How to Learn Data Analysis as a Business Expert

You want to have more impact at work, so you’ve decided to learn how to analyze data. Congrats! Data skills are a powerful way to differentiate yourself professionally. If I may, I would offer a piece of advice. Please don’t do what I did when I first started. I approached learning data analysis backward. My mistake was leading with technology and data. The most efficient way to learn data analysis and have an impact is to lead with business questions. When you lead with technology and data, you fall into the classic trap of having a hammer and then starting to look for nails to pound with it. However, when you first start with business questions, you tend to focus on what truly matters. Take the following examples from various business domains: [Product Management] - What feature(s) are most important to our sticky customers? [Customer Service] - Can we handle more volume with a different mix of agents? [HR] - Is the bad attrition rate of Org A higher compared to Org B? [Marketing] - Are there synergies between digital ad channels? Questions like the above guide you in several ways: 1 – What analysis technique(s) you should learn. Not every technique is applicable in every situation. 2 – What data you need in your analysis efforts to answer the question. 3 – Who is the audience for the answer? Depending on the audience, you may need to choose a technique that provides more detailed explanations (e.g., logistic regression vs a random forest). NOTE – When considering analysis techniques, it is imperative to use the following in your evaluation: A – Can the analysis technique provide an acceptable answer based on the question and audience? B – Which of the shortlist of analysis techniques is the quickest/easiest for you to learn? You want to use the simplest technique that gets the job done. C – Of the shortlist of analysis techniques from A & B, can you use familiar tooling (e.g., Excel)? You want to avoid learning new tools until you absolutely need them. Over the years, I had to learn the above the hard way. My business stakeholders never cared about the underlying technology, only the results. I wasted much time learning “cool” tech that I never used in practice. BTW – Over the years, I’ve ended up using a small number of techniques 90+% of the time in my data analyses: 👉 Exploratory data analysis 👉 Process behavior charts 👉 Random forests 👉 K-means clustering 👉 Logistic regression 👉 Linear regression 👉 Market basket analysis 👉 Process mining Here’s the best part. The first two techniques are easily accomplished using out-of-the-box Excel features. The first six techniques are easily accomplished with Python in Excel. Planning on learning some data analysis skills this weekend? Please keep the above in mind. Your time is valuable. Maximize the ROI of your study efforts. Stay healthy and happy data sleuthing! #excel #microsoftexcel #pythoninexcel #analytics #businessanalytics

If I had to restart my data analytics career from scratch in 2026 - Here is the exact 6-month roadmap I would follow. Not based on what looks good on a resume. Based on what I actually test candidates on when I interview them. Month 1 - Excel & SQL Basics Excel first. Pivot tables, VLOOKUP, basic formulas. Understand how data is structured before you touch a database. Then SQL. SELECT, WHERE, GROUP BY, JOIN. Do not move forward until you can write a query without Googling the syntax. Practice platforms: DataLemur, HackerRank Month 2 - Intermediate SQL + Data Cleaning Window functions, CTEs, subqueries. These are what I test in every single interview. If you cannot write ROW_NUMBER() or RANK() confidently you are not ready for a mid-level role. Spend equal time on data cleaning. Handling nulls, duplicates, outliers. 80% of a real analyst's job lives here. Almost no tutorial covers it enough. Month 3 - Data Visualization Pick one tool. Power BI or Tableau. Learn it deeply before touching the other. Build dashboards from real messy datasets — not tutorial data. The goal is not beautiful charts. The goal is answering a business question in a way a non-technical person understands immediately. Month 4 - Python + AI Tools Pandas and NumPy for data analysis. One end-to-end project on GitHub. But here is what I would do differently in 2026: Learn AI tools in parallel. Use Claude to pressure test your analysis. Use it to draft executive summaries you then edit and own. Use it to explain complex findings in plain language. The analysts getting hired now are not just writing Python. They are combining Python with AI to produce output in half the time. Month 5 - Business Analytics + Storytelling KPIs, revenue analysis, customer segmentation, churn. Study how real businesses use data. Then practice communicating findings to someone who does not know what SQL is. If they understand it - you are ready. This skill separates a $70K analyst from a $120K one. Not the technical stack. Month 6 - Portfolio + Job Search Build 3 projects: -- A sales performance dashboard -- A customer churn analysis -- An operational efficiency report Each needs a clear business question, clean code on GitHub, and one paragraph on what the business should do with the finding. Then optimize your resume, LinkedIn, and start applying. 6 YouTube Channels to Learn Everything : - Alex The Analyst→ https://lnkd.in/gJ75EQZE - Luke Barousse→ youtube.com/@LukeBarousse - Kenji Explains→ youtube.com/@KenjiExplains - Mo Chen→ youtube.com/@mo-chen - StatQuest with Josh Starmer→ youtube.com/@statquest - Thu Vu Data Analytics→ youtube.com/@Thuvu5 6 months is enough if you treat it like a job. 2 hours on weekends will take you 2 years. The roadmap is not the hard part. The discipline is. Where are you on this roadmap right now? ♻️ Repost to help someone just starting out 💭 Tag someone breaking into data analytics 📩 Get my full data analytics career guide: https://lnkd.in/gjUqmQ5H 

