How to Start a Data Job Search as a Beginner

I helped 8 beginners land their first data job in 2024. These 4 costly mistakes held them back before we started to work together in my mentoring program. Out of the 22 clients I supported in my DataShip community, 8 of them were complete beginners in the field of Data. Here’s what I suggested to them to simplify their approach: Avoid: 🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once. 🚫 Spending months on theoretical concepts without hands-on practice. 🚫 Overloading your resume with keywords instead of impactful projects. 🚫 Believing you need a Ph.D. to break into the field. Instead: ✅ Start with Python or R and focus on mastering one language first. ✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter. ✅ Dive into a simple machine learning model (like linear regression) to understand the basics. ✅ Solve real-world problems with open datasets and share them in a portfolio. ✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests. Focus on this for your first 6 months. The key isn’t knowing everything It shows you can solve meaningful problems.

I wasted so much time applying the wrong way. If I could hit reset on my job search, here’s what I would do: 📌Track your applications Keep a record of the company name, job title, and date applied. I was contacted by companies up to a year after I applied. 📌 Save every job description Job postings are often taken down after a while, so save a copy as soon as you apply. They are great for interview prep. 📌 Apply consistently and keep learning Apply to several jobs every day, and continue building your skills. I dedicated my weekends to working on skills or projects. 📌 Tailor your resume for each job Use keywords from the job listing to help get past applicant tracking systems. I was getting immediate rejections when I used the same resume every time. 📌 Build connections and aim for referrals Reach out to employees and try to schedule short calls before applying. I learned that companies prefer referrals over direct applications. P.S. Which of these steps are already part of your process? #DataSistah 🔔Follow me for more tips on how to accelerate your data science career. ♻️Reshare to help others.

Trying to land your first data job but feel stuck in “learning mode”? You’re not alone. Most new analysts spend months on courses without knowing what hiring managers actually care about.  After years helping professionals break into data, here’s what I’ve learned:  Skills don’t speak for themselves, 𝘰𝘶𝘵𝘱𝘶𝘵𝘴 do. If you’re just starting out, here’s the fastest way to build trust with recruiters (even without experience): 𝗦𝘁𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 “𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.” 𝗦𝘁𝗮𝗿𝘁 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘄𝗶𝘁𝗵 𝗶𝘁. That means: – Create one-page projects that answer real business questions  – Use tools you’re learning (SQL, Excel, Power BI, Python) to clean messy data  – Share insights in plain English don’t hide behind dashboards  – Post consistently and narrate your process like a consultant would You don’t need 10 certificates. You need 3 solid case studies that show how you think. 📌 If you’re targeting analyst roles, aim to solve:  ➝ How can we increase customer retention? ➝ Where are we losing money? ➝ What product is underperforming? These aren’t just data questions. They’re business problems solved with data thinking. You won’t master everything at once. But you can show you're learning like a pro. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗶𝗲𝗹𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁.

There’s one mistake I see over and over again. College students think they need more time to prepare. They say: → “I’ll apply next semester.” → “I need to finish this course first.” → “I’ll build a portfolio later.” Here’s the truth no one told me: You’re never going to feel ready. So you might as well start before you think you’re ready. If I were back in school today, here’s what I’d do: → Pick one project and build it all the way through → Don’t jump from tutorial to tutorial. Find one problem and solve it end-to-end. It doesn’t have to be perfect, it just has to be real. → Post your work on LinkedIn → Don’t wait until it’s polished. Post your progress. Your thinking. What broke. What you learned. That’s how people get to know you. → Talk to professionals, not just professors, DM people. Ask questions. Invite someone for a 15-minute coffee chat. Most won’t answer. Some will. That’s all you need. → Build in public, even if it’s uncomfortable → Document your journey. Not to go viral but to stay accountable. To build clarity. To get feedback from people doing the job you want. → Learn the tools companies actually use SQL. Python. Git. Basic cloud. You don’t need to master it all, but learn enough to not get lost when you land your first job. You don’t need to be the smartest person in the room. But if you’re the most consistent, you’ll be surprised how far that takes you. #DataScience #CollegeToCareer #LearningInPublic #EarlyCareer #LinkedInForStudents #CareerAdvice #BuildInPublic #StudentLife #JobSearchTips #LinkedInNews