Navigating Data Careers

Before Snowflake, learn SQL. Before Databricks, learn Python. Before dbt, learn Data Modeling. Too many in our industry rush to the "Shiny Data Tools" shop, driven by hype and FOMO. I still remember when Hadoop was everywhere. Now? Hardly seen. Tools are just means to an end. They change every few years. Fundamentals build decade-long careers. Master them first. The shiny tools? Easy to pick up after. A Data Engineer who knows the fundamentals will grasp any new tool fast. A Data Engineer who chases tools only will be obsolete in 3 years. Choose wisely. --- ♻️ Repost if you found it useful! Follow 👉🏻 José for more insights on Data Engineering.

Once you’ve worked in Data Engineering (8 years like me) long enough, you realize tools don’t matter as much. ➥ Whether it’s Airflow or Dagster At its core, it’s just orchestrating dependencies and running jobs on a schedule. The syntax changes, the UI gets fancier, but the underlying challenge is the same: can you build reliable pipelines that never miss a beat, even when something fails at 2 AM? ➥ Whether it’s Spark or Dask At its core, it’s about distributed computation and memory-efficient processing. Sure, Spark’s APIs might feel different from Dask’s, but you’re always wrestling with partitioning, shuffles, and squeezing every ounce of performance out of your cluster before the bill shows up. ➥ Whether it’s Kafka or Pulsar At its core, it’s event streaming, buffering, and pub-sub. The configuration files change, but the real work is designing robust consumer groups, managing offsets, and making sure no critical event gets dropped or duplicated, especially when things scale. ➥ Whether it’s Snowflake, BigQuery, or Redshift At its core, it’s columnar storage, distributed querying, and cost-optimized warehousing. UI, pricing models, or integrations might look shiny, but the tough part is always designing schemas for future analytics, tracking costs, and tuning performance for the business. ➥ Whether it’s dbt or custom SQL pipelines At its core, it’s transformation, testing, and version control of business logic. dbt gives you modularity and lineage, but your biggest wins come from nailing reusable models, data tests that actually catch issues, and making sure every logic change is trackable. ➥ Whether it’s Parquet, Delta, or Iceberg At its core, it’s about data formats optimized for query performance and consistency. New formats will keep appearing, but the big lesson is understanding partitioning, versioning, schema evolution, and choosing what actually fits your use case. Tools come and go. The icons on your resume might change every few years. But fundamentals like: ➥ Data modeling (can you design for flexibility and performance?) ➥ Scalability (will it survive 10x more data or users?) ➥ Latency (does your pipeline deliver data when the business needs it?) ➥ Lineage (can you explain how that metric was built, step-by-step, a year later?) ➥ Monitoring & recovery (will you be the one getting that 3AM pager?) Those are the real make-or-break skills. Focus on what stays true, not just what’s new.

It took me 6 years to land my first Data Science job. Here's how you can do it in (much) less time 👇 1️⃣ 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 — 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝘁𝗼 𝗶𝘁. I learned SQL and Python at the same time... ... thinking that it would make me a better Data Scientist. But I was wrong. Learning two languages at once was counterproductive. I ended up being at both languages & mastering none. 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙢𝙞𝙨𝙩𝙖𝙠𝙚: Master one language before moving onto the next. I recommend SQL, as it is most commonly required. ——— How do you know if you've mastered SQL? You can ✔ Do multi-level queries with CTE and window functions ✔ Use advanced JOINs, like cartesian joins or self-joins ✔ Read error messages and debug your queries ✔ Write complex but optimized queries ✔ Design and build ETL pipelines ——— 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗶𝘁 As a Data Scientist, you 𝘯𝘦𝘦𝘥 to know Statistics. Don't skip the foundations! Start with the basics: ↳ Descriptive Statistics ↳ Probability + Bayes' Theorem ↳ Distributions (e.g. Binomial, Normal etc) Then move to Intermediate topics like ↳ Inferential Statistics ↳ Time series modeling ↳ Machine Learning models But you likely won't need advanced topics like 𝙭 Deep Learning 𝙭 Computer Vision 𝙭 Large Language Models 3️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 For me, this was the hardest skill to build. Because it was so different from coding skills. The most important skills for a Data Scientist are: ↳ Understand how data informs business decisions ↳ Communicate insights in a convincing way ↳ Learn to ask the right questions 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Studying for Product Manager interviews really helped. I love the book Cracking the Product Manager Interview. I read this book t𝘸𝘪𝘤𝘦 before landing my first job. 𝘗𝘚: 𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘥𝘪𝘥 𝘐 𝘮𝘪𝘴𝘴 𝘢𝘣𝘰𝘶𝘵 𝘣𝘳𝘦𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘵𝘰 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦? Repost ♻️ if you found this useful.

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!

