SeedMetrics的动态

As a data analyst, I always strive to stay ahead in tackling real-world problems. I found an insightful resource, "13 Questions You Must Ask for Every Data Science Project." These questions provide a structured approach to ensure projects are efficient and add real business value. Whether it's understanding the business goal, scoping out the data, or assessing the solution’s future impact, this framework is a great checklist for anyone involved in data science. #DataScience #DataAnalytics #ProjectManagement #BusinessIntelligence

?? ???????????????? ????????????: ???????????????????? ???????????????????? ???? ?? ?????????????????????? ?????? ?????????????????? ?????????????? ?? During my ongoing Data Analyst Boot Camp with Codebasics, I'm thrilled to share the latest enhancements to my project, now including dynamic Sales and Finance Reports. This project offers comprehensive insights into our business operations and financial performance. Let's dive into the details: ?? ?????????? ???????????? ???????????????????? ??????????????????????: Analyzed data to highlight top-performing customers and key trends. ???????????????? ????????????????: Our Market Performance vs. Target report dives into specific data, pinpointing profit drivers and improvement areas. ?? ?????????????? ???????????? ???????????????? & ???????? ????????????????: Dynamic charts visualize profit and loss data, giving a clear yearly and quarterly view. ???????????????? - ???????????????? ???????????? ????????????????: Insights into profit by market segment shape our financial strategy. ?????????????? ???????????? ???? ??????????????: Calculating margins aids in data-driven decision-making. ?? ?????? ???????????????????? ????????-?????????? ????????????????: Deep insights for informed decision-making. ???????????? ??????????????????????????: Simplifying complex data for better understanding. ?????????????????????? ????????????????: Driving actionable decisions ?? ???????????? ???????????? ??????????????????????: ETL, Power Pivot, PivotTables, DAX in Excel. ???????????? ????????????: Domain knowledge in sales and finance, problem-solving, and critical thinking. Huge ???????????? to Dhaval Patel and Hemanand Vadivel for their guidance! I’m excited to connect with fellow data enthusiasts. Your thoughts and feedback are welcome ???? ____________________________________________________ #DataAnalyst #DataAnalysis #SalesReport #FinanceReport #LinkedIn #DataVisualization #SalesAnalytis #LinkedInLearning

#DAY89 OF #100DAYSOFDATAANALYST ?? ???????? ???????????????? – ?? ???????? ??????????????’?? ???????????? ????????????! ?? As a Data Analyst, your insights are only as good as your data! Before creating dashboards, reports, or models, ???????????????? ?????? ???????? ???? ?????? ?????????? ?????? ???????? ?????????????? ????????. Messy data leads to misleading conclusions—let’s fix that! ?? ???????????? ???????? ???????????????? ???????????????????? :- ?? ?????????????? ???????????? – Should you drop them, fill them, or use imputation? ?? ?? ???????????????????????? ?????????????? – Dates, currency, and categories need standardization for accurate reporting. ?? ?????????????????? ?????????????? – Inflated numbers can throw off KPIs and business decisions. ?? ???????? ?????????????????????????????? – Typos, different spellings ("NY" vs. "New York"), and case variations affect analysis. ?? ???????????????? & ?????????????????? – Are they errors or meaningful business insights? ? ???????? ?????????????????? ?????? ?? ???????? ?????????????? :- ?? ???????????????? & ???????????? ?????????????? ???????? – Use domain knowledge to decide the best strategy. ?? ???????????? ?????????????????????? – Standardize data formats before analysis. ?? ?????????????????????? ???????? ?????????????? – Avoid counting the same transaction or customer twice. ?? ???????????? & ?????????? ???????????????? – Use visualization tools like boxplots in Excel, SQL filters, or Python’s pandas. ?? ???????? ?? ???????? ???????????????? ?????????????????? – This saves time for future analysis! ?? A big thanks to NEERAJ SHARMA Ma'am and SkillCircle? for guiding me on my data analytics journey! ?? A clean dataset means reliable insights and better decision-making! In tomorrow’s post, we’ll dive into ?????????????????????? ???????? ???????????????? (??????) to uncover patterns and trends in our cleaned data. Stay tuned! ?? #DataAnalytics #DataCleaning #DataQuality #100DaysOfDataAnalyst?

