Temitope

@officialladi_T SQL

Which tool do you love working with: Excel SQL Power bi Python

Dear @as02774 and every other beginner that is learning. I will start by saying “completing one course perfectly before starting another.” This approach actually increases forgetting. In data analytics, the better strategy is layered learning + continuous reuse, not one-course-perfection because every tool is interconnected to each other. Here’s how to move from one course to another without losing the previous one: 1. Learn in Layers, Not in Isolation Think of your skills like a stack: Excel = foundation for analysis & reporting SQL = data extraction & structuring Power BI = visualization & storytelling You don’t leave Excel behind when learning SQL. You carry it forward. So instead of: Finish Excel → forget → start SQL → forget → start Power BI Do this: Learn Excel → use it while learning SQL → use both while learning Power BI 2. Always Combine Old + New Skills Every time you learn something new, force a connection: Excel + SQL → import SQL results into Excel PivotTables SQL + Power BI → write SQL queries as data source Excel + Power BI → compare dashboard outputs with Excel analysis This “mixing” is what locks knowledge in memory. 3. Build One Evolving Project Instead of separate projects per tool: Start ONE project and upgrade it: Example: Sales Analysis Project Stage 1 (Excel): clean and analyze sales data Stage 2 (SQL): store and query the data Stage 3 (Power BI): build dashboard on top of it Result:Same dataset. Same story. Different tools. This prevents forgetting because you’re not starting over, you’re building on memory. 4. Use “Active Recall” Weekly Once a week, without tutorials: redo a SQL query from memory rebuild an Excel PivotTable recreate a Power BI visual Even if you forget and check notes, that struggle is what strengthens memory. 5. Keep a Mini Revision Routine You don’t need long study sessions. Try this simple rotation: Mon: Excel (30 mins) Wed: SQL (30 mins) Fri: Power BI (30 mins) Result: This keeps all tools “alive” in your brain. 6. Don’t Wait for Mastery Before Moving On This is a common beginner mistake. You don’t need to master Excel before SQL. You don’t need to master SQL before Power BI. In real jobs: you learn while working you revisit tools when needed 7. Focus on Concepts, Not Tools Tools will fade if you focus only on buttons. But concepts stay: filtering data grouping data relationships aggregation visualization logic Once you understand these, you can switch tools anytime. Even experienced analysts still Google SQL syntax, DAX formulas, or Excel functions. What matters is not memorizing everything, but building the ability to: understand concepts, know where to find answers, and apply them repeatedly. Simple Rule to Remember: “don’t finish tools. Reuse them.” Yours in data, Ekanem Data Analyst | Power BI Developer

