One of the most common mistakes I see in R: People manually removing outliers. You can automate it with: data <- data %>% filter(abs(scale(variable)) < 3) Saves a lot of time when cleaning datasets.

Your dashboard is beautiful. But is it actually useful? A lot of beginners (and even some experienced analysts) build dashboards full of charts and forget to ask: what decision does this help someone make? Every visual on your dashboard should answer one clear business question. "Total Sales", okay, but so what? "Are we on track to hit our Q2 target?" now that drives action. Before you drag a single chart onto your canvas, write down the 3 questions your dashboard must answer. Let those questions guide your design. Less visuals. More clarity. More impact.

𝗚𝗼𝗼𝗱 𝗺𝗼𝗿𝗻𝗶𝗻𝗴 𝗴𝘂𝘆𝘀. If you’re starting out as a data analyst, before you hop on any tools, you need to understand the data analysis lifecycle. This is the framework every analyst uses, whether they realize it or not. Let me break it down. 🧵 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 (𝗔𝘀𝗸) This is where most beginners mess up. They jump straight into tools without understanding what problem they’re solving. Ask: → What question are we trying to answer? → Who needs this information? → What decision will this analysis drive? If you don’t understand the problem, your analysis is useless. No tools needed here! 𝟮. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 Now you know what you need. Go get it. Where is the data? → Database? Export it. → Excel file? Import it. → API? Pull it. → Manual entry? Document it. Generally given to you, but still no tools needed. 𝟯. 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Real data is messy. Always. Missing values. Duplicates. Wrong formats. Inconsistent entries. Clean it: → Handle nulls → Remove duplicates → Fix data types → Standardize formats This step takes 80% of your time normally, BUT, we have a tool for you. RChat will do all of this in one prompt, in the exact manner you want it done. FOR FREE. 𝟰. 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 Now you start asking questions. What patterns do you see? → Trends over time? → Outliers? → Correlations? → Unexpected values? This is where curiosity matters more than technical skills. Explore. Dig. Ask “why?” 𝟱. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 / 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 Apply your methods. Descriptive analysis - what happened? Diagnostic analysis - why did it happen? Predictive analysis - what will happen? Prescriptive analysis - what should we do? Again use RChat to model graphs and give you answers. 𝟲. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 You found insights. Now make them understandable. Nobody cares about your SQL query or your pivot table. They care about: → What does this mean for the business? → What should we do about it? → What’s the impact? Visualize it with free tools such as Excel or RChat. 𝟳. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Your analysis drives a decision. The decision leads to action. Did it work? → Monitor the results → Track the metrics → Measure the impact If it didn’t work, cycle back. Refine. Try again. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗮𝗿𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂: This isn’t a straight line. You don’t go 1 → 2 → 3 → 4 → 5 → 6 → 7 and you’re done. You cycle back and forth. During exploration, you find data issues → back to cleaning. During analysis, you realize you need more data → back to collection. During presentation, stakeholders ask new questions → back to analysis. That’s normal. That’s how it works. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Beginners think data analysis is about tools. “Should I learn Excel or Python first?” “Which BI tool is best?” Wrong. Use RCHAT! Trust us, it’ll save you so much time. Understand the lifecycle. Follow the process. Get results. That’s data analysis.

Day 2 of improving RChat Today we improved: • Fixing instant manipulation of data files. • Improved UI. • Free tool - CSV to EXCEL converter (also Excel to CSV) / Check the uploaded screenshots Our goal : Turn a 2-hour Data/R assignment into a 5-minute task. Still a way to go. But progress is happening.

Data scientists, I wanted to share the new dashboard page of the tool I am using for all my analysis. We have been building this project for over a year now and we are now creating free tools to accompany it. • File converters • Graph Generators • Error Fixers • Ai assistant That's the update for Day 3, but I wanted to talk more about the current data job market. READ THIS - Our team has a good 50 years of experience in data analytics and we have been discussing creating a program to help aspiring analysts, but we keep bumping into a problem. - Analysts fresh out of Uni or College don't understand the data. They know how to clean it, manipulate it, store it and visualise it. But if you ask them to use it to produce solid founded business statement on what needs to change, they can't. - An analyst is NOT a data manipulator, YOU ARE THE BRAIN. You need to read the data and say ¨This ad is consuming 20% of our marketing budget and producing 5% less conversion that ad2 which has 10% budget.¨ - ¨WE need to lower the budget for ad1 and transform its target audience. ad2 is our priority and should get 50% budget!!!.¨ - A company that implements those changes and sees a rise in conversion and profit will promote you within a year. Anyway, if you need a tool for this, Use RChat. If you need advise on other tools, dm us. If you need career advice, comment. Stop wasting your time learning all of SQL.