Best way to stand out early in your data career? Think like a business owner 💡 👉 Talk to stakeholders to understand their motivations 👉 Build domain knowledge to learn the nuances of the business 👉 Clearly articulate how your analysis ties to specific goals or KPIs 👉 Draft a measurement plan before you even touch the data Early in my career all I wanted to do was build fancy reports and dashboards, but as soon as I started thinking this way everything changed. Not only did I start earning respect and recognition from management, but I began to actually see (and measure) the impact of my work. This was probably the single biggest catalyst in my career growth and development as an analyst. So to all the seasoned pros out there, what other advice would you give to help an analyst accelerate their career?
Data Analyst Career Growth
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The most challenging transition from "breaking into" a data career to "growing" your data career is your relationship with technical skills. Getting into data requires much investment in growing your technical skills and showing proficiency. The harsh truth is that these technical skills are just the bare minimum. While it's essential to upskill and improve your technical understanding, this alone won't get you promoted. What gets you promoted is applying your technical skills to business problems and getting buy-in to implement them. The key phrase here is "buy-in to implement," and this is where you NEED to become proficient in soft skills and selling internally to your peers and leadership. It's why I spend so much time talking to stakeholders across the business to understand the pains they experience and how data can support their respective business goals. It's why I spend so much time scoping problems and their impact. It's why I spend so much time bringing my stakeholder along the building process so they feel it's their project as well. Stop focusing on data itself, and instead focus on what data can do for your stakeholders and watch your career trajectory accelerate. #data #ai
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Hey LinkedIn Family ! As we venture through an era rich with AI breakthroughs and the rise of large language models, I've noticed a lot of concern among friends and colleagues about the future. As a Python developer, data scientist, and MLOps specialist, I've seen firsthand how the tech landscape is shifting. One key lesson that I learned through my journey : being technically adept is crucial, but it’s not the complete picture. And the more you only rely on your hard skills, the more vulnerable you become! Here's the brighter side: tech is as much about understanding the impact of our work as it is about executing tasks. It’s about seeing the bigger picture. Those who broaden their horizons beyond just code and data often find themselves in a stronger position. 🌟 My advice is simple but powerful: Lean into the career development opportunities your workplace offers. Think beyond the code! Expand your horizons to include management skills, communication, leadership, and technical writing. For those starting out as junior software engineers or data analysts, try your hand at agile management. Document your achievements and your workflows, make sure to to be vocal about your accomplishments, and make sure you’re seen—don’t just wait for tasks to come your way, actively ask for new tasks, and if you are in benches for sometimes, ask to help your colleagues in a new endeavor so that you can show your accomplishments to managers. Being visible matters. If you’re not seen by your manager, you might be overlooked when it comes to recognizing the company’s successes. Collaborate, share your successes, and ensure your contributions are acknowledged. The secret to securing your place in today’s job market? Be proactive, embrace a spirit of professionalism, and steadily ascend the leadership ladder. 🔑 Be more than unfirable. Be invaluable. #CareerDevelopment #Leadership #TechIndustry #MLOps #DataScience #ProfessionalGrowth
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6 things to AVOID in your first data job... - prioritize complex queries when basic ones work just fine - use technical jargon that no one else understands - create obscure, complicated data visualizations - figure out how to do everything without help - memorize every Excel formula imaginable - master every data tool possible Instead, DO these 10 things: - ask smart, insightful questions - build relationships with stakeholders - become the go-to person in 1 or 2 tools - communicate insights clearly & concisely - use language that everyone can understand - get curious about your data and why it matters - continue to learn new techniques and approaches - learn the business and hone your domain knowledge - think about how your analysis will impact business goals - collaborate with people who have more experience than you A data analyst's job is about more than just the data. It's about how to use that data to make a difference.
