Minh Duong

Happy Monday, Data Professionals! 📊 This week, I had an eye-opening experience conducting a dashboard review. It struck me just how crucial it is to regularly assess our dashboards with a fresh perspective. Key takeaways: 1. Identify the user story each dashboard represents 2. Critique from the end user's point of view 3. Ensure the dashboard effectively communicates its intended message This process helps us create more impactful, user-centric data visualizations. Are you reviewing your dashboards regularly? Want to dive deeper into this topic? I've got you covered! 🎥 Check out the full video on YouTube for an in-depth exploration. YouTube Link: https://lnkd.in/erxQBG6W #DataVisualization #BusinessIntelligence #UserExperience What are your thoughts on dashboard reviews? I'd love to hear your experiences and best practices in the comments below!

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View my verified achievement from Tableau.

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Are you ready to embark on the journey to become a data analyst by 2024? Here's a crucial insight: The key skill? Communication. Did you know that a significant portion—around 70%—of a data analyst's time is spent communicating insights? It's not just about crunching numbers. It's not just about coding. It's about crafting a narrative. While numbers can convey a message, do people truly grasp its meaning? While images are powerful, do they always provide the necessary insights? As a data analyst, your role will involve translating data into a compelling story, transforming insights into meaningful visuals. To effectively communicate about data, you must be proficient in: Excel SQL, Power BI/Tableau Python But equally important are skills in: Communication Visual storytelling So, if you aspire to become a data analyst by 2024, remember to enrich your skill set, including your vocabulary.

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I came across a relevant data analyst job posting asking for 2-8+ years of experience. I am not quite sure if they are looking for an entry level person or a senior level. Why can't job postings be more applicant friendly? 🙂 #dataanalyst #jobopenings #career #jobs #jobsearch #datascience

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🌟 Had an insightful session with Ali Faisal today on transitioning into a data analyst role! Here are some key takeaways: 1. Essential Skills: Focus on mastering SQL, statistics, and tools like Python & Tableau/PowerBI. Recommendations include Alextheanalyst for SQL, StatQuest for statistics, and courses like Stanford's Machine Learning on Coursera. 2. Learning Resources: Utilize bootcamps, YouTube channels, and hands-on projects. Key topics to cover: probability distributions, central limit theorem, confidence intervals, hypothesis testing, and data cleaning with Python. 3. Certifications: The Machine Learning course from Stanford (Coursera) is highly valued in the industry. 4. Tools & Proficiency: Be comfortable with Excel, SQL, Python, and possibly R before applying. Real-world application and understanding of these tools are crucial. 5. Job Hunting & Interviews: Leverage LinkedIn, Kaggle, and projects to stand out. Expect to demonstrate SQL, Python skills, data cleaning, business knowledge, and data visualization during interviews. 6. Networking: Vital for career growth. Attend events, join clubs, and connect with professionals. ADP Global meetups are a great opportunity! 7. Portfolio & Projects: Showcase practical projects and dashboards. Real-world experience, even from side projects, is invaluable. 8. Trends: Stay updated on generative AI and large language models like ChatGPT. 9. Challenges: Overcome by building connections and continually learning and practicing your skills. 10. Advice for Beginners: Learn, apply, practice, and share your work on LinkedIn. Avoid leaning too heavily on tools—focus on understanding stats and probabilities. Thank you, Ali Faisal, for the valuable guidance! Ready to take these next steps in my data analysis journey. 🚀

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After 4 months working as a data analyst, I found out a few key points that has worked for me: Excel - is a key tool to always use when setting the format of tables SQL - main tool to pull data fast, it has helped me find any information that would have take me more time if I were to do it with a different tool Creation of dashboards - ask as many questions as possible to your stakeholder, this has helped create a better interaction and results to the end user.

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Today's small success is this neat little optimizer function (based on earlier forecasting models/ KPI functions) for demand planning. Finding the best Alpha and Beta parameters based on the MAE KPI. Having a blast with Nicolas Vandeput books, really learning a lot. #datascience #demandplanning #forecasting

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Don't be like me. Learn dbt (data build tool) and you won't miss out on analytics opportunities. Although not a requirement, familiarity with advanced SQL concepts like CTEs will give you an edge. This allows you to focus on the tool's building blocks: the models, sources, tests, documentation, and deployment. Learning the fundamentals will take less than two weeks by committing an hour or less per day.  The best part? It's completely free. Up next, I will jump straight into the advanced concepts like macros and refactoring legacy code here - https://shorturl.at/ehFLM.