🚀 3-Month Data Analyst Roadmap: From Zero to Hero (with Free Resources!) No fluff. No distractions. Just what you need to land your first job. My last post on how to become a data analyst in 3 months sparked much interest, and many of you jumped into my DMs asking for a detailed roadmap. That’s exactly what this post is for. Because the biggest mistake beginners make? Learning everything except what matters. Here’s a laser-focused, beginner-friendly roadmap that takes you from clueless to confident, with free, high-quality resources at every stage. 📅 Month 1: Excel – The Foundation of Analysis 🔹 Week 1–2: Excel Basics Learn formulas, pivot tables, lookups, charts, everything foundational. 🎓 Resource: Complete Excel Course: Beginner to Expert by Simplilearn 🔗 Link: https://lnkd.in/dUB3-R6S 🔹 Week 3–4: Dashboard Building in Excel Build at least 3 interactive dashboards for your portfolio. 🎓 Resource: Creating Interactive Excel Dashboards by Data Decisions 🔗 Link:  https://lnkd.in/dmZR-ubH 📅 Month 2: SQL – Speak the Language of Data 🔹 Week 5: SQL Basics (We’ll use MySQL, but PostgreSQL is fine too) 🎓 Resource: MySQL Tutorial Series by Alex the Analyst 🔗 Link:https://lnkd.in/dY-f9AcW 🔹 Week 6: Advanced SQL – Joins, CTEs, Window Functions 🎓 Resource: SQL from Beginner to Advanced (4 Hours) 🔗 Link: https://lnkd.in/djmgpWeZ 🔹 Week 7–8: Data Storytelling + Report Writing This is where many learners fail. Learn how to communicate insights. 🎓 Resource: Data Storytelling & Visualization Playlist 🔗 Link: https://lnkd.in/dGQhmq4b 📅 Month 3: Microsoft Power BI – Bring Your Data to Life Learn Power BI directly from the official Microsoft learning path. Build beautiful reports, dashboards, and start solving business problems with data. 🎓 Resource: Data Analyst Career Path by Microsoft 🔗 Link: https://lnkd.in/dnhm2R_m 🎯 At This Point… You’re Job-Ready! But don’t stop there. Here's how to boost your visibility: ✅ Document your journey: Post weekly progress on LinkedIn. ✅ Build a portfolio website: Showcase your dashboards, SQL queries, and Power BI reports. 💬 I’ll share how to position yourself for data jobs while still learning in tomorrow’s post. Stay tuned! And if you found this helpful, please like, comment, or share to help others who might be feeling lost on their data journey. You've got this! 💪📊

A lot of people trying to break into data science spend months, sometime even years... Learning the wrong things. They dive deep into neural networks, reinforcement learning, and complex machine learning algorithms, thinking that’s what will land them a job. But when they finally start applying, they realize the job market is looking for something else. So... what do companies want then? Most companies hiring data scientists aren’t looking for cutting-edge AI research. They need professionals who can: + Work with messy, real-world data – Cleaning, structuring, and analyzing data is 80% of the job. If you can’t handle raw datasets, machine learning skills won’t matter. + Use SQL fluently – If you can’t query a database efficiently, you’ll struggle in almost any data role. SQL is still one of the most in-demand skills in the field. + Apply basic statistical thinking – Companies don’t need fancy deep learning models for most problems. They need people who understand probability, regression, and how to make sense of data. + Communicate insights effectively – Data scientists who can translate numbers into clear, actionable recommendations will always be more valuable than those who just build models. + Understand the business problem first – Companies care about ROI, not algorithm complexity. If you don’t connect your work to business impact, you’ll be seen as just another technical hire. So... what mistakes are people doing? - Overloading on Theory Without Application – Learning every ML algorithm but never actually working on real datasets. - Ignoring SQL and Data Wrangling – Machine learning is useless if you can’t efficiently extract and clean data. - Building Portfolio Projects With No Business Impact – Instead of copying Kaggle projects, focus on solving problems that could help a company save money, improve efficiency, or make better decisions. How would I approach it? 1. Master SQL and data manipulation before diving into machine learning. 2. Prioritize problem-solving with real business datasets, not just pre-cleaned Kaggle data. 3. Learn to present insights clearly and tell a compelling data story. Focus on building projects that demonstrate impact, not just model accuracy. The data science job market isn’t looking for people who know the latest AI trends—it’s looking for people who can solve real problems with data. If you’re trying to break into the field, ask yourself: Are you learning what actually matters, or just what looks impressive on paper? Would love to hear your thoughts.