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Navigating New Data Analyst Challenges... As a new data analyst you will be faced with common challenges.Think of each hurdle is an opportunity for growth, and by mastering these, you'll set yourself up for success. 💎 Excel's Limitations💎 As much as I love Excel... Diversify your arsenal with advanced tools like Python, SQL or Powerbi for a more robust analytical experience and visual experience. Excel is just one piece of the puzzle, not the entire picture. 💎Art of Data Curation💎 Choose data wisely! The temptation to accumulate mountains of data is real, but finesse lies in cherry-picking only the most relevant. Avoid analysis paralysis; be a discerning curator of information. 💎Elegant Visualization💎 Craft compelling visualizations that captivate, not confuse. Avoid creating chaotic graphs that resemble spaghetti. Simplify your visuals... remember, less complexity often equals more impact. 💎Causal Does not = Correlation💎 Distinguish correlation from causation. Mere coincidence of data trends doesn't imply causality. Employ sound statistical techniques to establish causation, avoiding hasty assumptions 💎Honesty in Analysis💎 Maintain data integrity and transparency. Refrain from molding data to fit preconceived narratives. Trust in your insights is paramount... let the data speak its truth. 💎Plain Language💎 Effective communication is vital. Translate your findings into plain language for universal understanding. Avoid cryptic jargon that alienates non-analysts. 💎Collaboration💎 Foster collaboration...data analysis is a team effort. Seek insights from experts, engage colleagues in discussions, and learn from your peers. Together, you can orchestrate success. As a data analyst with over a decade of experience I still struggle with some of these... A chart that I think tells an amazing story but is too complicated... An analysis that is sound but so technical my end user has no idea the impact of the program... Remember working the in world of data is a journey where you are constantly learning... #dataanalytics #dailylearning #dataanalyst #careeradvancement
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Three things I wish I knew when I started my career in data science👇 These are lessons I learned over the course of 6 YoE across a startup, PayPal, and Google. Hope they help you in your growth! 𝟭. 𝗬𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗠𝗟 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 When I was a rookie in data science, I crammed as many algorithms as possible. Hidden Markov model, DBSCAN, SARIMAX, Bi-Directional NN - you name it - I was obsessed with learning them all! Several years later, looking back, as much as I enjoyed learning the intricacies of learning those algorithms, most had no practical applications in real-life projects. In most real-life cases, you will be using simple models because (1) they are easy to train, (2) easy to interpret, and (3) easy to productionize. Such simple models are: - Linear regression with L1 regularization - Logistic regression with L1 regularization - Random forest - Boosted trees - Neural networks - K-Means So, if you are a rookie, keep your learning simple. Just learn the basics, and focus more of your time on applications. 𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗺𝗲𝗻𝘁𝗼𝗿 𝘄𝗵𝗼 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝗳𝗼𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 A good mentor can come in kinds of contexts - it could be school or work. At every stage in my career, I’ve been fortunate to learn from one of the best. In college, my statistics professors were pivotal in fostering my calling in data science. When I started my job in data science, I met managers and senior data scientists who shared techniques to improve my data science workflow and statistical analysis skills - much of which you can’t find on the internet. Find those who will inspire you and help you grow in your career 𝟯. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝘀𝗼𝗳𝘁 𝘀𝗸𝗶𝗹𝗹𝘀 𝗻𝗼𝘄, 𝗻𝗼𝘁 𝗹𝗮𝘁𝗲𝗿. Soft skills are greatly underappreciated in the field. Technical skills will help you get things done. But, if you want to thrive, you have to foster your soft skills. At every stage of your data science project, you are collaborating with people. From the moment, you enter your first meeting with a client, collaborate with colleagues to build a data science tool, to present the MVP solution to the client - you work with people. Data science is merely just a tool data scientists use to solve a business problem. But, at its core, know that it starts with people, and ends with people. Spend time investing in cultivating soft skills today, not later. Active listening skills, presentation skills, and delegation skills take years to build. Resources that were helpful for me were Toastmasters, How to Win Friends and Influence People, Never Eat Alone, and, most importantly, 1,000+ days of trial & error. -- 👉 I report insights on Data Science and ML Systems based on firsthand experience. Follow Daniel Lee for more! 👉 If you are a candidate, looking for your next dream job in data, Use promo 𝗗𝗮𝗻𝗗𝗦 to get 10% off on 𝗗𝗮𝘁𝗮𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄[.]𝗰𝗼𝗺 🚀