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🌟 Seeking an Entry-Level Position in Data Analytics 🌟 Greetings to all my connections, I'm reaching out to share that I'm currently seeking an entry-level position in Data Analytics. This is a field I've grown incredibly passionate about through my academic journey and hands-on projects. ✨ About Me: Education: I graduated in March 2024 with a Bachelor in Investment Economics from the University of Economics, Ho Chi Minh City. My GPA is 3.87/4.0, and I've immersed myself in courses and projects that revolve around data. Experience: Over the past couple of years, I've worked as a Research Assistant at the Health and Agricultural Policy Research Institute (HAPRI). There, I conducted detailed econometric analyses, co-authored research papers, and played a key role in training new interns. Projects: I've tackled various projects that involved analyzing transaction data, creating interactive dashboards, and developing ETL pipelines. These experiences have honed my skills in using tools like Tableau, AWS Lambda, S3, PostgreSQL, and Docker. Skills: I'm proficient in descriptive statistics, econometrics, research methods, and data analysis. I also have hands-on experience with programs like Stata, SPSS, AMOS, Excel, Endnote, Zotero, Tableau, Python, and Docker. 🌱 Why Data Analytics? Coming from an economic and research background, I've found a deep connection between data and decision-making. Every dataset tells a story, and I am passionate about uncovering these stories to drive insights and solutions. My self-taught journey in data analytics has been driven by curiosity and a relentless desire to learn. Additionally, I have developed a strong interest in data engineering and programming, enjoying the process of building data pipelines and ensuring data integrity and accessibility. 🌟 What I’m Looking For: I am eager to take my first step into a professional data analytics role. I’m looking for an opportunity where I can apply my analytical skills and continue to grow. I am particularly interested in roles that involve data analysis, visualization, data engineering and leveraging statistical methods to solve real-world problems. 📢 Let's Connect: If you have any advice, know of any opportunities, or simply want to connect, I would love to hear from you. Thank you for taking the time to read this, and for any support you can offer as I embark on this new journey. #DataAnalytics #JobSearch #CareerOpportunity #DataScience #EntryLevel #Economics #DataDriven #dataengineer

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Good News for people in the logistics industry! LOS ANGELES – April 17, 2024 – The Port of Los Angeles handled 743,417 container units in March, a 19% increase over the previous year. It was the eighth consecutive month of year-over-year growth at the nation’s busiest port.   For the first quarter ending March 31, local dockworkers moved 2,380,503 Twenty-Foot Equivalent Units (TEUs) across Los Angeles marine terminals––nearly 30% more than 2023. It was among the Port’s best first quarter starts, behind only the pandemic import surge in 2021 and 2022.   “Moving into April and the second quarter, I expect robust cargo flow to continue here,” Port of Los Angeles Executive Director Gene Seroka at today’s media briefing. “A strong job market and continued consumer spending, along with our ability to handle additional volume, will help drive cargo to Los Angeles in the coming months.”   Seroka was joined at the Port’s media briefing by Anne Neuberger, Deputy National Security Advisor for Cyber and Emerging Tech. As Deputy Assistant to President Biden, Neuberger advises on matters related to cybersecurity, digital innovation and emerging technologies.   Among other topics, Neuberger shared insights on President Biden’s recent Executive Order to bolster cybersecurity at U.S. ports.   March 2024 loaded imports landed at 379,542 TEUs, up 19% compared to the previous year. Loaded exports came in at 144,718 TEUs, an increase of 47% compared to last year. It was the Port’s best export month since January 2020 and marked 10 consecutive months of year-over-year export gains.   The Port processed 219,158 empty containers, up 7% over 2023.  

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If you are in the supply chain domain, and eagerly looking for a role as a supply chain analyst. You need to be good in following analysis with the help of data. Try to incorporate those in your resume, Hiring managers look for it in the resume. 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Analyzing historical sales data to forecast future demand for products or services. This involves using statistical methods and predictive modeling techniques. 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Analyzing inventory levels, stockouts, and replenishment patterns to optimize inventory levels and minimize carrying costs while ensuring product availability. 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Evaluating supplier performance based on metrics such as on-time delivery, quality, cost, and responsiveness. This helps in identifying top-performing suppliers and areas for improvement. 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing transportation routes, modes, and costs to optimize transportation networks for efficiency and cost savings. This may involve route optimization, carrier selection, and freight cost analysis. 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing warehouse operations to optimize layout, storage capacity, picking processes, and labor utilization. This helps in reducing order fulfillment times and improving overall warehouse efficiency. 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Identifying potential risks in the supply chain such as disruptions in supply, geopolitical issues, or natural disasters. Analyzing these risks and developing strategies to mitigate them. 𝐂𝐨𝐬𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing costs associated with various aspects of the supply chain, including procurement, transportation, warehousing, and inventory carrying costs. This helps in identifying cost-saving opportunities and optimizing overall supply chain expenses. 𝐑𝐨𝐨𝐭 𝐂𝐚𝐮𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Investigating the root causes of supply chain issues such as stockouts, delays, or quality problems. This involves analyzing data to identify underlying factors and implementing corrective actions. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠: Monitoring key performance indicators (KPIs) such as fill rates, order cycle times, inventory turnover, and on-time delivery performance. Analyzing trends in these metrics helps in assessing overall supply chain performance and identifying areas for improvement. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Creating visualizations such as charts, graphs, and dashboards to communicate key insights and findings to stakeholders effectively. Any thoughts on it? Add if I am missing anything.

