Syed Abid Hassan’s Post

A lot of aspiring data analysts today understand the technical aspects of data cleaning. But what sets you apart is knowing how data cleaning fits into the real day-to-day workflow. It’s not just about how you clean data; it’s about why? To excel, you need to go beyond just learning technical steps. You should have a high-level framework that enables you to onboard any dataset from various industries. This way, you're prepared to clean data more effectively, no matter the project. In my little years of experience, I’ve found that the key to success as a data analyst is mastering the why behind the process. It’s not just about fixing issues—it’s about understanding the context in which they arise and deciding what matters most to the analysis. Here's a high-level thought process: Conceptualize the dataset—understand the grain, measures, and dimensions. Locate solvable issues like formatting and consistency problems. Evaluate unsolvable challenges and know how to move forward when perfection isn’t possible. Data cleaning is iterative, but don’t get bogged down. Prioritize the critical columns and make the most important ones usable, so you can deliver insights faster and keep improving. #DataCleaning #DataAnalysis #AnalyticsTips #CareerGrowth #BuildAStrongFoundation

As data analysts, our primary job is to extract valuable insights from datasets, not necessarily to create them. But learning to recognize bad table practices is crucial. Take this table: 1 | 1,2,3,4,5,6 Why is it bad? 🤔 It breaks fundamental principles like atomicity, which says each value should be stored separately. This makes data harder to query, visualize, and analyze accurately. #DataAnalytics #BestPractices #DataCleaning #Learning However, understanding such mistakes is key to becoming a better analyst—because cleaning up data is often the first step to meaningful insights. 🚀

🤓 Being a data analyst means living in a world of terms and acronyms that sometimes feel like a foreign language. Here’s a down-to-earth glossary to make sense of the essentials without all the jargon headaches. Perfect for anyone who’s ready to make data analytics feel a bit more human. So sit back, read on, and let’s demystify the world of data! #datingthescience #dataanalysis #data

Emphasizing Skill Development Wondering what skills you'll need more as a data analyst expert in the years to come? Take a hint from the experts around you! 💡 What are they learning? 📚 What challenges are they tackling? ✅ Their experience can be your roadmap for future-proofing your skillset. 🎯 #DataSkills #LifelongLearning #DataDrivenFuture

Dear data analyst, are you part of the 1%? 💡 70% of people dream about becoming skilled in data analysis. But only 1% push through and master the craft. Why is that? When I first started with data, it felt overwhelming—so much to learn, and no clear starting point. But here's the thing: the more I dove in, the more everything around me started making sense. Data isn't just numbers on a screen. It’s the story behind customer behaviors, business decisions, and even everyday life. As I sharpened my skills, my perspective shifted. Suddenly, patterns emerged where I once saw chaos. Nuances that seemed invisible became crystal clear. That’s the real magic of an analytical mind—it transforms the way you see the world. 🌍 So, let me ask you this: ➡️ Are you dreaming about data analysis or taking steps to master it? Let’s discuss below—what’s your next step toward joining the 1%? #dataanalytics

8 terms in the data analyst's dictionary: 1. "It depends" = "This is way more complex than you think" 2. "Interesting" = "I have no idea what this means yet" 3. "Statistically significant" = "Probably not a fluke, but no guarantees" 4. "Data-driven decision" = "A guess, but with numbers" 5. "Quick analysis" = "This will take way longer than you expect" 6. "Data quality issues" = "Someone messed up the data entry" 7. "Outlier" = "This point is ruining my beautiful model" 8. "Further investigation required" = "I'm confused and need more time" What's on your vocabulary? #datahumor #withadoseofreality

Sometimes stakeholders won’t do what you recommend as a data analyst. You might have an impressive story and solid insights but they decide to take action in another way. That’s ok. There are so many things that go into why a stakeholder makes a choice and data is just a part of that. If you feel like your voice is not being heard, ask your stakeholders: Why did you make that decision over the other choices? What about my recommendation did not fit your strategy? Did mistrust in the data influence your decision? #dataanalytics #hueristics 

It's been a while here 😊 A quick one: 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘄𝗵𝗮𝘁 𝗼𝘂𝗿 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗼𝗿 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲 𝗳𝗿𝗼𝗺 𝘂𝘀? Simply by asking the right questions. This is an essential step in the analytical process. As Data Analysts, we must understand the needs and expectations of our clients or stakeholders. This understanding is crucial for delivering valuable insights, building trust, and driving success in our data analysis projects. "Never assume as a Data Analyst; ask questions, get clarity before you begin to analyze." - Maduka Esther. Thanks coach for this very important reminder. #DataAnalysts #DataAnalysis #ClientNeeds #DataDriven #InsightfulAnalysis #AskQuestions

There’s this misconception among many data newbies that just learning the tools is enough. Absolutely not. Understanding the concepts and processes, and your ability to solve problems is the KEY. Can you creatively interpret data and use it to solve problems? Or do you just throw charts around into a dashboard? As a data analyst, you are first a PROBLEM SOLVER before anything else. That numbers you are getting in your charts can you interpret it and explain your results to both technical and non-technical stakeholders? The bottom line here is that knowing only the technical aspects of data analytics is good but not enough, the true success comes from understanding the underlying processes and concepts and being able to solve problems with those data. Have a beautiful new week! #dataanalytics #dataanalyst #dashboard #concepts

If you want to stand out as a data analyst, master this one essential skill: -Data cleaning. Real world data is messy. Really messy. The clean, perfect datasets you see in tutorials or projects? That’s not reality. When working on your next project, don’t take shortcuts. As my colleague often says: Choose data that challenges you, data that needs wrangling, cleaning, and transforming. Why? Because the ability to turn chaos into clarity is what makes you truly invaluable. Avoid shortcuts and embrace the hard way It will make you more valuable. #DataAnalytics #RealWorldSkills #data