How to Avoid Mistakes in Data Career Transitions

Looking back, I made a lot of mistakes in my data science journey. If I had to start over, here’s what I’d do differently—so you don’t have to make the same mistakes. 1. Stop Learning Everything & Focus on What Actually Matters When I started, I thought I had to learn every single ML algorithm, master deep learning, and get into reinforcement learning just to land a job. -> Reality? I barely needed any of that in my first role. What actually mattered: ✅ SQL – Used daily, and the most underrated skill in data science. ✅ Python & Pandas – Not just writing code but actually understanding how to work with messy real-world data. ✅ Data Storytelling – If you can’t communicate your insights, your work doesn’t matter. -> Instead of chasing every new trend, I would have focused on strong fundamentals early on. 2. Stop Collecting Certificates & Start Building Projects I used to think more certificates = better job prospects. So I took courses, completed certifications, and added every badge I could find to my LinkedIn. -> Guess what? Not a single recruiter ever asked about them. What actually made a difference: ✅ Building real-world projects that solve problems ✅ Documenting and explaining my work like a case study ✅ Having a GitHub/portfolio that showcases practical skills -> Certificates can be helpful, but they won’t replace actual experience—even if that experience comes from self-initiated projects. 3. Start Networking Way Earlier For too long, I thought I could just apply online and get hired. So I focused on resumes, cover letters, and grinding through applications. -> What I didn’t realize? 🚨 Most jobs are filled through referrals and networking. 🚨 Many roles are never even posted publicly. If I had to start over, I would have: ✅ Attended local meetups and conferences earlier ✅ Engaged on LinkedIn, not just scrolled ✅ Asked for informational interviews with industry professionals -> One conversation can open more doors than 100 cold applications. 4. Learn the Business Side of Data Science Sooner At first, I focused purely on the technical side—writing the best code, getting the highest model accuracy, optimizing algorithms. -> What I didn’t realize? No one cares about a 0.1% model improvement if it doesn’t drive business value. Companies don’t hire data scientists to build models. They hire them to solve business problems. ✅ Understanding the industry and domain is just as important as technical skills. ✅ If you can tie data insights to business impact, you become invaluable. The Biggest Lesson? -> I spent too much time learning things I never used and not enough time on things that actually mattered. If I could start over, I’d focus on practical skills, networking, and solving real problems from day one. If you could restart your career, what’s one thing you’d do differently?

Our client pivoted from Sales to Data Analytics. They did it with no formal data experience. Here are 6 strategies they used to make it happen: Context: When our client reached out, they were stuck. They had spent months applying to data analyst roles with no success, despite completing a data analytics course. They had even received a verbal offer that was later rescinded. Frustration was building, and they were considering a return to account management. We teamed up with them, and things started to change: 1. They Clarified Their Target Role Before working with us, their approach was to just apply to any and every data analytics role that popped up. We helped shift that mindset to focus more of our energy on a smaller set of highly-aligned companies. They used this clarity to create a “Match Score” for each opportunity—filtering out roles that didn’t align with their ideal job. 2. They Optimized Their LinkedIn For What Employers Wanted To See Before joining, they weren’t getting any outreach for roles on LinkedIn. We revamped their LinkedIn headline and profile to include keywords specific to the Data Analytics space as well as projects that illustrated their capabilities. Then the inbound messages began to roll in. 3. They Shifted Their Time From Online Apps To Networking Instead of just applying online, they reached out to alumni from an analytics bootcamp they attended. They specifically focused on people who had successfully transitioned into data roles. One alum gave them insider insights into the hiring process at a target company and even suggested key skills to emphasize their application. 4. They Built A Consistent Outreach System They started sending 5 personalized LinkedIn messages per day to data professionals. They focused on asking for advice, then taking action on it and using it to open the door for a follow-up. This helped build rapport and trust, which led to multiple referrals and interviews. 5. They Went Deep On Interview Prep They knew that other candidates would likely have more “traditional” experience to lean on, so they went deep on interview prep. For technical interviews, they built a portfolio project analyzing Airbnb data to showcase SQL and visualization skills. For behavioral interviews, they prepared answer examples that tied directly into the company’s biggest needs and goals. 6. They Stayed Persistent & Flexible Originally, the recruiter who reached out was asking about a business analyst role. After pitching their SQL and Python skills, our client convinced the recruiter to get them in the door for a data analytics position. Then they used their networking to gain insider info on goals and challenges which they pitched in their interview. That approach secured the offer.

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.

The Strategic Flaw Undermining Career Transitions   Throughout my career guiding professionals through industry and functional transitions, I've identified a consistent pattern among those who struggle to pivot successfully: they position themselves as inexperienced candidates in the new domain rather than as valuable cross-pollinating experts.   This fundamental positioning error creates unnecessary obstacles in an already challenging process.   Successful career pivoters employ a distinctly different approach: • Value Reframing: Positioning their outside perspective as an asset that brings fresh thinking to entrenched industry challenges • Problem-Solution Alignment: Identifying specific issues in the target field that their unique background equips them to address differently • Strategic Narrative Construction: Developing a compelling story that connects their existing expertise to the future needs of the target industry • Selective Credential Building: Acquiring specific knowledge markers that demonstrate commitment while leveraging existing transferable skills   The most effective career transitions aren't accomplished by minimizing differences or attempting to compete directly with industry insiders on their terms.   Rather, they succeed by deliberately highlighting how cross-industry perspective creates unique value in solving the target industry's evolving challenges.   For professionals considering a pivot, the critical shift isn't in acquiring years of new experience, but in reframing existing experience to demonstrate its relevance and value in the new context.   What unexpected industries have you seen professionals successfully transition between by leveraging seemingly unrelated backgrounds?   Sign up to my newsletter for more corporate insights and truths here: https://lnkd.in/ei_uQjju   #deepalivyas #eliterecruiter #recruiter #recruitment #jobsearch #corporate #careertransition #crosspollination #industryshift #careerstrategist