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📊 Exciting Update! 📈 I'm thrilled to share that I've been diving deep into the world of data analytics with Google Analytics courses over the past few months. 🚀 Currently, I'm nearing the completion of the first series titled "Foundata, data, data, everywhere". 🌟 It's been an enlightening journey exploring the intricacies of data analysis and its application in decision-making processes. From understanding user behavior, data process, and data life cycle to optimizing data analytic strategies, the insights gained from this course have been invaluable. 💡 I can't wait to apply these newfound skills to drive impactful results for businesses and organizations. Suppose you're interested in leveraging the power of data to make informed decisions and drive business growth. In that case, I encourage you to follow my LinkedIn account for more updates on my data analytics journey! Let's connect and learn together. 🤝 #DataAnalytics #GoogleAnalytics #DataDrivenDecisions #ProfessionalDevelopment #LearningJourney #LinkedInLearning #CareerGrowth

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I love utilizing code practice platforms like StrataScratch.Their exercises, like this sample SQL interview problem from Uber, keep you sharp and up to date with "real world" problems. 😎 📶 This particular problem asks you to calculate the percentage of orders loss and profit loss for each service type. If you wanna give it a try follow the link to get started: https://lnkd.in/gfu9P_WW #SQL #StrataScratch #SQLInterviewProblem #PracticeTime #StaySharp #RealWorldProblems #DataAnalysis

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Tired of Feeling Like a Data Analyst Rookie? Level up your skills beyond the bootcamp curriculum! This blog unpacks the soft skills needed to thrive as a senior analyst. (Think communication, collaboration, and more!) Get insider tips on: -         Reproducible Analysis (no more hunting for old code!) -         P-Values Explained (made simple!) -         Tracking Your Analyst Achievements Ready to make an impact? Head over to Muvaamia for more! https://www.muvaamia.com/ #DataAnalysis #SoftSkills #CareerGrowth

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When it comes to crafting a resume for a data analyst or a BI role, quite often I have observed individuals put details like “utilized tableau dashboards to deploy metrics tracking leading to x% conversions”. These are all very quantitative measures. While most of those are absolutely important and must-haves to show your accomplishments and value-add, the qualitatives many times get ignored. Why are the qualitatives important aspects to be shown in a resume? The qualitatives such as communication, team player attitudes and trust building are critical factors that determine the person’s ability to be able to work with others and ensure alignment between technical and non-technical stakeholders. In my experience, when I look back at my university days, I spent most of my time doing data analysis on datasets to build outcomes and score the good grades. In the real industry world, while the data analysis matters, other factors such as stakeholder engagement and alignment were often more critical. How does one mention the qualitative factors in a resume? Within one’s work experience, points such as “collaborated with stakeholders to conduct interviews and gathering of requirements” or “ensured alignment between technical and business teams establishing a sense of trust across the organization” can really go a long way in landing those BI or data analyst interviews. Feel free to reach out for discussion regarding your experiences on the same. Always happy to chat. #dataanalytics #businessintelligence #data #resume

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I just cracked a difficult SQL puzzle which was asked in McKinsey interview. I had to generate each 3-topping pizza possible from a list of ingredients. This is to make sure that we do not repeat the same topping as part of a single combination and within every LHS, combinations are in increasing ascending orders. The results had to be ordered by total cost (highest in the list) and then alphabetically by toppings. My Approach- INNER JOINs: I had used multiple INNER JOIN to combine the rows of table with itself so only unique combinations properly ordered appeared. Dynamically Built Combinations: Created a query to generate all possible combinations without duplicates of any one topping. Fast Sorting: Added sorting of the total cost in descending order and after that by orders name. This way, each combination is played exactly once and in the right order whist being efficiently retrieved. Special thanks to Nick Singh 📕🐒 for inspiring this challenge! Your content never fails to push us into the critical thinking and making our SQL strive for greatness. #CodingChallenge #SQL

