager to excel in Excel? You’re covered. The key is to keep building a portfolio of skills that are as diverse as the data sets you’ll encounter.

Improve Soft Skills: Not Just About Numbers

Let’s chat about what sets a good data analyst apart from a great one, and no, it’s not just about having a love affair with numbers. It’s about soft skills – and don’t you dare think of them as ‘soft.’ These are the hardcore traits that help you make a real impact!

The Power of Storytelling in Data

Picture this: you’ve crunched the numbers, and they’re singing to you a tale of trends and insights. But here’s the kicker – can you translate this data-driven aria for someone who thinks ‘Python’ is just a snake? Effective communication is your bridge. It’s not just about what you say; it’s about making it click for others. Imagine explaining the significance of a spike in user engagement after a product update to a team of marketers who care about catchy slogans more than standard deviations. That, my friend, is the art of communication in data analysis.

Now, let’s talk storytelling – not the ‘once upon a time’ kind but the narrative that turns data into decisions. It’s the difference between a snooze-inducing report and a gripping story that gets stakeholders on the edge of their seats, ready to act. When your data visualization not only looks snazzy but also tells a compelling story, that’s when you know you’ve hit the jackpot.

Solve Business Questions

Diving deep into data is like being a detective in a tech thriller. Let’s take a cue from the big leagues: Google, for instance, is known for harnessing data from user search trends to improve their algorithms. Over at Amazon, they’re scrutinizing customer purchase patterns to personalize recommendations. Meanwhile, Netflix analyzes viewing habits to suggest your next binge-watch. The common thread? They ask the right questions and are always on the prowl for new data sources. They step out of their comfort zone, and so should you.

Mentoring: The Mark of a Leader

And hey, if you’re climbing up the ladder, don’t forget to throw down a rope for the ones starting the climb. Coaching junior analysts doesn’t just help them; it cements your reputation as a knowledgeable and helpful colleague. Picture yourself leading a workshop on the nuances of data modeling, or even just being the go-to person when someone’s SQL query is turning into an SOS signal.

Increasing Career Visibility for Promotion

Lastly, let’s not forget about boosting your career visibility. There’s a LinkedIn course that’s just like a secret map to promotion treasure. It’s about learning to spotlight your achievements and letting the world know you’re not just another number-cruncher but a data whisperer ready for the next big challenge.

Real-World Practice: From Theory to Application

You’ve got the theory down pat, but how do you prove your mettle when you’re out there in the wild? Simple. You dive into personal projects and side gigs like there’s no tomorrow. Here’s the kicker: often, you can’t flaunt your full-time job’s data accomplishments like you’d show off a new smartphone—company policies and privacy concerns see to that. 

So, what do you do? You get creative. Start with personal projects. They’re your sandbox; play, experiment, and build something you can showcase with pride. It’s like creating your own secret recipe that you can actually share with the world.

Next up, imagine a playground where data enthusiasts flex their analytical muscles—that’s what hackathons and Kaggle competitions are all about. They’re not just for honing your skills but are prime spots for rubbing elbows with peers and experts alike. Winning or just participating ups your credibility and puts you on the map. It’s like LinkedIn for data geeks, but with more algorithms and less small talk.

Offering Pro-Bono Work: Your Data Skills as a Force for Good

And here’s an idea that’s both golden-hearted and brilliant for your resume: offer your data analysis services on a pro-bono basis to non-profits or small local businesses. These organizations are often data-rich but insight-poor, and your skills could make a genuine difference. Plus, helping others is not just good for the soul; it’s a shiny badge of real-world experience on your CV. Think of it as good karma and good business.