Trying to land your first data job but feel stuck in “learning mode”? You’re not alone. Most new analysts spend months on courses without knowing what hiring managers actually care about.  After years helping professionals break into data, here’s what I’ve learned:  Skills don’t speak for themselves, 𝘰𝘶𝘵𝘱𝘶𝘵𝘴 do. If you’re just starting out, here’s the fastest way to build trust with recruiters (even without experience): 𝗦𝘁𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 “𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.” 𝗦𝘁𝗮𝗿𝘁 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘄𝗶𝘁𝗵 𝗶𝘁. That means: – Create one-page projects that answer real business questions  – Use tools you’re learning (SQL, Excel, Power BI, Python) to clean messy data  – Share insights in plain English don’t hide behind dashboards  – Post consistently and narrate your process like a consultant would You don’t need 10 certificates. You need 3 solid case studies that show how you think. 📌 If you’re targeting analyst roles, aim to solve:  ➝ How can we increase customer retention? ➝ Where are we losing money? ➝ What product is underperforming? These aren’t just data questions. They’re business problems solved with data thinking. You won’t master everything at once. But you can show you're learning like a pro. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗶𝗲𝗹𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁.

🎯 How to Approach Any Data Analyst Project & Maximize Learning Whether you're tackling your first data analyst project or your tenth, the real value isn’t just in getting the answer it’s in how you approach the problem and extract insights along the way. Here’s how I break down any project to maximize learning and deliver impact: 1️⃣ Understand the Problem Before Touching the Data Before diving into SQL, Python, or dashboards, take a step back: ✅ What’s the business question you’re trying to answer? ✅ Who will use this data, and what decisions depend on it? ✅ What are the key metrics or success criteria? 💡 Pro Tip: If you can't explain the problem in one sentence, you don’t understand it well enough. 2️⃣ Explore & Clean the Data (Don’t Skip This!) Most real-world data is messy. Spend time: ✔ Checking for missing, duplicate, or inconsistent values ✔ Understanding data types & distributions ✔ Identifying outliers that might skew results 📊 Learning Boost: Try different approaches (e.g., handling missing values via imputation vs. deletion) and compare how they impact the final analysis. 3️⃣ Analyze with a Hypothesis-Driven Approach Instead of randomly looking for trends, form hypotheses: ❓ Does A cause B, or are they just correlated? ❓ Which segments of users are behaving differently? ❓ What external factors could influence this trend? 🔍 Learning Boost: Every project should refine your ability to think critically and spot misleading conclusions. 4️⃣ Communicate Insights, Not Just Numbers Great analysts don’t just present numbers—they tell a story with data: 📌 Start with the key insight, not just the method 📌 Use visuals to simplify complex trends 📌 Tailor insights to your audience (executives, product teams, etc.) 🚀 Learning Boost: Challenge yourself to explain your findings in one sentence to a non-technical person. If you can’t, refine your messaging. 5️⃣ Reflect & Document Learnings Every project is an opportunity to improve: ✅ What assumptions did you make that turned out wrong? ✅ What techniques or tools would have made the process easier? ✅ What would you do differently next time? 📝 Learning Boost: Keep a project journal or start a blog sharing your key takeaways - it’ll reinforce your learning and build your personal brand. Final Thought Every data project is more than just a dataset it’s a chance to develop business acumen, problem-solving skills, and storytelling abilities. The best analysts aren’t those who know the most tools but those who think critically and communicate insights effectively. How do you approach your data projects? Would love to hear your strategies! 👇🔥 #DataAnalytics #SQL #Python # #CareerGrowth #DataScience #Jobs #PythonFunctions #DataAnalyst #CareerGrowth #InterviewTips #DataAnalysis #JobSearch #TechCareers #DataVisualization #projects

