Navigating Data Careers

Machine learning education is broken, especially for those who aspire to start solving real-world problems at a company. Most classes, courses, and books start with a dataset and show you how to train a model. dataset → model This is, at best, 5% of the work you'll need to do. Real-life problems never start with a "dataset," and they never end after you finish training a model. I've never seen a company with a "dataset" ready to go. In fact, most companies don't even have any data at all. It's your job to determine what data you need and how to collect it. Here is a simplified process that will give you a better idea of how people solve real problems: problem → framing → data → model → feedback → repeat Before understanding the problem and deciding how you'll frame it to solve it, you can't start thinking about datasets. A few other challenges: 1. How do you get data from its source? 2. Is the data diverse enough to solve the problem? 3. Do you have enough data? 4. How is the data biased? 5. How frequently does the data change? 6. How sensitive is the data? 7. Are there missing, inconsistent, or incorrect values? 8. How noisy is the data? 9. How can you trace back every piece of data to its source? 10. Are there any legal restrictions on the use of the data? 11. How do you scale as data grows? 12. How quickly does the data become stale? Building systems that work requires a lot of effort. I wish more people would talk about this.

REEL vs REAL : Data Analyst In REELs and online posts, it looks like: ✔️ Learn SQL ✔️ Learn Python ✔️ Master Excel ✔️ Create dashboards in Power BI / Tableau …and you're set to land your first job! But in REAL life: Project requirements change. Tech stacks are different in every company. Suddenly it’s not just about SQL and Python - it’s also Snowflake, Databricks, AWS, Airflow, Git, scripting, and whatever new tool the team uses. Sometimes it’s internal tools nobody outside the company even knows about. And no matter how many courses you finish, real-world problems will always throw something new your way. The expectation isn’t that you know everything from day one. It’s that you stay curious enough to figure things out. Foundations like SQL, Python, Excel, and Power BI are important - they give you the confidence to start. But building a real career in data goes way beyond ticking off a list of skills. It's about how quickly you can adapt when a tool you’ve never heard of becomes critical to your project. It’s about staying calm when you don’t have all the answers, Googling like a pro, asking good questions, and learning from every messy situation. In real-world data teams, things rarely go by the book. New tech keeps coming in, project needs evolve, and every organization has its own way of doing things. The people who thrive aren’t the ones who knew everything beforehand - they’re the ones who learned how to learn, again and again. ♻️ Repost : If you found this helpful, to reach others who might need it. ✳️ Follow Mariya Joseph for more daily content!

Data Scientists, Engineers, Analysts—these roles are exploding, with data science jobs projected to 𝐠𝐫𝐨𝐰 𝟑𝟔% 𝐛𝐲 𝟐𝟎𝟑𝟏, according to BLS—one of the fastest-growing professions. Meanwhile, according to Gartner 𝟔𝟏% 𝐨𝐟 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 are evolving their data strategies to keep up with AI-driven disruption. But let’s be honest: job titles don’t tell the full story. Here’s what these roles actually do: • 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 – 𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬 They design the structure that makes everything else possible—data lakes, warehouses, and pipelines that ensure information moves efficiently and securely. Without them, data would be a tangled mess. • 𝐃𝐚𝐭𝐚 𝐀𝐥𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐬 – 𝐓𝐡𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐫𝐞𝐚𝐭𝐨𝐫𝐬  They don’t just analyze data; they extract value from it. Using machine learning, statistical modeling, and predictive analytics, they turn raw data into business-changing insights. • 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐯𝐞𝐬 – 𝐓𝐡𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐅𝐢𝐧𝐝𝐞𝐫𝐬 They specialize in uncovering trends, correlations, and anomalies. Whether it’s identifying fraud, optimizing operations, or finding revenue opportunities, their job is to make sense of the noise. • 𝐃𝐚𝐭𝐚 𝐖𝐡𝐢𝐬𝐩𝐞𝐫𝐞𝐫𝐬 – 𝐓𝐡𝐞 𝐀𝐈 𝐇𝐚𝐧𝐝𝐥𝐞𝐫𝐬  They prepare data for AI, ensuring it’s clean, structured, and optimized for machine learning models. Because feeding bad data into AI is like training a GPS with a 10-year-old map. • 𝐃𝐚𝐭𝐚 𝐎𝐫𝐚𝐜𝐥𝐞𝐬 – 𝐓𝐡𝐞 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬  They predict what’s coming next—market trends, customer behavior, risk factors. Using historical data and predictive models, they help businesses make proactive decisions. • 𝐃𝐚𝐭𝐚 𝐒𝐮𝐫𝐠𝐞𝐨𝐧𝐬 – 𝐓𝐡𝐞 𝐂𝐥𝐞𝐚𝐧-𝐔𝐩 𝐂𝐫𝐞𝐰  They fix bad data, remove errors, and ensure consistency. Because even the best algorithms are useless if they’re working with garbage. • 𝐃𝐚𝐭𝐚 𝐏𝐡𝐢𝐥𝐨𝐬𝐨𝐩𝐡𝐞𝐫𝐬 – 𝐓𝐡𝐞 𝐄𝐭𝐡𝐢𝐜𝐬 & 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐆𝐮𝐢𝐝𝐞𝐬  They ask the big questions: Should we use this data? Is it biased? Does it comply with privacy laws? They ensure data-driven decisions are also responsible ones. With Chief Data Officers now overseeing AI strategy at 58% of organizations, the importance of these roles is only growing. So, which one best describes what you do? Or do you have a better title for your role? Drop it in the comments! 𝐅𝐨𝐫 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: https://lnkd.in/eM6c3FkG ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