90% of Data Analysts and developers create solution for the wrong problems. You should always deeply understand the problem before thinking about thee solution. Don't build a solution for a problem that does not exist. Making your dashboard or analysis useless. My 8 steps list to express and refine a business need in Data Visualization. When gathering the need, never take what users demand for granted. They may not formulate their needs, but propose a solution that ignores the context, which is essential to question 1. When a business user expresses a need, listen carefully before responding 2. Ask why this need has emerged and why it is important 3. Listen to understand, not to answer 4. Rephrase in your own words 5. Ask what value it will bring 6. Align with the strategy 7. Take notes or record 8. Validate and start If these steps are not followed, you risk investing in a solution that addresses a non-existent problem... ... leading you to build an unnecessary product. Your credibility is on the line. And if you're afraid or tired of creating worthless reports and want to focus on building a meaningful, long-term career in Data Visualization, this is for you. ?? Save the date: On January 7th, I’m launching a program designed to transform the way data professionals think, shifting their mindset towards a product design approach to data visualization. Join the waitlist here: https://lnkd.in/eG372TsR #dataanalytics #datavisualization #businessintelligence

Today, let’s talk about the data analysis process. Day 2 of #25DaysofDatawithDesola The truth is, there isn’t one single architecture uniformly followed by every data analysis expert. But, there are some shared fundamentals in every data analysis process. If you’re a data enthusiast, think of this process as a roadmap to guide you from messy datasets to meaningful insights. Here’s a step-by-step breakdown (???????????????? ???????? ?????? ???????????? ???????? ?????????????????? ???????????????????????? ?????????????????????? ????????????): ??. ??????: Before touching any data, ask yourself - what question am I trying to answer? Whether it’s analyzing sales performance or predicting the next pandemic, having a clear problem statement guides the entire process. ??. ???????????????: Once the problem is clear, gather the data you need. Data comes in all shapes and sizes - structured, unstructured, raw or processed. ??. ???????????????: “Garbage in, garbage out” is a rule in data analytics. Raw data is often messy - missing values, duplicates, incorrect formats. This is where data cleaning comes in. ??. ???????????????: Use tools and techniques to identify patterns and extract insights. Carry out exploratory and explanatory analysis and draw conclusions. ??. ???????????: Numbers alone aren’t enough to tell a compelling story. Bring the data to life using the right visuals/charts. Focus on clarity and simplicity. Communicate to help others understand the results. ??. ???????: Use the insight to solve the problem. The conclusion of the analysis should not remain to collect dust on the shelf. Note that the one who conducts the analysis isn’t always the one to make a decision. Recommendations based on the findings can be provided to enable ????????-???????????? ????????????????-????????????. Which step do you find the most challenging and the most interesting? Let me know in the comments. Stay tuned for Day 3 tomorrow, where we’ll explore the skills required to be a data analyst. #25DaysofData #DataAnalysis

A list of KPIs should ?????????? be the foundation of a dashboard... Let's be serious. If we want end-users to trust us, we have to be reliable enough to say "no" when our expectations for creating a quality dashboard are not met. Yesterday, I shared my method with over 4,000 people to avoid this inextricable situation that causes so much trouble for BI developers, Data Product Managers, and Data Analysts. If you missed it, you can check it out here: https://lnkd.in/eQS66PTM #DataAnalytics #Analytics #BusinessIntelligence

