Data Career Paths and Education

The learning path I’d follow if I were starting data analytics in 2025 No fluff. No endless tutorials. Just a clear path to real-world, portfolio-worthy skills. You don’t “learn data.” You practice decision-making with data. 0. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 Before Python or dashboards, understand what data is for. • Alex The Analyst – https://lnkd.in/dN8xUHZy • Google (Data, Data Everywhere) – https://lnkd.in/d_vwRE5q 1. 𝐄𝐱𝐜𝐞𝐥 & 𝐒𝐐𝐋 Still 70% of the job. Still underrated. • Luke Barousse (Excel for Data Analytics) – https://lnkd.in/dwR2rJsj • Alex the Analyst (SQL in 4 hours) – https://lnkd.in/dUZGt9Jw • Luke Barousse (SQL for Data Analytics) – https://lnkd.in/dadiekDw 2. 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐨𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Python gives you range, even if not every job requires it. • Efficient Python for Data Scientists (Book) – https://lnkd.in/dfe-ZpFP • Kaggle (EDA Notebooks) – https://lnkd.in/d9Sv_QWF • FreeCodeCamp (Data Analysis with Python) – https://lnkd.in/d4x9c3uA • Project: Clean & explore a messy dataset (Netflix, Airbnb, etc.) 3. 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 & 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 This is what stakeholders see. Tell a story that speaks to business. • Tableau Public – https://public.tableau.com • Alex the Analyst (Learn Tableau) – https://lnkd.in/dPr8BQFa • Luke Barousse (Power BI for Beginners) – https://lnkd.in/d5ApkuC2 • Project: Visualize COVID trends, churn, or company growth 4. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 Your portfolio gets you noticed. Show, don’t just tell. • Luke Barousse (Portfolio Guide) – https://lnkd.in/dA_gydAC • Alex the Analyst (Portfolio Projects) – https://lnkd.in/d-mVCk7X • Codebasics (Data Analysis Project) – https://lnkd.in/duk93hQv • Job-Ready Project Guide – https://lnkd.in/dvpQqS9i • Share your work via GitHub + LinkedIn/Medium 5. 𝐉𝐨𝐛 𝐏𝐫𝐞𝐩 & 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬 Polish your resume. Practice SQL. Sharpen your storytelling. • Resume Worded – https://lnkd.in/dK73PQ8U • Pramp – https://www.pramp.com • DataLemur (SQL & Case Prep) – https://www.datalemur.com • Daniel Lee (Data Interview Guide) – https://lnkd.in/dSdshHG8 • Dataford – https://lnkd.in/enbEEgYd • 10-Day Interview Prep Series – https://lnkd.in/dCUPGStB This is the roadmap I wish I had when I started. No guesswork. Just what actually gets you hired. ♻️ If this helped, repost it. Someone out there needs clarity today. P.S: I share weekly guides here – https://lnkd.in/e9wsdgc8

Ready to unlock your career in Data Analytics? Here's your 2024 Roadmap! ➡️ Skills to Master (In This Order): Spreadsheet Fundamentals: Get comfy with Excel or Google Sheets. Learn to manipulate data, create basic visualizations, and understand functions. This is your data playground! SQL: The language of databases. Free courses on platforms like Codecademy, Khan Academy, or even YouTube (check out Alex Freberg and Luke Barousse) can get you querying like a pro. Practice with sample databases. Data Visualization: Learn to tell stories with data! Tableau Public and Power BI are incredible (and free) tools for mastering interactive visualizations. Statistics: Understanding concepts like probability, distributions, and hypothesis testing is critical. StatQuest is my jam! Python or R (Optional): While only sometimes necessary for entry-level, learning one of these languages opens up more advanced data manipulation and analysis. Check out free courses on Coursera or edX. ➡️ Most Common Tech Stack: Databases: SQL is your go-to language for communicating with databases like MySQL, PostgreSQL, or cloud-based services like Dremio or bigquery. Visualization: Tableau, Power BI, and Excel for more straightforward charts. Programming Languages: Python and R are popular for deeper analysis, machine learning, and process automation. ➡️ Top FREE Resources: Platforms: Coursera, edX, Khan Academy, Codecademy, DataCamp (free tier) YouTube Channels: StatQuest, freeCodeCamp, Alex Freberg Communities: Kaggle, Reddit, Inc.'s r/dataanalysis, and r/dataisbeautiful ➡️ Projects: Build a portfolio of projects to showcase your skills. Use public datasets or find real-world problems to solve. ➡️ Networking: Connect with other data enthusiasts on LinkedIn or local meetups. The data community is incredibly helpful! The most important thing is to start learning and practicing consistently. Stay calm - break it down into manageable steps. You've got this! Python Software Foundation YouTube #dataanalytics #sql #dataengineering

Data science and machine learning seem like areas you can start working in right after finishing college. But from what I've seen at work, these are usually not the first jobs people get. Many of my coworkers and the projects I've worked on showed me that people with master's degrees or PhDs are common in these jobs. This makes sense because these jobs are more than just doing tasks. They need a lot of research, new ideas, and planning ahead. Also, in these jobs, you often have to explain complicated things in a way that clients can easily understand. This means not only working with data but also helping clients achieve their goals and save money. If you're thinking about working in data science or AI, you should be ready to always be learning new things. Being successful here means you need to be good at both technical stuff and talking to people. My master's dissertation in Astro Physics really helped me with this. It taught me how to write clearly about complex ideas and talk about them in a way that others can understand. These skills are very important for explaining data and making it useful for clients. If you're interested in data science and machine learning, be ready for a challenging but rewarding path. Always learning, being good with technical details, and being able to communicate well are equally important. #datascience #continuouslearning #careerdevelopment

#DearPhDs, your skills align with MULTIPLE data roles! But you need to emphasize RIGHT skills for EACH role. Understanding the differences between common data-related roles is the KEY to crafting effective resume. Here are 4 common data-related roles in organizations: 🔎 Data Analysts look for patterns, trends, and insights in historical data. Their job is to conduct descriptive analyses, often using tools like Excel and data visualization software to make data understandable for decision-makers. 🔮 Data Scientists not only analyze historical data but also predict future trends using advanced techniques like machine learning. They build models to answer complex questions and uncover hidden insights that guide a company's strategy. 🛠️ Data Engineers create and maintain the structures that store and manage data, ensuring it's accessible, reliable, and ready for analysis. They build the foundation that data analysts and data scientists rely on. 📈 Decision Scientists use data to make smart business decisions. They focus on understanding the cause-and-effect relationships within a company and designing strategies that maximize value. They bridge the gap between data insights and real-world decisions. Now that you better understand these data roles, tailor resume to show your value to that SPECIFIC role: ➝ Focus on relevant skills and experiences. ➝ Speak to responsibilities of that role. ➝ Lead with role's essential skills. ➝ Remove unrelated bullets. ...and, finally, hit 'Apply'! 🚨 Which data role resonates with your skills and aspirations? Share it in the comments below! #dataroles #phdcareers #altac #datacareers #phdjobs ♻️ Found this useful? Share with your network! 💡 Also, tag a fellow PhD or data enthusiast who should see this.