Where do you find these opportunities? Platforms like Taproot and Catchafire are like matchmakers for your skills—they connect you with non-profits in need of your analytical prowess. Here’s a mini walkthrough:

  1. Sign Up and Set Up: Create profiles on these platforms. Showcase your skills, your passion for data, and your eagerness to contribute.
  2. Search and Offer: Browse through the projects or the organizations looking for help. Filter them based on the cause you’re passionate about or the skills you want to polish.
  3. Connect and Contribute: Once you’ve found a match, reach out and offer your services. Be clear about what you can do, your availability, and your commitment level.
  4. Project Time: Dive into the data. Whether it’s analyzing donor databases, optimizing resource allocation, or creating impactful visualizations, give it your all.
  5. Document Your Journey: Keep track of the challenges you face and how you solve them. These stories are gold dust for interviews and your LinkedIn profile.
  6. Share the Success: Once you’ve wrapped up, request testimonials and endorsements. A few words from a grateful organization can speak volumes.
  7. Reflect on Your Impact: Post-project, take a step back. What did you learn? How did you make a difference? Use this to craft a narrative for your CV and cover letter.
  8. Rinse and Repeat: The more you do, the better you get. Keep the momentum going by taking on new projects.

Every pro-bono project you undertake polishes another facet of your skills as a data analyst. Think about it—this isn’t just about giving back (though that’s a huge part of it). It’s also about working with real, messy, non-textbook data. It’s about making decisions without all the answers. That’s the stuff of data analysis dreams!

So by the time you’re narrating this at your next job interview, you won’t just be another candidate—you’ll be the one who didn’t just aspire to be a better data analyst, but who put in the work where it counts. Plus, you’ll have a bunch of heartwarming stories to share. And let’s be honest, who doesn’t love a good story where data is the hero?

Networking: The Data Analyst’s Social Network

Being a hotshot data analyst isn’t just about crunching numbers; it’s also about who you crunch them with.

The Networking Foundation

Starting off in the data world can be as intimidating as a spreadsheet without headings. But, fear not! Networking is the secret ingredient to your career recipe. Think of it as the handshake between your skills and opportunities. Now, you might be asking, “Where do I even begin?” It’s simpler than you might think.

Find Your Tribe: Data Communities and Meetups

Your first mission is to find your tribe. This is where local meetups and online forums come into play. Websites like, eventbrite and LinkedIn are bustling with data analytics groups just waiting for someone like you. Join one, or hey, why not three? The key is to show up. Be that person who attends talks, engages in discussions, and yes, even offers to help organize events. This isn’t just about showing your face; it’s about showing your passion.

The Learning Exchange

Imagine you’re at a local meetup, and there’s a talk on the latest data visualization techniques. You, being savvy with Tableau, strike up a conversation with the speaker, who happens to need an analyst for an upcoming project. Boom! That’s your in. By sharing your knowledge, you’re not just learning—you’re demonstrating your value.

The Breakthrough: Alex’s Ascension

Let’s talk about Alex, an entry-level analyst. Alex started off eager yet unsure of how to make a mark. Attending a major data conference was a game-changer. There, Alex volunteered to assist with a workshop, showcasing not just attendance, but active involvement. This move caught the eye of a senior data scientist who was in the market for a mentee. Fast forward six months, and Alex isn’t just analyzing data, but also co-authoring a paper with the mentor. All it took was that one connection, that one event, to catapult Alex from the backroom to the boardroom.

Networking isn’t a one-off activity; it’s a career-long journey. Each interaction is a potential lead, a lesson, a partnership waiting to happen. So put yourself out there, mingle with the data gurus, learn from the analytics aficionados, and before you know it, you’ll not only better your skills but also amplify your career trajectory. And remember, every data giant out there started as a newbie once—so get networking, stay persistent, and watch as those career doors swing wide open.

Wrapping Up: The Never-Ending Data Journey

You’ve just had a taste of what it takes to sharpen those analysis skills, and I hope you’re hungry for more because the world of data analysis is as endless as the rows in a Google Sheet. Remember, improvement isn’t a sprint; it’s a marathon with data points as milestones.

Throughout my own journey—from those first tentative steps with online courses to where I stand today—I’ve learned that the quest for improvement is perpetual. It’s about embracing the data puzzles that stump you, the projects that push your boundaries, and the learning opportunities that pop up in the most unexpected places.

Let’s take a moment to reflect. Think about where you started and the strides you’ve taken. Whether it’s mastering a new data visualization tool or finally cracking that complex algorithm, every victory is a step forward in your career. Just like any good data set, your journey should have a trend, and ideally, it’s an upward one!

So, keep pushing those analytical boundaries, and remember: every dataset, no matter how complex, starts with a single data point. You, my friends, are that data point, so go forth and plot your path to data analysis greatness!

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