Pathway to Data Science Careers

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

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

🚀 Starting Your Data Science Journey Without a Computer Science Background? Here's What You Need to Know! 💡 Breaking into data science without a traditional computer science background can seem daunting, but it's definitely possible with the right approach. Here are some key tips to help you get started: 🌱 Cultivate a Mindset of Curiosity and Resilience: Embrace curiosity and be ready to tackle challenges. Learning data science can be a bumpy road, but with a resilient mindset, you'll be able to overcome any hurdles. 🎯 Leverage Your Transferable Skills: Don't underestimate the value of your unique background! Whether it's analytical thinking from business or statistical knowledge from psychology, your transferable skills can give you a strong foundation for data science. 🧮 Build Your Mathematical Foundation: Linear algebra, calculus, and statistics are the bedrock of many data science algorithms. Invest time in building your knowledge in these areas, and you'll be well-equipped to tackle data science challenges. 🔍 Understand the Data Science Workflow: Familiarize yourself with the data science process, from defining problems to communicating insights. Understanding the big picture will help guide your learning journey. 🤓 Learn by Doing: Engage in practical projects. The best way to solidify your theoretical knowledge is through hands-on experience. Take on projects that interest you and apply what you've learned. Don't be afraid to get your hands dirty with real data! 🤝 Join Data Science Communities: Surround yourself with like-minded learners and experts. Engage in data science communities, both online and offline, to learn from others, collaborate on projects, and stay motivated. 🔄 Embrace Continuous Learning: Data science is an ever-evolving field, so commit to continuous learning. Keep updating your skills and knowledge by exploring new tools, techniques, and resources. Remember, your unique background is an asset in data science. Embrace your diverse perspective and use it to bring fresh insights to the field. Check out my contribution to a collaborative article: https://lnkd.in/gcHYGmWV Share your insights below. Disclaimer: This is my personal opinion and it does not represent the opinion of Intel Corporation. #datascience #analytics #lifelonglearning #continuouslearning 

Wondering why you're not landing your first data role? Often, people attempt to jump multiple steps at once. Jumping from a guided project on YouTube to job searching. Then wondering why they haven’t secured a job 3 months later. End up burning out or giving up and switching careers… It's similar to hitting the gym a few times and expecting to be in peak physical condition. So Here are my 8 progressive steps to work your way up to landing your first data role:   Level 0: Guided Project - Follow along with tutorials to understand the basics. Level 1: Toy Project  - Clean data with most of the work already done (e.g., Kaggle datasets). Level 2: Academic Project - Projects from your coursework that may not be production-ready or end-to-end. Level 3: Personal Project - An end-to-end project solving a small problem, showcasing your initiative. Level 4: Industry-Like Project - Collaborate with teammates and mock stakeholders to simulate real-world scenarios. Level 5: Unpaid Stakeholder Projects - Gain experience through volunteering or unpaid internships. Level 6: Paid Short-Term Projects - Engage in freelancing/internships to earn and learn. Level 7: Paid Long-term Projects - Secure contract or full-time roles to demonstrate sustained performance. Yes, it's possible to progress quickly, maybe skip a step or 2, especially if you have relevant experience. For example, I had a client who landed a $99k entry-level Data Scientist role in less than 3 months. She jumped from Level 0 to level 7 but she had 3 years of experience as a software engineer, thus covering missing levels in her previous roles. This is what my progression looked like: Level 0: Guided projects - Youtube Level 1: Toy Project - Kaggle Level 2: Academic Project - Took many courses and many capstone projects Level 3: Personal Project - Skipped. had SWE internships Level 4: Industry-Like Project - Skipped,  had SWE internships Level 5: Unpaid Stakeholder Projects - Joined a 2-week data science hackathon (3 team members & stakeholder) Level 6: Paid Short-Term Projects - Landed a 4-month internship Level 7: Paid Long-term Projects - Converted To a Full-time Data Scientist role before I graduated (Full-time school & work) Remember, gaining experience is a step-by-step process. Be patient and diligent, and your efforts will pay off. Ready to start building your standout portfolio? Share your level and your next steps comments below! ------------------------- ➕ Follow Jaret André for more daily data job search tips.  🔔 Hit the bell icon to be updated on job searchers' success stories.