Donabel Santos’ Post

Not feeling quite ready to apply for Data Analyst roles? That’s totally okay! But you should definitely start building a portfolio. Here’s why: First, each project you create helps you get a sense of what the job really involves. It’s a great way to dive into the day-to-day tasks, and it’s never too early to start gaining that experience. Next, as you work on these projects, you’ll notice areas where you need to learn more. This is actually a really effective way to focus your learning and make sure you’re progressing toward your goals. Finally, let’s be real—you’ll probably never feel 100% ready. And that’s fine! You don’t need to meet every single requirement in a job description. If you check off about 70% of them, you’re in a good spot. #interviewpreparation #dataanalyst

Data Analyst vs. Developer: Which Tech Career is Right for You? Please Like, Share , Comment and Subscribe for more videos

Not everyone should become a data analyst. The data career path has exploded in recent years and many people want in. But who SHOULD become a data analyst? If you're someone who is: 1. Curious 2. Inquisitive 3. Love solving problems Then data might be a good career for you. But you should also have: 1. Strong communication skills. 2. Able to manage projects well. 3. Willingness to collaborate with others. Data is a technical field, but also a people-oriented one. And this isn't an all-inclusive list, but these are definitely some big points to consider. ___ ✌️ Anything I missed? What would you add?

Temi landed a remote Data Analyst role! Here's how he did it in less than 3 months: Like a lot of aspiring data analysts, Temi had applied to hundreds of jobs without success. Here's how we changed that: - Brainstorm which jobs he ACTUALLY wanted instead of spraying applications like Steven Gerrard attempting a Hollywood pass - Prioritise fewer, higher quality applications - Turned his projects into Tailored Projects Once you take the right step to market yourself properly, finding a data analyst job isn't that hard. This has me thinking, which part of the application process do you struggle with the most? Let me know in the comments below ⬇

If you're looking for your first Data Analyst job and are concerned that your portfolio projects aren't "real"... Don't stress. For your first data role, good hiring managers will understand. They'd like to see: - Understanding of how data can assist organisations (creating questions/hypotheses). - Logical approaches to problem solving (rather than simply attacking the problem as a whole). - The best way to try to answer these questions (choosing the best tool/technique for the job). - The ability to effectively summarise your work and make actionable and specific recommendations. - Openness about what went well and what could be improved/investigated further next time. After all, "real" data work isn't that different.

I've spoken with thousands of career changers the past 3 years. And they all have 2 things in common: - They want to become a data analyst - But, they don't know how It's quite easy and you can do it in 4 steps: 1. Build top 10% skills 2. Build your brand 3. Craft your story 4. Land your offer I discuss all 4 in detail in my new 4-Step Guide to a $100K+ Data Analyst Career. Grab it here: https://bit.ly/40ej7fI #dataanalyst #dataanalytics #career

🤔 Unpopular Opinion... Maybe? I was recently asked how to transition from one job into data with no data experience and I gave my truthful opinion. Its the anti- "Make 100k" as a data analyst post. My opinion was this... Get your foot in the door with a company that has growth opportunities. Be willing to take a lower salary in order to gain experience. Even if you have bootcamps, certificates, and projects, the company is taking a risk on you. If the only positions that you are applying for are high paying analyst roles, you are competing directly with analysts with years of experience. Even if you rockstar analyst, it would be hard for your resume to stand out in the pile of applicants. Taking a lower paid analyst position will allow you to put that analyst role on your resume, give you experience, and potentially allow you opportunities within the company. Some companies have internal only positions which you would never see unless you work there... Id love to hear your thoughts?!?

Imagine you can fast forward to landing a data analyst job. (Sadly you can’t, but imagine.) How do you make sure to kill it and be not a good, but a GREAT data analyst? • Get used to taking a lot of notes! (Important during onboarding and for meetings) • Get comfortable working with and asking questions to stakeholders, not just your boss! • Learn everyday! (Spending even 20 minutes a day working through a course will accelerate your growth as an analyst and help you find new ways to solve problems) • Spend a lot of time early on getting incredibly familiar with the database(s) and data you work with! (This includes table structure, industry knowledge, and more) • Address weaknesses early! (You aren’t going to get everything right off the bat, and that’s okay! Address any weak points and ask questions early to be the most confident data analyst you can be! • Keep track of achievements! (This is important to kick the imposter syndrome, and will help you for those end of year meetings) • Be kind to yourself! (If you do all of the above, you should be well on your way to being an A+ analyst)

Business Analyst or Data Analyst? Let’s Simplify the Choice! After interacting with many people, one question stands out as the most frequently asked: What’s the difference between a Business Analyst (BA) and a Data Analyst (DA)? To help those still seeking clarity, I’ve broken it down in this post. Whether you're exploring career options or just curious, I hope this makes the distinction crystal clear. Let me know if it helps or if you have more questions! #BusinessAnalyst #DataAnalyst #TechCareers #LinkedInLearning #Analytics

When hiring managers are looking for a data analyst, they typically focus on a range of attributes that indicate both technical proficiency and the ability to apply analytical skills in a business context effectively. Here are the top 5 attributes that are often considered important: