Core Data Analysis Skills for Job Seekers

Right now, everyone is rushing to learn AI—deep learning, LLMs, and complex machine learning techniques. But most companies aren’t struggling with AI... They’re struggling with basic data management, analytics, and decision-making. Yet, many job seekers believe they need to master deep learning to land a data science role when the reality is much different. Before focusing on AI, it’s essential to develop strong data fundamentals: + SQL and Data Manipulation – Extracting, cleaning, and structuring data efficiently is critical. SQL remains one of the most in-demand skills in data science. + Business-Focused Data Analysis – Companies prioritize professionals who can use data to drive decisions, optimize processes, and create measurable impact. + Data Visualization and Communication – Insights have no value if they can’t be communicated effectively. Data storytelling is an underrated skill that influences decision-making. + Problem-Solving with Simple Models – Many business problems can be solved using logistic regression, decision trees, and forecasting methods rather than complex AI models. Many businesses lack structured data, clean pipelines, and the ability to make sense of the information they already have. Before implementing AI, they need: - Better customer segmentation rather than an AI-powered chatbot - Stronger demand forecasting instead of deep learning solutions - Clearer sales and operations insights before investing in predictive modeling - Organizations are looking for data-driven decision-making. The ability to translate raw data into business impact is far more valuable than knowing how to fine-tune a large language model. Most entry-level roles don’t require deep learning. The focus is on: // Understanding and working with real-world messy data // Solving business problems through analytical thinking // Presenting insights in a way that leads to action AI is only as good as the data that powers it. Strong data fundamentals will always be more valuable than chasing the latest AI trends. Those who focus on building these skills will position themselves for long-term success.

I was a Business Analyst at Amazon from 2017-2019. The analytics skills I needed then are the same now. Last month, I analyzed 100 current job openings. Focusing only on ↳ Data Analyst roles ↳ Business Analyst roles Here's what my analysis revealed... We think AI is rapidly changing the Data industry. But that's not true. The 5 must-have skills for Data Analysts in 2025 are the same skills I interviewed with in 2017. # 𝟱. Python # 𝟰. Excel # 𝟯. Data visualization # 𝟮. Communication # 𝟭. SQL So what does this mean for you? → 𝗟𝗲𝗮𝗿𝗻 𝗦𝗤𝗟 𝗳𝗶𝗿𝘀𝘁 Because SQL is the most in-demand skill. After SQL, learn a data viz tool, and then Excel. This will maximize your chances of landing a job. → 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗼𝗳 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Because communication is the #2 in-demand skill Your portfolio should ↳ Your ability to tell stories with data ↳ Your strong written communication skills → 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗱𝗮𝘁𝗮 𝘃𝗶𝘇 𝘁𝗼𝗼𝗹 It was requested most frequently in these job postings. So get lots of practice with Tableau Public (free!). Making sure each project is 10% better than the last. → 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗱𝗶𝘀𝘁𝗿𝗮𝗰𝘁𝗲𝗱 𝗯𝘆 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 It's so easy to get distracted by the shiny, new thing. But companies aren't hiring for these trendy AI skills. (At least, not for Data Analyst roles.) Stick to the skills that companies actually care about. ——— PS: Want to get better at SQL fast? Get free SQL practice on www.InterviewMaster.ai Because we have ↳ SQL questions on real companies & real products ↳ AI interviewer to help you practice for interviews ↳ Instant, customized feedback to learn FAST ——— ♻️ If you found this useful, please repost it!

I put together 7 skills every data analyst should focus on to further their career. Not just buzzwords, but the real capabilities that will separate good analysts from great ones. Here’s the short list: 1. Data storytelling, not just reporting 2. SQL mastery beyond the basics 3. Business thinking as a core skill 4. Data cleaning instincts 5. Visualization that drives insight 6. Python for automation and analysis 7. Clear communication over complexity These came from what I’ve seen firsthand with working with teams, building scalable systems, and solving business problems with data. If you’re early in your analytics journey (or mentoring someone who is), I hope this gives you a clear focus. What would you add to the list? ————————————— I’m starting a free job search cohort soon, and there will be limited spots. Feel free to message me for more info!

Most people think learning data analysis is just about learning tools. But the real path looks like this: ✅ Build your core knowledge (statistics, databases, programming) ✅ Apply it with practical, real-world projects ✅ Learn to communicate your insights clearly to decision makers ✅ Practice working with messy, imperfect data (because that's what real projects look like) ✅ Develop business context — understand the why behind the analysis ✅ Build a portfolio that shows how you solve problems, not just run reports ✅ Keep iterating and improving — the best analysts never stop learning The fastest way to stand out? Show that you can turn raw data into business value.

If I had to start from scratch to become a Data Analyst in 2025, here's exactly what I’d do: (A roadmap I wish I had when I began 👇) 1. 𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐌𝐚𝐭𝐡 & 𝐒𝐭𝐚𝐭𝐬 Start with Descriptive Stats, Probability, Hypothesis Testing, Linear Algebra & basic Calculus. → These form the backbone of data intuition. 2. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐒𝐐𝐋 • Python: Pandas, NumPy, Matplotlib • SQL: Joins, subqueries, indexing, Window Functions, optimization → These are your bread-and-butter tools. 3. 𝐃𝐚𝐭𝐚 𝐖𝐫𝐚𝐧𝐠𝐥𝐢𝐧𝐠 Learn how to clean, transform, and merge messy data. → 70% of your job is here; don’t skip it. 4. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Use Tableau, Power BI, Seaborn, Plotly → Telling stories with data gets you noticed. 5. 𝐈𝐧𝐭𝐫𝐨 𝐭𝐨 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Know the basics: Regression, Clustering, Model Evaluation → So you can support predictive use cases too. 6. 𝐁𝐮𝐢𝐥𝐝 𝐒𝐨𝐟𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 Storytelling, communication, and structured thinking → This is what makes you irreplaceable. ♻️ Save this roadmap. Follow it step-by-step. And turn your data dreams into a real analytics career! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here → https://lnkd.in/dUfe4Ac6