How I Would Become a Data Analyst in 2023: If I Were to Start All Over Again.
In today’s business environment, it’s clear that almost all activities involve some form of interaction with data. The amount of data we both produce and consume is staggering. In a world where informed decision-making based on real data is a valuable skill, I invite you to step into my shoes as I share my journey to becoming a data analyst.
After a couple of months of immersing myself in the realm of data, I saw a fair number of successes and setbacks. Along the way, I collected a lot of stuff — some really valuable, others just a mess. To make this process easier for you, I’ve distilled my experience into three key areas that are essential to the security of the data analyst role.
We start with the general advice I needed, or should I say hoped someone told me when I started on this data-driven journey. Next, I will delve into the essential skills you need to develop. Lastly, I will guide you through the art of applying and demonstrating these amazing skills, ultimately leading you to a successful career as a data analyst.
Tip #1: Craft a Well-Defined Learning Roadmap
Starting out as a data analyst might feel overwhelming. There’s a ton of information out there, many courses online or at schools, certificates you can earn, and different projects to try. My first tip is to plan your learning step by step. Imagine where you want to end up in the future and what kinds of tasks or jobs you like. Then, learn things that fit that picture.
If you’re learning to code, don’t just code randomly. Focus on coding things that matter for your goals. Think of coding like an ongoing adventure. If you try to learn everything, you might not get really good at anything. Creating a clear learning plan helps you move towards your goal of becoming a data analyst. It helps you use your learning time wisely and get better at what matters most.
Tip #2 Set Aside Time for Learning and Development
My second tip is a game-changer: reserve a chunk of your weekly schedule for learning and growth. The tech and data landscape shifts swiftly. Without ongoing learning, you’ll risk falling behind. When I started out, concepts like cloud computing were foreign to me. But now, major corporations are racing to adopt the cloud for its benefits — like flexible power and constant accessibility.
So, I decided to dive into cloud computing. I earned an AWS certification and completed an online degree in cloud data engineering. These moves kept me ahead of the curve and equipped me with valuable skills. By dedicating time to learning, you ensure your expertise remains relevant and you stay ahead in the ever-evolving field.
Tip #3 Enjoy the Learning Process
Here’s my final piece of advice, and it’s a gem: Enjoy the learning process. During my journey, I made the mistake of being too sure about the end result and putting unnecessary stress on myself. In the process, I lost sight of the wonderful things I was learning along the way. If your heart is in working with data, you’ll love the endless things you’ll discover. Just remember to pause and appreciate your progress.
Now, let’s delve into the skills required for a data analyst role. This section unfolds into four pivotal parts: technical skills, soft skills, analytical skills, and last but certainly not least, industry knowledge.
Technical Skills — Building a Solid Foundation
When it comes to technical skills, they’re tangible and measurable — probably the easiest to learn. My recommendation for a strong start? Begin with Excel and SQL. These are the powerhouses in the data world and mastering them sets the stage for a promising career. Think of them as the sturdy foundation of your data house — supporting the weight and giving you a solid footing.
Begin with Excel. It’s user-friendly, allowing you to click and point using the UI. Macros let you save time by recording actions and viewing them in VBA editor. Advanced Excel skills are a minimum requirement for a data analyst, and they can even land you an entry-level job.
Once you’re comfortable with Excel, venture into SQL. It’s like a gateway to coding. After conquering these two, it’s time to pick a programming language. Python is my top recommendation due to its popularity and learnability. However, focus on learning aspects relevant to data and analytics. No need for exhaustive programming courses; targeted ones for data and analytics will suffice.
Understand Python basics — arrays, lists, dictionaries, and object mutability. Embrace pandas and NumPy for data manipulation, and matplotlib or Seaborn for visualization. Once your programming skills are honed, explore data visualization using BI tools. Power BI and Tableau are top choices, being widely used. I personally use Power BI for impactful visualizations, conveying my message effectively.
Remember, it’s not just about gathering, cleaning, and manipulating data. Transforming it into clear, actionable insights using charts, tables, and dashboards is key. Think of it as storytelling with data — making intricate messages simple, understandable, and visually appealing. After all, who prefers poring over dry PDFs and PowerPoint slides when dynamic interactive dashboards can deliver engaging insights?