Job seekers are trapped in the "need experience to get a job, but need a job to get experience" cycle. Here is how you can break it: • Gain experience using public datasets: it's not about fancy machine learning projects. Start with cleaning, aggregating, and visualizing data in tools like Excel or Python in Google Colab. Find an interesting datasets from platforms like Kaggle, or US Goverment Open Data (https://data.gov/ ), or data from your city (e.g. Seattle's real-time fire 911 calls https://lnkd.in/gNEdS9Yk). ALWAYS create an artifact—a blog post, a GitHub repository, something to showcase. • Seek opportunities near you: Your uncle is running small business? They might need data insights. Your professor might be eyeing for someone to dissect student performance data. Reach out, offer your skills. Maybe you can collect your own data on your diet or sleep, and analyze it for yourself. (Data science YouTuber Ken Jee analyzed his own health data: https://lnkd.in/gf2SWNDq) No one is offering you a job? Create a job for yourself. • Leverage your current experience: maybe you are just learning data science but you have experience in other industries like marketing, finance, etc. You might not be the best data person, but you could be the person that knows more about the industry than an average data person, and knows more about about data than the average retailer. Leverage your current domain knowledge as a stepping stone, you don't have to start over completely. In the realm of data analytics, the world is your playground. Forget the traditional paths—carve out your own. There are multiple guests on my podcast started their career in non-tech roles. Experience isn't confined to job titles; it's crafted through initiative and passion. I interviewed a career coach who got into Google from non-tech background, learn more from our conversation: Apple: https://lnkd.in/gaM_cWP9 YouTube: https://lnkd.in/gCHTU94N Spotify: https://lnkd.in/g6fGuXzP #Datascience #Career

Want to become a Data Analyst? ... my own realistic roadmap.........based on what actually worked for me When I started, I tried learning everything at once.........Python, SQL, ML, dashboards, and what not. Result? Burnout. Confusion. Imposter syndrome. when i had to start over, here’s exactly what I’d do: ✅ Phase 1: Start simple, build confidence 🎯 Excel – Learn pivot tables, VLOOKUP/XLOOKUP, conditional formatting 🎯 Power BI – Build your first dashboard. Learn DAX basics. 🎯 SQL – SQL queries. (This alone will make you job-ready) 📌 Do 1–2 small projects with just these tools. Focus on storytelling. ✅ Phase 2: Go deeper, get confidence 🎯 Python – Learn pandas for data cleaning, numpy Array and matplotlib & seaborn for visuals. 🎯 Statistics Basics – Central Tendency , Measure of dispersion. 🎯 Data Projects – Clean messy datasets, build dashboards, derive insights. 🔄 Mandatory ( must to have): GitHub, resume-building which is ATS Friendly, LinkedIn Optimization etc. 🤖 But what about Machine Learning? You don’t need it to become a data analyst. But if you’re curious, explore these: 🎯 Linear/Logistic Regression 🎯 Decision Trees & Random Forest Only models you can explain, not just run. 💬 A message from someone who's been in your shoes: I know how overwhelming this path can feel. But the secret isn’t learning 100 tools ........... it’s staying consistent with 3–4. 📌 Save this post. Come back when you feel lost........... And remember: 💡 Depth > Variety. Progress > Perfection. You’ve got this. One step at a time. 👣 follow for more Priyanka SG Data Analyst Mentorship : https://lnkd.in/gasgBQ6k #DataAnalytics #DataAnalystRoadmap #PowerBI #SQL #ExcelToPython #CareerSwitch

6 tips to land your first data analyst job (from someone who’s helped dozens do it) This is for the career changers. The new grads. The curious minds trying to break in. Here’s what actually works (not fluff): 1. Learn the right skills ↳ SQL, Excel, Python, Power BI 2. Get hands-on with real projects ↳ Kaggle. Personal dashboards. Side gigs. 3. Understand business context ↳ Know how data ties to decisions 4. Master your interview skills ↳ It’s not just about what you know, it’s how you explain it 5. Think smart, not hard ↳ Focus on leverage skills: storytelling, impact, automation 6. Create an impactful resume ↳ Your resume should say “I solve problems” I’ve seen these tips change careers. They can change yours too. Which one do you need to focus on most? ♻️ Repost this if someone in your network is job hunting!

A common mistake I see many new Data Analysts making (and honestly, I made this mistake too) is joining a new company or attending an interview without properly understanding the key business metrics and KPIs of that domain. Being skilled in SQL, Python, or Excel is important—but what's equally important is your ability to understand and solve real business problems. For this, you must clearly grasp the company's business context. Not just before joining, even before attending an interview, always spend some time researching the general business metrics relevant to that company's domain. For example: E-commerce: Metrics like Gross Merchandise Value (GMV), Customer Acquisition Cost (CAC), Return on Investment (ROI), Conversion Rate, Retention Rate, Churn Rate etc. Fintech or Banking: Metrics like Loan Default Rate, Net Interest Margin (NIM), Customer Lifetime Value (CLV), Transaction Volume, Monthly Active Users (MAU) etc. Ride-Hailing or Mobility: Metrics like Daily Active Users (DAU), Ride Completion Rate, Average Revenue per User (ARPU), Driver Utilization Rate, Customer Cancellation Rate etc. SaaS Companies: Metrics like Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), Customer Churn, Activation Rate, Customer Acquisition Cost etc. Having this basic understanding beforehand gives you a huge advantage. You'll grasp questions quickly during interviews, communicate your ideas clearly, and start adding value faster once you join. Always remember, Data Analysts don’t just analyze data—we solve real-world business problems. And to solve them effectively, understanding the business context is key. Share your experiences below—let's help each other out!