Invisible Ink

There’s a difference between learning data analytics and actually doing data analytics. When you’re learning, everything feels structured. You’re been given a clean dataset, a clear question, and sometimes, an expected answer. But when you start building projects, you realize that real data doesn’t care about your plans. Dates are inconsistent. Values are missing. Duplicates show up. And sometimes, you don't even know what question you should be asking. That’s where I’m realizing the real learning happens. Not just in knowing how to use a tool, but in learning how to investigate a problem, make decisions, and figure things out when there’s no step-by-step tutorial. Building projects has been teaching me things that simply learning the tools only couldn't.

Good morning #datafam So I woke up on the right side of my bed this morning and I thought to give you this quick advice. If you're a planning to learn data analytics or you're already learning with the mindset that you're going to start earning large sum of money Immediately you start learning or when you're done learning, kindly disembark from the journey of becoming a data analyst. Cause your healing go long. Don't say i didn't do anything for you😁

Hi, I'm Mercy, a Data Analyst passionate about helping businesses make better decisions through data. I enjoy transforming raw, messy data into meaningful insights by uncovering trends, identifying opportunities, and answering the business questions behind the numbers. My toolkit includes: Excel SQL Power BI Right now, I'm building end-to-end case studies using real-world datasets, analyzing business problems from data cleaning and exploration to visualization and actionable recommendations. I share my projects, insights, and lessons from solving business problems with data, while connecting with other professionals in the data and business community. If you're a recruiter, hiring manager, business owner, or fellow data professional, I'd love to connect. I'm always open to meaningful conversations, collaborations, and opportunities.

After what felt like forever, I'm finally done cleaning three datasets for the retail case study I'm working on. 💃🤭 Now it's time for my favorite part... the analysis. This stage reminded me that data cleaning is rarely the glamorous part of analytics, but it's easily one of the most important part of analytics. One of the biggest challenges wasn't even the data itself, it was staying consistent. Frequent electricity interruptions slowed my progress more than I expected, reminding me that sometimes the biggest obstacles to a project aren't technical at all. Of course, the datasets came with their fair share of data quality issues: Missing values Inconsistent date formats Duplicate records Standardizing the different date formats was probably the most challenging part of the cleaning process. After a lot of trial and error, some research, and a little help from Claude, I finally got everything working as expected. Claude, I still owe you a bottle of Coke😄 A few practical lessons from this stage: 👇

We go again this week with the backing of the Lord🙏 Good morning all...

You know when they say your main learning starts when you start building projects or when you start working, trust me they're not making it loud enough. This past few days, I have learned, unlearn and relearn things that I didn't know during my learning phase. Anyway, Good morning and have a lovely day.

Analysis wey go go well na from descriptive stage and data cleaning stage you go know. Guys... I can't wait to share my update on this case study I'm working on. My eyes have seen alot😤 Anyway, let me get back to work😁😁

At this point, i think sleep is for those that don't have deadline to meet up with

Good morning #datafam Welcome to another week

Good morning #datafam Today I will be starting a new case study. a fictional mid-sized retail company is experiencing shrink in margin despite growing revenue. I will start the deep dive now. The cause could be pricing, product mix, discounting, fulfillment costs, or something nobody's noticing yet. Over the next few days, I'll: • Clean the dataset • Explore the data with SQL • Build a Power BI dashboard • Recommend solutions based on my findings And I'll share what I find. Let's see what the data says, shall we...😁

"Work smarter not harder" is an advice from people who never had to work hard to get where they are. For people like you and me, we need both to thrive. Smart strategy (right skills, right niche) AND Hard work (daily practice, actual projects). Skip anyone and you're done for

When people ask this question, I tend to understand where they are driving at. They already believe that Data analytics job market is saturated. And that Ai has taken over data analyst job And it's true. It has been saturated by data analyst who just focused on the tools alone, and that, is the area where Ai is going to take over. Because Ai is already very good at: Writing SQL queries that takes data analyst hours to write. Cleaning datasets. Explaining formulas and codes. Creating charts and dashboards. Summarizing trends in data. Generating reports and presentations. But the job market is not hiring data analyst to write queries or build dashboards only, cause dashboard doesn't even solve a problem 🤷. It's only a tool for understanding the problem and highlight where to investigate. Data analyst are needed to answer business questions. And answering these questions requires understanding the the business context, asking the right clarifying/follow-up questions, interpreting results, and communicating recommendations. AI can help with each step, but it doesn't automatically know the organization's goals, constraints, or priorities. As a data analyst, If you build your skills around solving business problems and not just the tools, you'll stand out much more in this data field. And won't have to worry about Ai taking over your job or when someone throws the samme question at you, or make the same statement about data analytics.

Good morning #datafams Should African data professionals target local companies, global remote roles, or build their own products? Where's the real opportunity?

Someone just asked me "Data analytics in 2026?" 😂 Abeg leave me oo 😆 In this new era, we don't do explanation. we show workings...😅

Understanding the tool is absolutely not enough, you also need soft skills like analytical thinking, problem solving skills, good communication skills to communicate insights and storytelling with data.

Good morning #datafam... 🫣 It's been a minute...🤗🤗 So I went ghost on X for a month and honestly, I didn't quit learning data analytics. I just got overwhelmed trying to balance learning, posting consistently, and school after resumption. For a while, I felt like I had to be progressing publicly every day, and that pressure started affecting the actual learning and my mental health. So I stepped back Just to breathe. Honestly, I didn't plan to take this much time to breathe, but... it is what it is. Now I’m back with a different mindset: Less pressure. More practice. More projects. More consistency without burning out. I may not have all the answers yet, but I’m still committed to becoming a great data analyst. Also I want to specially thank @Kmec37 and @Kayvhee3 for checking up on me while I was away, I sincerely appreciate it. Now, let's continue the journey...💃 If you're also trying to restart something you abandoned, this is your sign to begin again...

Good morning #datafam Let's do a little re-introduction since we've got some new faces here 😊🤗 Hi 👋👋 I'm Mercy, A Data Analyst-in-training, currently building my skills in solving real world problems with data and the tools i already have. Over the next few months, I will be focusing on: • Building real-world projects • Developing strong analytical thinking • Improving how I communicate insights • Documenting my journey consistently • Building a portfolio that helps me land my first data role I'll be sharing what I learn, what I build, the mistakes I make, and the lessons I pick up along the way. If you're in data, tech, or learning something new, let's connect and grow together.

Please oo, am i the only one having issues logging in to my X account on my phone?

You won’t always be motivated to do things that you're supposed to do which is why you need to build habits. Good morning #datafam

The way you write your SQL query is totally different from the way SQL runs it. Knowing and understanding this difference will help you to write accurate queries, avoid errors and debug errors easily.