Are you contemplating to pivot into data analytics & data science field? As someone who has been in the field since 2013, and who's been mentoring and coaching others in the data field for the past 7 years, here are my thoughts: 𝐓𝐢𝐦𝐞-𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐠𝐢𝐯𝐞𝐧 𝐭𝐨𝐝𝐚𝐲’𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐞𝐜𝐡 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 𝟏) 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐨𝐯𝐞𝐫 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡 Instead of learning SQL or Python from scratch, focus on using AI tools to meet existing analysis needs. For example, master how to craft prompts to generate SQL or Python code, or use GenAI to build processes, streamline data workflows, and uncover insights faster. You can also harness LLMs to enhance your analysis and insights generation, rather than slowly building your portfolio through years of hands-on experience. Use LLMs to critique and refine your insights and recommendations, ensuring that what you propose aligns with business goals and stakeholder questions. 𝟐) 𝐓𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐠𝐫𝐨𝐰𝐭𝐡 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 Focus on industries with bright futures like GenAI, healthcare, cybersecurity, green energy, or mental health. These sectors are more likely to need data professionals to drive growth through analysis and insights. Do your research by searching for industry reports or talking to seasoned practitioners to identify promising industries. Reports or analyses published by organizations such as below can be your start, e.g. US Bureau of Labor Statistics, McKinsey Global Institute, World Bank, CB Insights, or Gartner. 𝐒𝐨𝐦𝐞 𝐭𝐢𝐦𝐞𝐥𝐞𝐬𝐬 𝐚𝐝𝐯𝐢𝐜𝐞: 𝟏) 𝐆𝐞𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐟𝐢𝐫𝐬𝐭, 𝐜𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐫 Instead of pursuing yet another bootcamp or credential (though you do need baseline technical skills), start by volunteering, interning, or offering to help current practitioners with projects. Build a portfolio using open-source data, freelance on platforms like Fiverr or Upwork, and secure your first data job—even if it’s not a 100% match to your current criteria. The ideal industry or company will come later once you’re in the door. 𝟐) 𝐍𝐞𝐯𝐞𝐫 𝐬𝐭𝐨𝐩 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 Whether it’s validating a specific industry’s need for your skills, creating opportunities for referrals, or honing your pitch for future interviews, networking is critical for career transitions and building long-term influence in your field. Identify “hubs” of people or communities that can help you gain new opportunities. Communities such as Women in Big Data, Women in Data Science (WiDS) Worldwide, or Data Science Association (that I helped co-found), can be your starting point. If you've been contemplating or ready to make the switch, book a Discovery session (via my profile) as your first step! Let’s explore how I can help you in our 1:1 coaching space—where to focus, and what steps to take to launch your new career in data analytics.

For data people considering a move to P&L ownership... I struggled with the transition. Learn from my pain. My first P&L owner manager said "You're too slow". She was right :( When you work in data, it's easy to obsess over perfection. You explore all the rabbit holes. You make sure to caveat every little nuance. You think about how to explain limitations in the data. Pursuit of perfection takes time. Too much. When you move to owning a P&L, speed counts. You're no longer judged on getting the analysis "right" or solving the tough technical data challenges. You're judged on one thing: "is your P&L improving?" It doesn't matter if your analysis was perfect or "just 95%". The only thing that matters is improving the business. Right now, I could name 3 or 4 "imperfections" in our data. But I don't fix them. I don't waste my time. 10 years ago I would have obsessed. Today, I leave it alone. Won't help my business. Not worth it. If you're transitioning to P&L owner, resist the urge to pursue perfection and try to speed yourself up. 95% is usually fine. Imperfect, but "good enough to steer" is great, if it's fast. Focus on impact. Don't let perfection get in the way. Any other tips for someone making this transition? #data #data #analytics #businessintelligence #careers

Everyone’s posting their data analytics wins. Today, I'm sharing my losses. Courses didn't make me a data analyst. Real-world experience did with every failure along the way. Here’s my mistakes: • Scheduled a report with SQL errors that sent blank data to essential managers • Accidentally emailed key stakeholders the wrong file • Rushed a report with a critical formula mistake that had to be retracted and corrected • Updated a dashboard in production without proper testing, breaking visualizations for executive teams These failures taught me to: - Slow down when it matters most - Build consistent checks and processes - Test obsessively before releasing - Create safety nets for mistakes I owned those errors AND the required solutions. The truth? Every failure is an opportunity to grow. The best analysts I know aren't those who never make mistakes. Instead, it’s those who learn from them faster. What mistake taught you the most? Share below 👇 #DataAnalytics #FailForward #ProfessionalGrowth #DataLessons

The fastest way to transition to a career in data. - Learn SQL - Learn Python - Hire a resume writer - Do 5 Kaggle competitions Just kidding. Those things don’t matter as much as people selling you things want you to think they do. Why? Because knowing SQL and Python syntax but lacking the fundamentals and business context to address problems and build solutions is useless. And a pretty resume won’t bring all the recruiters to the yard, unless what’s on it is compelling. Finally, Kaggle is cool and all, but the incentives are much different than the real world. Spending 4 months on a 2% accuracy improvement makes sense in Kaggle-land, but will likely get you PIPed in the real world. Career transitioners often want to abandon their old life and focus on their new shiny skillz. But you have something new grads don’t. Experience. Embrace the skills that only come with experience and use that context to make your projects, resume, and interview more compelling. Sure, you still need to learn new things, but don’t discount your experience and the problem-solving that got you where you are now. #experience #career #tipsandtricks