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I started working in data analytics 8 years ago. I’ve learned a lot in that time. Here’s one of the BIGGEST mistakes to avoid to be more effective in your role and advance your career. In the world of data analytics, getting bogged down with ad hoc, reactive requests is a common challenge. The key to transcending this reactive loop lies in probing the WHY behind each request. When a co-worker comes to you with a request, don’t just immediately get started working on it and then send them the solution once you finish. It’s important that you discover the real need. Often times business users come with specific data requests based on their limited understanding of what data can do. As an analyst, when someone asks for a particular data set, it's crucial to ask why they need it. Understanding their underlying motivation can reveal more about what they're trying to solve or understand. If you don’t do this you’ll end up wasting A LOT of your time and you won’t even provide them the best solution. Once you grasp the real question or problem, you're in a position to offer a more effective solution. For example, if someone asks for a specific data pull, by understanding their ultimate goal (e.g., understanding customer behavior, improving operational efficiency), you might suggest a better, more comprehensive way to look at the problem using data. Business users aren't typically data experts, and allowing them to dictate data solutions can lead to suboptimal outcomes. Instead, train them to approach you with problems, not preconceived solutions. This approach not only leads to better data-driven decisions but also educates users about the potential and limitations of data analysis. By understanding the true motivation behind data requests, you position yourself not just as a data analyst, but as a strategic partner in problem-solving. This approach allows you to leverage your expertise to provide more insightful, impactful data analysis, ultimately enhancing the decision-making process within the organization. Remember this next time you get a request! 🤝 Every Thursday I send out a free newsletter to 9,000+ data crunchers like you. The content varies each week but includes SQL tips, open data jobs, freelance gigs, datasets for portfolio projects, data memes to keep it fun, and any other useful info we find. Click the link in my profile page to sign up or you can go to thequery.jobs! #data #dataanalyst
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Soft skills fuel growth, but “soft” hides their power. Unlock 12 essential skills to truly be S.O.F.T. Soft skills are the hardest to master. They're also known for boosting career growth. Yet, we still call them “soft,” like they’re optional. Many have talked about rebranding. I say, let's redefine them instead. To me, SOFT means 12 career strengths: 1️⃣ S – Self-Disciplined ↳ Time management: Prioritize what matters most. ↳ Self-awareness: Understand your strengths and limits. ↳ Habits: Build routines that drive consistency. 2️⃣ O – Outcome-Driven ↳ Effectiveness: Focus on doing the right things well. ↳ Goal-setting: Define clear, measurable objectives. ↳ Problem-solving: Tackle challenges with focus. 3️⃣ F – Flexible ↳ Adaptability: Embrace change with confidence. ↳ Creativity: Find innovative solutions under pressure. ↳ Resilience: Stay steady through challenges. 4️⃣ T – Trustworthy ↳ Integrity: Do the right thing, even when it’s hard. ↳ EQ: Approach others with empathy and care. ↳ Clear communication: Build trust & transparency. Soft skills aren’t “soft.” They’re the foundation of growth, trust, and leadership. Let's embrace being S.O.F.T. What skills do you think are most needed to be SOFT? Drop your thoughts in the comments and let’s start a conversation! _______________ ♻️ Repost to help others embrace their SOFT skills. 📌 Follow Jorge Luis Pando for more insights. 📘Feel stuck despite working hard? After teaching 70,000 professionals, I found 5 habits that quietly block career growth, and how to break through them. Get the free guide: https://lnkd.in/gQm5bSPJ
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Are you a data professional who is sad with having a low-impact project at work? Don't be too disheartened; there's many paths to growing your responsibilities! Here's three: 👇 1️⃣ Get buy-in to scale your project up 📈 Is your data science model creating a 100% lift in the click-through rate of a small button? Time to present a pitch about applying that technique to bigger, more prominent entry-points. Small button yesterday, front-page widget tomorrow! 2️⃣ Get "promoted" to a bigger project ⬆️ Proving you can execute on a smaller project is one of the main ways people get increased responsibilities. If you are a data analyst who owns metrics for a small project but go above and beyond the median data analyst, then you'll be the natural choice to own a larger project when names are being pulled. Be the position you want to be tomorrow, today! 3️⃣ Ask for a lateral move or change companies 🏃♀️👋 It's possible that the team you're working on is nothing but an entry-level farm. In that case, see if you can move internally if you feel unsatisfied with your growth. And if your company doesn't recognize your execution or have any opportunities for growth, then get the heck out of there!