?? Excited to Share My Latest Data Analysis Project! ?? I've been working on a fascinating small project analyzing 100 days of sales data, and I'm thrilled to share some key insights and visualizations! ?? ?? Project Overview: Using simulated data, I tracked daily sales, returns, and net sales over a period of 100 days. My goal was to uncover trends and patterns that could provide actionable insights for business decisions. ?? Key Metrics Analyzed: Daily New Sales Daily New Returns Daily New Net Sales ?? Methods Applied: Data Simulation: Generated realistic sales and returns data. Net Sales Calculation: Computed by subtracting returns from sales. Daily Changes: Analyzed the day-to-day changes in sales, returns, and net sales. Moving Averages: Calculated 7-day moving averages to smooth out short-term fluctuations. Summary Statistics: Computed mean, median, and standard deviation for a comprehensive understanding of the data. ?? Visual Insights: Daily New Sales Trends: Highlighted significant sales days, including the highest and lowest sales. Returns Analysis: Tracked return patterns and identified peak return days. Net Sales Performance: Visualized overall net sales trends and pinpointed key highs and lows. ?? Highlights: Maximum Sales Day: Achieved the highest sales of 72 units on day 13! Minimum Sales Day: Saw the lowest sales of 32 units on day 83. Maximum Returns Day: Recorded 15 returns on day 57. Minimum Returns Day: No returns on multiple days, including day 1. Net Sales Trends: Showed a steady increase in net sales, with some fluctuations. ?? Visualizing Data: Here are some charts from the analysis, showcasing daily new sales, returns, and net sales along with their moving averages. These visualizations offer clear insights into the data trends and help in making informed business decisions. This project demonstrates the power of data analysis in driving business insights. I'm looking forward to applying these techniques to real-world datasets and contributing to data-driven decision-making processes. source code : Github:https://lnkd.in/gdUfJgx5 #DataScience #DataAnalysis #Python #Pandas #Visualization #BusinessInsights #SalesData #DataTrends

Hey, Data Analysts and Data Engineers! Are you tired of your projects not getting approved? Do you struggle to get stakeholders to buy into your ideas? I've got you! I listened to an episode of 5-Minute Friday with Joe Reis ?? where he talked about data teams "playing to not lose." I have been thinking about this for some time. Why aren't data teams more proactive in driving positive outcomes for the business? I think some of it has to do with the fact that many data project proposals do not get funded. Why try if the business won't move forward? Here's my advice for getting data projects approved by your stakeholders. ?? We often lead with the technical benefits of our idea: ?? Its query performance is better! ?? Our ETL process will be stronger! ?? We'll increase our data quality! These technical benefits are GREAT! However, they don't close deals. You need to describe the positive impact on people (enablement, trust, etc.) and, ultimately, the tangible (think $$$) outcome for the business. A strong proposal must include all three of these arguments to give you a MUCH better chance of getting your idea approved. Want to learn more? Follow: ?? Joe Reis ?? for all things data ?? Dylan Anderson for data strategy ?? SCOTT TAYLOR for working with the business...and puppets ?? What are your tips for getting technical projects approved? #dataanalytics #dataengineering #business

As a data analyst, I’m passionate about empowering others to excel in this field. That’s why I’ve put together a series of blog posts to help you enhance your skills and avoid common pitfalls. Whether you're a beginner or looking to refine your expertise, there's something for everyone: 1?? 10 Common Data Analysis Mistakes and How to Avoid Them Discover practical tips to steer clear of errors that can compromise your analysis. https://lnkd.in/diVymjWT 2?? From Raw Data to Insights: A Step-by-Step Guide to Data Cleaning Learn how to transform messy datasets into meaningful insights. https://lnkd.in/d3FkDgCP 3?? 5 Key Data Visualization Practices Every Analyst Should Know Master the art of presenting data in a clear and compelling way. https://lnkd.in/d62gyYRS 4?? A Beginner’s Guide to Simulation Models for Data Analysts Dive into the world of simulations and how they can elevate your analytical approach. https://lnkd.in/dsUXRUTr ?? These resources are packed with actionable insights to take your data analysis game to the next level! Let me know which one resonates with you the most or share your own tips in the comments. Let's grow together as data professionals! #DataAnalysis #DataScience #DataVisualization #ProfessionalDevelopment

Do you, the data analyst, feel in charge of deeply understanding the problem the business users want to solve? Or do you rely on the requirements and solely focus on the data and tech side while building data products for them? Isn't figuring out what they really need THEIR job? Let me know in the comments! ?? -- P.S. I’m Evelyn the Chart Doktor. I can show you how to build user centric dashboards. ?? For a FREE crash course, check the link in my profile header! Hit the ?? to see more of my musing.