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. 𝘗��: 𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘥𝘪𝘥 𝘐 𝘮𝘪𝘴𝘴 𝘢𝘣𝘰𝘶𝘵 𝘣𝘳𝘦𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘵𝘰 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦? Repost ♻️ if you found this useful.

Back in 2020, being a Data Analyst often meant being a generalist — handling everything from reporting to modeling, sometimes even engineering tasks. But fast forward to 2025, and the landscape looks very different. Now, we’re seeing a growing demand for specialized roles like: ✔️ Product Analyst ✔️ Marketing Analyst ✔️ Risk Analyst ✔️ Power BI Developer ✔️ Healthcare Analyst …and many more. This shift reflects the increasing complexity of data challenges and the need for deeper domain expertise. As someone navigating the data field, I find it both exciting and essential to keep sharpening skills in specific areas while staying curious about the bigger picture. 💡 Tip: Whether you’re just starting out or already in the field — focus on a niche, but learn to collaborate across roles. That’s where real impact happens. 👉 Which of these roles are you working toward or exploring? I’d love to hear your path. #DataAnalytics #CareerGrowth #DataAnalyst2025 #PowerBI #SQL #ProductAnalytics #Specialization #LinkedInLearning

Everyone wants to break into data. But most are focused on just one piece of the puzzle. Here’s what I’ve learned after helping dozens land roles in data (and doing it myself): 1. Technical skills will get you through the door. SQL. Python. Excel. Power BI. But being “technical” alone isn’t enough. 2. Soft skills move you forward. Can you communicate findings clearly? Can you translate numbers into narratives? 3. Networking opens unexpected doors. Most offers don’t come from cold applications. They come from conversations. 4. Motivation is your long-term advantage. Because the journey is hard. But if you stay consistent, you’re unstoppable. It’s not about mastering one area. It’s about stacking strengths until you break through. If you’re just starting your journey into data: Build all four pillars. Which pillar are you focused on right now?

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.

❌ I spent 5 months learning Machine Learning… and never used it once as a Data Analyst When I started my data journey, I didn’t know what to focus on, and I had no clear pathway what I need to learn or how to stand out among thousands of applicants. At that time, AI was growing rapidly and becoming so popular and trendy. Terms like “Machine Learning”, “Python”, and “AI” immediately captured my attention because they sounded so powerful and fancy. I thought if I added them to my resume, I would become more competitive and stronger than other people. On top of that, I also got distracted by job descriptions for Junior Data Analyst roles that listed requirements like Python, ETL pipelines, and even predictive modeling—which made me believe those were must-have skills from day one. But I was wrong. 🚫 I wasted too much time studying things that a Data Analyst doesn’t really need and rarely uses in a career. I’m honestly surprised how many people have reached out to me and said they faced the same struggle—without a clear pathway, they also didn’t know what to focus on. Even many universities offering Business Analytics courses put heavy emphasis on R, Python, and Machine Learning. ✨ From my experience, here’s what you should focus on to secure a Data Analyst role: Data Analyst: Work with structured data to identify patterns, create reports, and provide insights that guide business decisions. Core tools: Power BI / Tableau (build dashboards), SQL (Beginner → Intermediate), Excel (Power Query, Macros, VBA). 💡 My best tip: Data Analysts live and breathe data visualization. Since many people associate the role with dashboards, a strong Power BI portfolio can instantly capture HR’s attention. I tested this myself (and experienced it from many successful people), and it really works—once I focused on building and sharing more Power BI projects on LinkedIn, the number of interviews I landed increased significantly. Data Engineer: Transform raw data into structured data, build pipelines, and maintain systems that make data reliable and accessible. Core tools: Python, SQL, Cloud platforms (AWS/Azure/GCP), ETL pipelines. Data Scientist: Apply statistics and machine learning to explore data, build predictive models, and uncover deeper business opportunities. Core tools: Python, R, ML frameworks, Statistics, Mathematics. ⚠️ Don’t let job descriptions trick you. Many will list every tool under the sun, but the truth is: ➡️ Focus on SQL, Excel, and BI tools first. ➡️ Build projects (Dashboards) that show you can turn data into insights. ➡️ Save Machine Learning and Python for later, if you decide to move into Data Science and Data Engineering. ✨ let’s connect with me and share your ideas (I would love to hear it from you). Thank you very much! #DataAnalytics #PowerBI #SQL #CareerGrowth #DataVisualization