Dear Vivian, Based on your first Powerbi dashbaord, here is my comprehensive review, rating, and actionable feedback for corrections. Firstly, always use this Windows shortcut to get a clean screenshot to publish your work online. 1. Window+G 2. Fn+ Prt Sc ​Overall, the dashboard is ​ok and will rate it a 6.5 / 10. Reasons: It established a clear layout with structured KPI cards and separate visual sections. However, there are several design, formatting, and data-modeling choices that can be refined to make it better. Detailed Feedback & Corrections ​1. KPI Cards & Formatting (Top Banner) ​Fix the Title: The title reads "SUPER PHONE SALES DATASET". Datasets are what analysts connect to; dashboards are what end-users read. Change this to something more business-oriented, such as "Super Phone Sales Performance Dashboard". Clean up "Count of Order ID" Showing a raw count as 9889 looks unformatted. ​Rename the field in the visual well to "Total Orders" and add a comma separator so it displays as 9,889. Card Border Consistency: The third card ("Total Discount") is missing its right-side border or has a lighter border than the others. Ensure the border properties match across all 5 cards. ​2. Title Text & Default Aggregations ​Remove "Sum of...": Power BI automatically prefixes fields with "Sum of" or "Count of". It is a major tell of an unpolished dashboard. Fix: Rename the titles manually in the formatting pane. -​Change "Sum of Sales by Category and Region" to "Sales by Category & Region". Change "Sum of Sales by Year" to "Sales Trend by Year". Change "Sum of Sales by Top 10 State" to "Top 10 States by Sales". ​3. Chart Corrections & Visual Best Practices "Sum of Sales by Category and Region" (Stacked Column Chart): ​Currently, you have data labels turned on inside very small stacked segments. This makes numbers overlap, overflow, and become entirely unreadable. ​Fix: Turn off data labels inside the bars, or switch this to a Clustered Column Chart if comparing the exact regional numbers side-by-side matters. Alternatively, keep the stack but rely on tooltips for the exact breakdown. ​"Top 10 Best Selling Product City" (Stacked Column Chart): ​This chart is heavily cluttered. There are far too many small stacked colour segments representing individual products/categories inside each city, making the legend massive, and the chart impossible to interpret at a glance. ​Fix: If the goal is to show the top cities, use a simple, clean Horizontal Bar Chart showing sales by City without the complex colour stacking. Let a tooltip or a drill-through handle the product breakdown. ​X-Axis Labels: The city names at the bottom are truncated and cut off. A horizontal bar chart will give the city names plenty of room to read left-to-right. "Sum of Sales by Top 10 State" (Bar Chart): ​Similar to the city chart, the X-axis labels (Washingt..., Pennsylva...) are cut off. Fix: Turning this into a Horizontal Bar Chart will instantly fix the readability of the state names. ​4. Slicer Section (Left Panel) ​Whitespace & Alignment: The slicers on the left (Region, City, State, Order Date) have a lot of unused vertical space between them, while the charts on the right are a bit cramped. ​Hierarchy: Consider leveraging Power BI’s hierarchical features or dropdowns to save space. Making City and State dropdowns instead of open lists will make the left panel look much cleaner. Weldone on this, it is a god job. Kind regards, Ekanem. Data Analyst & Powerbi dev.

@ekanemjr_99 I learnt from this correction Thanks for sharing

Cleaning a data with a date column that has different date format is so annoying😭 I spent hours trying to figure out what’s wrong while working on a dataset few days ago. I kept thinking about what was wrong. I almost ended up crying that day but I didn’t cry sha 😂

@keji_social I’m praying for a good job that will give me peace of mind

Asalamualaeykum warahmotullah wabarakatuh May Allah SWT ease our worries, bless our hustle, protect our families and grant us good news that we’re praying for. 🙏 As you see this tweet, drop one word you’re praying for today🤲

Children of Oblivion. 💔 The scariest part is not that these things happen. It’s how normal they’ve started to feel.

After making Power BI my bestie, I went back to my first love in data analytics today and cleaned a dataset 🤭 What did you do today?

@BusayoSegun Even me I tire for myself😂

@Temitop604 Una no dey rest? 😂

Eid day 2 in 2 pictures ✨

I regret to inform you that: - 2 hours a day - 4 times a week - 3 months Is enough to master data analysis and get a job. Pick a tool at a time Excel. Power BI. SQL 1 month for each tool With the right course I promise you 2026 will be different for you❤️

To all our Muslim brothers and sisters ❤️🎉

@sid_ipynb Well said It’s really the most important work. A piece of uncleanliness in a dataset make an insight wrong

@Temitop604 3 hours that felt like 3 years. Turns out 40% of the dataset was duplicates and nobody told me. Data cleaning isn’t unglamorous work it’s the most important work. It just doesn’t get the credit. 😐

Dear Data Analysts What is the longest hours you cleaned dirty data?

Eid Mubarak ✨

@AbbottBirungi I will

@Temitop604 Is it possible you share one and I also work upon them .

3 hours

@AbbottBirungi Yes but some datasets are so messy like the one I’m working on now

@Temitop604 Power query eases things

@GiftedAjamu Hours oo

@Temitop604 Hmm, Hours

@codewithmethod Data cleaning really takes time

@Temitop604 Remember cleaning data for hours for my final year project training data.