Alana Stevens

Data Quality: The Foundation of Accurate Analytics Data management isn’t just about storage; it’s about ensuring high data quality. Inaccurate or incomplete data can lead to poor business decisions. Companies like Amazon have built entire systems around maintaining high-quality, clean data to drive accurate predictive analytics. #DataQuality #CleanData #DataDriven

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3 tips when starting your data analytics career 1. Understand the Business Problem First: This is crucial because data analysis without context can lead to irrelevant insights. By focusing on the business needs, you can tailor your approach to provide value where it matters most. 2. Simplify Complex Data for Business Leaders: Visualization is a powerful tool. Creating clear, digestible visuals helps non-technical stakeholders understand the insights and make informed decisions quickly. If your audience can’t grasp it, your hard work will go to waste. 3. Continuous Learning: The data field evolves rapidly. Whether it's new tools, programming languages, or analytical techniques, staying curious and adaptable keeps you ahead and makes your work more impactful. Bonus! A supportive environment helps accelerate learning and boosts confidence, making you not only a better analyst but also a better collaborator. My teammates made me a better analyst!!

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In what order should I learn data analysis tools? In my opinion: 💡SQL Foundations: Mastering data retrieval is key! 💡Spreadsheets & Data Cleaning: Wrangling data is an essential first step. 💡Python & Libraries: Building the skills to analyze and visualize data. 💡Advanced Tools: Exploring options like Tableau or Power BI for next-level insights

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Turning Resource Overwhelm into a Strategic Advantage in Data Analytics When I first started diving deep into data analytics, I faced a challenge I didn’t anticipate: the overwhelming amount of resources available. As someone who values continuous learning, I wanted to absorb everything—from SQL optimization techniques and machine learning applications in Python to advanced dashboard designs in Tableau and Power BI. But the sheer volume of material was paralyzing, and I quickly realised that attempting to learn it all at once was more hindrance than help. Here’s how I shifted my approach and turned the overwhelm into a strategic learning advantage: 1️⃣ Clarifying My End Goals I honed in on the skills that would drive the most impact in my career and projects. Whether it was mastering data integration techniques in Python or fine-tuning my Power BI skills, setting clear priorities allowed me to filter through the noise and focus on resources that directly aligned with my objectives. 2️⃣ Embracing High-Quality, Applicable Learning Rather than bouncing from course to course, I chose comprehensive resources, ensuring I could immediately apply each new skill to real-world projects. This approach led to richer, more meaningful learning—boosting my confidence and helping me deliver results in my role. 3️⃣ Creating a Balanced Learning Routine By dedicating specific times to learning while maintaining a practical, hands-on focus, I found that I could not only absorb more but also retain and apply it effectively. This balance has been key in maintaining both progress and enthusiasm. Today, looking back, I realise that what once felt overwhelming has become an asset. Being selective, strategic, and intentional about my learning allowed me to thrive, developing the critical skills that now drive my career in data analytics. If you’re also feeling the weight of resource overload, remember: clarity, quality, and consistency are key. Focus on what matters most, trust the process, and watch your growth unfold. 😎 #DataAnalytics #ContinuousLearning #DataScience #SQL #Python #PowerBI #Tableau #CareerGrowth #ProfessionalDevelopment #LearningJourney #TechSkills #Analytics #DataDriven #OvercomingChallenges #CareerTips #LearningAndDevelopment

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🙋‍♂️ DAY 2/30 SQl Problem Challenge 🙋‍♂️ 🎯 I recently came across an interesting business challenge during an interview that I think sheds light on real-world financial decision-making. Scenario: 👇 Imagine your team at JPMorgan Chase is preparing to launch a new credit card, and you're asked to estimate how many cards will be issued in the first month. The key question? How well do new credit card launches typically perform in their initial month. Approach: 👇 ✅ To tackle this, I would begin by analyzing historical data on similar product launches, focusing on trends in customer acquisition, marketing spend, target demographics, and key performance indicators (KPIs) like conversion rates and approval rates. ✅ External factors like economic conditions, customer behavior, and competition also play a crucial role in accurate forecasting. ✨ This kind of question emphasizes the importance of data-driven insights in financial product launches, blending statistical analysis, consumer behavior, and risk assessment to drive successful outcomes. 🚀 ✍ Stay tuned for Day 3rd Problem Statement ✍ ✨ "Special thank to these people Shashank Singh 🇮🇳 Aditya Jain Rishabh Mishra Asim Manna Ankit Bansal Zain Ul Hassan Nikhilkumar Basude Amit Singh Abhimanyu Saxena Anshuman Singh Harshit Kumar Harshit Tyagi Sourav Tewari Saurabh Kango Scaler" #BestSellingCategories #eCommerce #DataAnalytics #SQL #BusinessStrategy #SalesOptimization #MarketingInsights #DataDriven #BusinessGrowth 💡 As a curator, I'm here to share valuable content. ✍️ Would you like to see more posts like this? ♻️ Share if you found this helpful!

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