If I were to start my Data Analyst career from scratch, here is the roadmap I would follow: 𝟭/ 𝗟𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Understand basic database concepts - Definition and purpose of databases Master data table fundamentals - Rows, columns, and primary keys - Common data types Grasp the concept of relationships between tables - One-to-one, one-to-many, and many-to-many relationships - Foreign keys and table connections 𝟮/ 𝗟𝗲𝗮𝗿𝗻 𝗘𝘅𝗰𝗲𝗹 Start with the basics - Workbooks, worksheets, cells - Common data formats - Sorting and filtering data Learn formulas and functions - Common functions (SUM, AVERAGE, COUNT) - Complex functions (VLOOKUP, IF statements) - User-defined functions Create & customize charts - Chart types and their use cases - Customizing chart components - Conditional formatting for trends Master pivot tables for data analysis - Summarizing large datasets - Interpreting and presenting data insights 𝟯/ 𝗟𝗲𝗮𝗿𝗻 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 Start with the common commands - SELECT, FROM, WHERE - ORDER BY, LIMIT, DISTINCT Learn aggregations - Functions: SUM, COUNT, AVG, MIN, MAX - GROUP BY and HAVING clauses Master JOINs - Types: INNER, LEFT, RIGHT, FULL OUTER, CROSS - When to use each type Learn CTEs and sub-queries - CTEs using WITH clause - Sub-queries for data pre-processing 𝟰/ 𝗟𝗲𝗮𝗿𝗻 𝗮 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 Foundations of data visualization - Chart types and their applications - Data types and variables Learn data sources and preparation - Connecting to various sources - Cleaning and structuring data Master creating charts and dashboards - Common and complex chart creation - Interactive visualizations with filters - Combining charts into dashboards 𝟱/ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 𝗳𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Understand business fundamentals - Basic concepts: revenue, costs, profit - Key metrics across target industries Learn how businesses use data for decisions - Case studies on data-informed strategy - Translating business questions to data queries Develop communication skills - Presenting technical findings to non-technical audiences - Creating executive summaries - Storytelling with data Apply analytics to business problems - Portfolio projects from various industries - Gaining experience through internships or projects 𝟲/ 𝗕𝗼𝗻𝘂𝘀 𝗽𝗼𝗶𝗻𝘁𝘀: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 Dive into window functions - Definition and usage - Construction and aggregation commands - Functions: RANK(), LAG(), NTILE() Learn how to optimize SQL queries - Effective use of indexes - Writing efficient subqueries and joins Understand basics of Extract, Transform and Load processes - ETL process overview - Loading strategies and pipeline scheduling ------ ♻️ Did you find this useful? If so, repost it so others can see it too. 👋🏼 I post about the Data career every day. Follow me for more!

Data Analyst Roadmap: A Step-by-Step Guide Want to break into data analytics or level up your current skills? Here's a practical, step-by-step roadmap that takes you from basics to job-ready in about 6-12 months. 1. Build Your Foundations: - Focus on basics to understand data concepts. No prior experience needed, but familiarity with computers helps. - Learn statistics, probability, business math, and data types. - Master Excel for analysis, formulas, Pivot tables, and visualizations. 2. Get Hands-On with Key Tools: - Pick up SQL to query databases. - Learn Python (or R) with libraries like Pandas, NumPy, Seaborn for data wrangling. - Explore BI tools: Power Bl, Tableau for dashboards and storytelling. 3. Apply Your Skills: Work on real-world projects-cleaning, analyzing, visualizing real datasets. - Build a portfolio (GitHub/website) with dashboards and case studies. - Earn certifications if possible (Google Data Analytics, Microsoft, etc.) - 4. Deepen & Specialize: - Learn about cloud/big data tools and Al-powered analytics. - Specialize in a domain: healthcare, fintech, marketing, climate, etc. Explore advanced topics (ETL, time series, machine learning basics). 5. Portfolio & Jobs: Wrap up with a capstone project. - Sharpen soft skills: communication, teamwork, problem-solving. - Apply to jobs, prep for interviews, and stay connected with the data community. **Pro Tip:** Embrace Al tools to boost your workflow, and never stop learning! Share your projects online for peer feedback and to catch employers' eyes. At last Never Stop Learning.....

