Data Analyst Workflow Guide: Excel Python SQL Transitions

📊 Day 3 of #100DaysOfBusinessAnalytics One thing I’ve learned while working with datasets is that clean data is more important than complex analysis. Before analyzing any dataset, data cleaning is a crucial step. Some common issues I’ve come across: • Missing values • Duplicate records • Inconsistent formats • Incorrect or irrelevant data If these issues are not handled properly, they can lead to wrong insights and poor business decisions. That’s why tools like Python (Pandas), Excel, and Power BI play an important role in cleaning and preparing data before analysis. 👉 Good analysis starts with clean data. #100DaysOfBusinessAnalytics #BusinessAnalytics #DataAnalytics #Python #Excel #PowerBI

Data analysis is a popular and growing field in the tech world. And this 19-hour course takes you on an in-depth journey, whether you're a beginner or more advanced in your skills. You'll learn about Python, Excel, SQL, Tableau and Power BI & much more. https://lnkd.in/gWNSChwT

The "Aha Moment" Story "I wish someone had shown me this earlier." A pivot table = GROUP BY VLOOKUP = LEFT JOIN Filtering = WHERE clause Once you see the pattern, you stop being a "tool person" and start being a data person. Here's a side-by-side cheat sheet for Pandas, SQL, and Spreadsheets. Tag someone who needs to see this. #DataSkills #DataAnalytics #SQL #Python #CareerGrowth

🚀 Excel → Python → SQL: The Ultimate Data Workflow Cheat Sheet 📊 Still switching between tools and getting confused? 🤯 Here’s a simple side-by-side breakdown of how the same data tasks are done in Excel, Python (Pandas), and SQL 👇 📊 One data task → 3 tools: ➡️ Excel ➡️ Python (Pandas) ➡️ SQL 💡 Learn the logic, not just syntax — that’s what actually matters in real jobs & interviews. 🔍 Covers essentials: ✔ Filtering & sorting ✔ Group By, SUM, AVG ✔ Joins & merging ✔ Handling missing values ✔ Removing duplicates ✔ Creating new columns ⚡ Stop learning tools separately. Start connecting them. That’s how real analysts think. 📌 Save this for future reference ➕ Follow Lulu Bind Abbas for daily data tips, cheat sheets & interview prep #data #analytics #excel #sql #python #datascience

3 Pandas functions I use every single day as a data analyst: 1. .value_counts() — instant frequency distribution 2. .groupby() — split data into meaningful segments 3. .isnull().sum() — catch missing data before it catches you These 3 alone can answer 70% of basic business questions. You don't need to memorize every function. You need to understand data deeply. Save this. Use it tomorrow. #Python #Pandas #DataAnalytics #DataAnalyst #TechTips

Day 28 – Revision Day 📊💻 Today was all about revisiting the core foundations of my Data Analytics journey — Excel, SQL, and Python. 🔹 Revised Excel concepts like formulas, data cleaning, and basic analysis 🔹 Practiced SQL queries including joins, filtering, and aggregations 🔹 Strengthened Python basics and problem-solving approach Revision days might feel slow, but they are where real understanding happens. Going back to basics helps me identify gaps and build stronger confidence. Consistency > Perfection. Small steps every day are adding up. #Day28 #DataAnalytics #Excel #SQL #Python #LearningJourney

Most people ask: SQL or Python or Excel? But the truth is — it’s not a competition. Each tool solves a different problem: • SQL → Extract & analyze structured data • Python → Automate, transform & build logic • Excel → Quick analysis & business reporting If you're entering Data/Analytics, don’t pick just one — learn when to use each tool. That’s what companies actually expect. 👉 SQL for data 👉 Python for processing 👉 Excel for insights What do you use the most in your work? #DataEngineering #SQL #Python #Excel #Analytics

Never underestimate Excel. 📋 Before Python. Before SQL. Before any fancy tool, Excel was the original data science platform. And in 2026, it is still one of the most widely used tools in every industry worldwide. Here is why Excel still matters: 1️⃣ Pivot tables, VLOOKUP and Power Query are incredibly powerful 2️⃣ It is the first tool most business professionals use for data 3️⃣ Understanding Excel makes you a better data communicator Mastering the basics is never boring — it is the foundation of everything. #Excel #DataScience #Analytics #MicrosoftExcel #DataAnalysis #Tech #DataDriven

Deduplication is not just about removing duplicates. It is about defining: - what counts as a duplicate - which row should survive That decision changes everything. The same SQL function can be applied in different ways: - latest record - highest value - clean event signals Same function. Different logic. Different outcomes. Which one do you use most in your work? Advanced analytical techniques across Python, SQL, R and Excel 👉 The Data Analyst Playbook 👉 Follow for more #SQL #DataAnalytics #DataEngineering #Analytics #DataScience

That “simple spreadsheet” is lying to you—then the boss asks for a chart. Here’s the mini-quiz our team uses when someone’s stuck: Python (cleaning + analysis) Power BI (dashboarding) SQL (querying) Which one should you learn first to go from messy data to a decision-ready chart? Start with SQL. It’s where the data stops being a guess and becomes something you can pull, filter, and trust—then you build visuals in Power BI. “You don’t need to be a ‘math person’—you need a workflow,” so you can go from raw rows to a real story. visit our website: https://lnkd.in/gzx7zatA Which tool would help you most right now? #DataAnalytics #PowerBI #SQL #Python