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🎓 Are you a recent graduate in data analytics or interested in transitioning into data analytics but lack work experience? Here are three strategic ways to gain real-world experience and land your first data job: 1. Portfolio: You may already find a lot of posts on LinkedIn talking about creating a killer portfolio. So I won’t repeat the same stuff. 2. Part-time Job: Even in your current part-time position, there are opportunities to apply data analytics techniques. Seek out tasks where you can utilise analytical tools to derive insights and add value to your organisation. 3. Volunteering: This is probably the least talked about. Many startups and non-profit organisations require support with data analytics. Volunteering not only allows you to contribute to meaningful causes but also offers invaluable learning experiences by working on real datasets. Benefits of Volunteering: - Flexibility: You can tailor your commitments to fit your schedule. - Low-Pressure learning: It provides a supportive environment for beginners to gain experience without the pressures of a formal job. - Skill Development: Beyond technical skills, it helps develop other underestimated skills like domain knowledge, communication skills, data cleaning techniques, etc. - Networking: You connect with all types of professionals in the field, potentially leading to job opportunities or referrals. Platforms to Explore: 1. DataKind UK: It's an onsite opportunity to volunteer for a weekend or longer projects, collaborating with experienced and novice data analysts/scientists on real datasets from charities. 2. Virtual Volunteering Platforms: The below webistes offer flexibility in project selection and time commitment. We Make Change https://lnkd.in/dzvhpHua Volunteer Match https://lnkd.in/d5EkbU78 Catch a Fire https://lnkd.in/dV8z8j-C Reach Volunteering https://lnkd.in/eqxUcuJ6 Adding volunteer experiences to my CV significantly boosted responses and calls from my applications. Happy volunteering!

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"You can't become a data scientist because you don't have the related degrees." This is the thought I initially had when I wanted to move into the data science field. Lo behold, I have worked professionally as a Data Scientist for over 5 years. I come from a biology degree and have a master's in evolutionary biology. It seems so far from data scientists identifying with Programming, Machine Learning, and Statistics. Yet, I can hold the title of Data Scientist without any degree in related fields. This is because I believe that I can become one. I want to become a data scientist, so I am working to become one. It's not an easy journey as I need to re-learn many skills I didn't have previously and need to stay strong as many internal voice says I am not good enough. But the process is rewarding. With trial and tribulation and persistence with some luck. I managed to become a data scientist. So, don't count yourself out if you want to become a data scientist. Keep working one step at a time. You would find things that are valuable to you. I am attaching my old picture when I was in Sweden for my Master's. It's a reminder of how I was before. If you want to know more about Data Science, Machine Learning, Tutorial, and Tips visit me here>>> https://lnkd.in/gg69EnG ——————— You don't want to miss #python data tips + #datascience and #machinelearning knowledge + #python. Follow Cornellius Y. and press the bell 🔔 to learn together. ———————

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Maven Analytics, Market Maven Analytics 🚆 Excited to share insights from my role as a BI Developer for National Rail in the Maven Rail Challenge! 🛤️ As part of the challenge, I've crafted an exploratory dashboard to decode crucial metrics aiding in strategic decision-making for the rail industry. Here’s a glimpse into our data-driven journey: 📊 Analysis I: Key Performance Indicators (KPIs) 1. Total Revenue, Total Travelers, Average Ticket Price: Assessing financial health, usage levels, and pricing strategy. 2. Peak Travel Time: Optimizing schedules, staffing, and marketing efforts. 3. Most Popular Routes: Prioritizing investments, enhancing services, and planning expansions. 4. On-Time Performance: Improving reliability, customer satisfaction, and operational efficiency. 5. Revenue by Ticket Types & Classes: Tailoring promotions, adjusting pricing, and segmenting customers. 6. Revenue by Payment Method: Enhancing payment systems, tracking trends, and improving security. 🔍 Analysis II: Digging Deeper 1. Identifying Trends: Spotting patterns and seasonal trends. 2. Total Revenue and Total Travelers by Railcard: Understanding the impact and popularity of different railcards. 3. Delay Analysis: Analyzing frequency, causes, and impact of delays. 4. Revenue by Ticket Class: Breaking down revenue and sales volume by ticket class. 💼 Analysis III: Fine-Tuning Strategies 1. Revenue Earned On Time: Ensuring revenue from on-schedule journeys. 2. Revenue Lost Due to Cancellations and Delays: Highlighting potential revenue loss and impact on passengers. 3. Total Revenue By Purchase Type: Understanding customer preferences and revenue streams. 4. Refund Requests Against Journey Status: Managing customer expectations and service quality. 5. Top Popular Stations With Revenue: Identifying revenue-generating stations for strategic investments. 🚀 These analyses empower us to optimize revenue, improve service quality, and drive sustainable growth in the rail industry. Let's keep the momentum going! #DataDrivenDecisions #RailIndustry #BusinessIntelligence #mavenrailchallenge 🚄📈

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