If I were starting my Data Analyst career from scratch today… Here’s the 6-step roadmap I’d follow. (Save this for later) 1. Learn how data actually works Before tools, understand the basics: - rows vs columns - structured vs messy data - how businesses use metrics You don’t need to master everything. Just understand how questions turn into data problems. 2. Learn SQL first SQL is the language of data. If you can: - join tables - filter correctly - use window functions - turn raw rows into business metrics You’re already ahead of most beginners. This is where I’d start: 👉 SQL for data analysts track - https://lnkd.in/eicpQ4G5 3. Learn a BI tool (Power BI or Tableau) Once you can pull the data… You need to show it clearly. Most analysts don’t fail because they can’t query data. They fail because stakeholders don’t understand their dashboards. Focus on: - one question per dashboard - clean layouts - clear KPIs If I were starting today, I’d learn: 👉 Data analyst in Power BI track - https://lnkd.in/eUUeZGX7 4. Learn Python for automation You don’t need to become a software engineer. But Python lets you: - automate repetitive work - clean messy datasets - handle larger data Even basic pandas skills can save hours. This is a great place to start: 👉 Python for Data Analysts track - https://lnkd.in/eS-ZpwAP 5. Build projects that answer real questions Not “sales by region.” Instead: - Why did revenue drop last quarter? - What drives repeat purchases? - Where are users dropping off? Projects that solve problems get interviews. 6. Get certified Certifications won’t magically get you a job. But they will help you get noticed. They build your confidence. Some industry-recognized certifications to consider: 👉 Data analyst certification - https://lnkd.in/eJ9jeHZv 👉 PL-300 Power BI data analyst certification - https://lnkd.in/ejiEqr6A 7. Learn how to explain your work Instead of saying: “I built a dashboard.” Say: “I found where users were dropping off and increased completion by 18%.” Tools don’t get you hired. Clear thinking does. -- 👉Save this for later ♻️Repost to help others

I am a Senior Analyst at Accenture with more than 5+ years of experience. Here are 5 pieces of advice I’d give to aspiring data analysts in 2025 who want to break into and grow in this field: ◄ Master Excel before anything else -Excel isn’t outdated, it's foundational. -Pivot tables, VLOOKUP/XLOOKUP, conditional formatting, -Power Query, these are non-negotiables. -Many companies still rely heavily on Excel; knowing it well gives you a strong edge, especially in interviews. ◄ Master SQL before chasing dashboards -Nail the fundamentals—joins, window functions, CTEs, and subqueries. -Learn to write clean, optimized queries that scale. -Understand the why behind each query, don’t just copy from Stack Overflow/Chatgpt. ◄ Think like a business stakeholder, not a data operator -Every chart or metric you build should answer a business question. -Translate insights into actions—don’t just say “Sales dropped,” explain why and what to do next.   -Learn basic business lingo: CAC, CLTV, MRR—this sets you apart instantly. ◄ Communicate with clarity and impact -A simple, clear insight always beats a flashy dashboard. -Summarize in bullet points, highlight “so what?” in every report. -Practice storytelling, take your audience from problem → data → insight → action. ◄ Your career = projects + proof + presence -Document your projects. Share your thought process online. -Build a strong LinkedIn presence, engage with the data community. -Opportunities come to those who show their work, not just those who do the work. – P.S. I’m Mukesh, a Senior Analyst at Accenture. Follow me for more insights on data analysis. Repost if you learned something new today!

These 4 things helped me crack Data Analytics role at McKinsey & Deloitte 💡: Got a few DM’s related to a career in analytics so sharing everything I know - 🖥 1.) Technical skills : ➡SQL - The majority of data resides in a database and to interact with a database we use SQL. We utilize SQL to fetch the data and do the data preprocessing. Problem-solving using SQL queries is also the most asked analytics interview topic. ➡Business math and statistics - Getting very basic familiarity with commonly used analytic terms like YOY growth %, forecasting, revenue, gross profit, market share, percentile, mean, median , ROI, CAC, KPI,  P&L etc. ➡BI tool - We use a BI tool to detect trends in the data and eventually make business decisions. Business Intelligence (BI) tools like Power BI and Tableau offer scalable charting solutions for the business data you have. ➡ Excel - Some of the basic analyses on smaller datasets are done directly in Excel. ➡Python - Used in advanced preprocessing of data along with SQL. 🖥 2.) Communication Skills : Learning to communicate your thoughts is the single most effective skill you can learn to crack any job role. Making a great project and not being able to explain it well during the interview could potentially break the deal. What will be helpful is to prepare draft answers to common interview questions before the interview and practice communicating those answers. 🖥 3.) Business Understanding : Simply put, better basic business understanding is directly proportional to the quality of insights you derive from the data. Here are a few good business case studies to look at : 🖊 How did Amul remain profitable during crisis times like Covid? 🖊 How do companies like Chaayos make profits selling a low gross margin product like tea? 🖊 How does BoAt have a majority market share (>40%) in a crowded hearables market? 🖥 4.) Resume and a well-made active Linkedin profile:  Making a good ATS-friendly resume and posting all your learnings on Linkedin will help you provide visibility to reach potential hiring managers. Did a course? Post about it on Linkedin. Made a project? Post a summary of that project on Linkedin. According to my personal experience and research I did, this is what you need to become a data analyst/business analyst in 2024 but would love to know your thoughts if you agree/disagree on any pointers mentioned? ☀ 🚀 #dataanalyst #dataanalytics #datascience #businessanalyst #businessanalytics