Soft Skills — Communication is King
Now, let’s dive into the softer side — soft skills. It all boils down to common sense: treat others the way you’d want to be treated. A fantastic book that really opened my eyes to understanding others is Dale Carnegie’s classic “How to Win Friends and Influence People.” Give it a shot — it’s a quick, engaging read that can be a game-changer.
Communication is king. Be clear and concise, whether you’re talking or writing. Nobody likes long emails — cut out the lengthy narratives. Use line spacing to your advantage. I often structure my emails into context and action paragraphs. It helps my stakeholders find information and know what to do or respond.
Tailoring your language is crucial. When presenting to a non-technical crowd, go for simplicity — make it easy for even your grandparents to grasp. Remember, presentations aren’t about flaunting your smarts; they’re about efficiently conveying information, so everyone understands.
For tech-savvy audiences, delve into details. Explain the code, formulas, functions, and the nitty-gritty. Flexibility is key — adapt your approach to your audience.
Analytical Skills — Navigating the Data Realm
Now, let’s delve into the realm of analytical skills. When I talk about analytical skills, I’m referring to problem-solving abilities, innovation, creativity, and, yes, the math side of things. However, the specific level of math you’ll need depends on your role, responsibilities, and tasks. Starting with fundamental descriptive statistics, like mean, median, or recognizing skewed data, is always a solid beginning. Personally, I’ve delved into probability, optimization, advanced econometrics, and statistics during my learning journey.
While these skills prove handy and impressive on paper, in day-to-day work, the impact may not be as significant. A good rule of thumb: the more data science-oriented your role becomes, the more math you’ll likely need. But here’s the great news: math is something you can pick up along the way, with resources like online tutorials and readily available Google searches.
Now, let’s touch on other crucial analytical skills. Problem-solving reigns supreme — your technical skills are practically worthless if you can’t crack the business conundrums at hand. Creativity and thinking outside the box might sound cliché, but it’s essential. The ability to generate innovative ideas, combined with the capacity to bring them to life, truly sets you apart. That’s why continuous learning and evolution are pivotal; they empower you to devise better, more cost-efficient, and time-saving solutions.
Industry Knowledge — Navigating the Context
Now, let’s dive into the crucial aspect of industry knowledge. While being an Excel wizard, mastering SQL and Python, and crafting stunning visualizations in Tableau or Power BI are impressive feats, without a grasp of industry dynamics, they lose their impact. Investing time and effort in understanding your industry’s landscape is essential.
Sure, you can read and comprehend data points and metrics in plain English, but the true power lies in your ability to ask the right questions and uncover profound insights. To do this, you need to comprehend the bigger picture. What’s the issue at hand? What are the gains from solving it? Can you navigate your company’s systems, products, and processes?
My experience in marketing brings a valuable perspective to the table when I work with marketing data. The terms and details, like how we divide markets, measure success, and understand customer experiences, come naturally to me. This familiarity lets me quickly notice any differences in the numbers or measurements, making sure they match what we anticipate. Remember, it’s not just about numbers — it’s about understanding the context, which transforms data into actionable insights.
Applying and Showcasing Your Skills
Lastly, let’s explore how to put your knowledge into action and demonstrate your capabilities. In my view, nothing beats actual work experience. Hands-on involvement in projects where you utilize your technical, soft, and analytical skills is the best way to stand out when pursuing your next job. Regardless of certifications, courses, or badges you’ve acquired, work experience holds the upper hand every time.
However, if you’re just starting and lack data analyst experience, don’t worry. There are plenty of alternative paths to exhibit your skills. For instance, you can tackle coding challenges online or embark on personal projects, sharing them on platforms like GitHub. You can access data from sources like Kaggle, Google Dataset Search, or government databases to craft solutions for intriguing problems. Opt for captivating datasets related to topics like sports, movies, or Google Play Store — they’re more engaging than mundane employment or tax statistics. Aim for around 2 to 3 projects to strike a balance; fewer might seem not enough, while more can be excessive.
And there you have it; we’ve reached the conclusion of this article. Beginning your journey in the data world may seem daunting, but with a strong determination, the challenges ahead can be exciting. I, too, learned to use Excel, code, and visualize data through self-learning — things I wasn’t even sure I could achieve. If I could do it, I’m confident you can too.