Key Soft Skills for Data Analysts

Tech is vast and intimidating, especially for us coming from non-traditional fields. Amid the buzz of Data Science and Engineering roles, I noticed a trend that these positions often favored candidates with a heavy coding or computer science background, leaving many talented individuals struggling to even land an interview. This realization was my reason to target 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 roles for my internships.  It looked like a space where my skills as a chemical engineer were not just applicable but highly valued. Unlike the steep technical requirements of Data Science and Engineering, Data Analytics offered a platform to leverage my existing strengths while still challenging me to grow. 🍀 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐟𝐨𝐮𝐫 𝐤𝐞𝐲 𝐬𝐤𝐢𝐥𝐥𝐬 𝐭𝐡𝐚𝐭 𝐈 𝐭𝐫𝐢𝐞𝐝 𝐭𝐨 𝐡𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭 𝐢𝐧 𝐦𝐲 𝐫𝐞𝐬𝐮𝐦𝐞 𝐚𝐧𝐝 𝐝𝐮𝐫𝐢𝐧𝐠 𝐦𝐲 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: ♦️ 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠: Data Analytics goes beyond just analyzing numbers. It's about creating stories from the data that help make decisions. This skill is invaluable in a data role, as it allows you to uncover insights that can transform business strategies and outcomes. ♦️𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: From my chemical engineering days, I learned to make complicated concepts easy to understand to both technicians and the leaders alike. This is super important in data jobs because you need to explain data stuff in simple words so everyone can get it. Good communication makes sure that the smart things you find in the data can actually be used to make better decisions and help the team work better together. ♦️𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Whether it's improving industrial processes or everyday tasks, the ability to enhance and streamline operations is a key skill in data analytics. This capability is crucial for identifying inefficiencies that can lead to significant cost savings and performance improvements in any data-driven organization. ♦️𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐢𝐧𝐠: The value of models and dashboards lies not in their complexity, but in their ability to address important business challenges and drive change. This focus on problem-solving is what makes data professionals indispensable, as they provide actionable solutions that directly contribute to the company's success. Embarking on this journey required courage, curiosity, and a willingness to learn! 🌱 If you're standing at crossroads, contemplating a leap into the tech world from a non-traditional background, I hope my journey inspires and motivates you. The path is rich with opportunity, and your unique perspective is not just valuable—it's indispensable.🚀

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

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

How do we mature in a Data Science career? There are different ways to move forward, but sooner or later, you realize 3 key things: 1. Soft skills matter more than coding skills If I were starting over at my first job, I wish I had honed on my soft skills more than my coding skills. When you are good at math and can code in 4 languages, arrogance comes easy. Watch out, because it can get you hired, but not promoted. How we connect with others is important to career growth. 2. Clean slides are important to your models This. Great models get lost in bad slides. If you can't communicate your results simply, or show your equations on slides, you may look smart, but that's about it. Businesses care about simplicity and simple charts that prove why you are right, and is important to the success of hours/weeks of work. 3. Listen to your stakeholders This took a while and still WIP. But I don't start just working on an analysis until if I've been able to understand the stakeholder's pain point and determine if a solution is needed. Its important to let people speak on what they need, in discovery, in more discovery, and especially in presentations. At the end of the day, its their decision to make whether they want to use your analysis for anything. Make room to listen. #datascience #learning

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 🚀

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

This guy here is getting a ton of heat for doing this, but 90% of companies who aren't talking about this are also doing this. With ChatGPT writing resumes and software filling out tax forms, it's easy to wonder where humans fit in today's job market. The answer probably lies in "soft skills," non-technical attributes that define our interactions and work ethics. Unlike "hard skills," such as technical proficiency, soft skills represent your communication style, interpersonal relationships, and personal traits, all aspects AI can't replicate. These skills augment your job performance and career advancement, creating a positive work environment for others and forming strong professional relationships. Employers value them, and they can be honed with practice and dedication. Here are the top 10 AI-immune soft skills: Creativity: AI can produce, but it takes human ingenuity to innovate and offer unique perspectives. Communication: ChatGPT can respond, but humans excel in understanding their audience and communicating effectively. Conflict resolution: AI can't argue, but humans can resolve disagreements constructively, a prized skill for harmonious workplaces. Collaboration: AI can process data, but our social nature lets us collaborate, contribute to teams, and build relationships. Problem-solving: AI can compute, but human discernment is needed to identify issues and make sound decisions. Leadership: AI can organize tasks, but inspiring and guiding a team is uniquely human. Adaptability: Unlike overheated smartphones, humans can adjust to new circumstances and embrace change. Time management: Like computers, humans who efficiently organize tasks and meet deadlines prove their worth. Emotional Intelligence: AI lacks emotions, but understanding and empathizing with others are invaluable human traits. Flexibility: Unlike rigid programming, human employees can adapt roles, accept feedback, and adjust to promote personal growth. It's the age of AI, but these 10 soft skills are an irreplaceable value of being human in the workforce.