Cecilia Carlos

~Elijah Butler “My data analyst job doesn’t use SQL (or whatever tool). How do I make sure I’m ready for my next job?” This is one of the most important pieces of advice I have to offer. If you want to become a great data analyst, don’t let your only learning be doing work tasks. If you do that, your talent is CAPPED by whatever tools and techniques you use during your job. For example, if your first job is only using Excel and basic SQL, when you go for a new job that wants advanced SQL and Tableau skills, you won’t be qualified. So I recommend spending a little time out of work learning. Even just 30 minutes a work day will lead to huge gains. When you first get a job, focus on learning things that will help you in your current job. This way you can kill it, have higher job satisfaction, and get raises. After a few months, focus on learning skills that will either help you improve analytics in your current job and/or land your next job. I’ve had 3 data analyst jobs. If I didn’t learn SQL and Power BI skills outside of my first job, I wouldn’t have landed my second. If I didn’t spend time learning outside of my second job, I wouldn’t have landed a senior role as early. Currently, I’m still learning the line of business my new job is in as well as improving my Python skills to improve analytics for my team. Always be learning something new. In addition to giving you better job opportunities in the future, it’ll improve your job happiness in the present. #data #growth #analytics #continuouslearning #career

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In order to break into the analytics engineering position or to be very good at it, you have to have a very solid understanding of these two core topics: 1. Table Grain/Granularity 2. Different Types of SQL Joins and Unions I often refer to excelling in this position as being a "table surgeon".

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I have been longing to try out decomposition tree analysis (Which can be so important in Path analysis) using Tableau. Today, after trying out with the latest Version 2024.2 of Tableau (Tableau) I was lucky in generating one. They recently introduced a Viz Extensions (Better known as Add on) on the latest release. You can just drag your measures and dimension into it and generate cool charts with it. I tried and below is my results. You can also try it out.

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MS. Excel Shortcuts

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What a clever way to explain SQL left joins! I love how it makes a complex concept so relatable. Breaking things down like this is exactly what we need more of in the data world, keeping it simple and fun. As a data analyst, I see every day how linking data (even when everything doesn’t line up perfectly) can lead to new insights. And if you’re not already following for these weekly AI insights, I’d definitely recommend it. Thanks for sharing this! #SQL #DataMadeSimple #Learning

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This is so great, initiative and effort are so powerful.

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Data analyst cheat code: Learn to say "It depends" with confidence. Half our job is explaining why that simple request actually needs context. The other half? Actually finding that context.

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If you talk to any recruiters that frequently hire data analysts, they all say the same thing: “I wish more data analysts had experience building holiday-themed Power BI projects!” Well, today is your lucky day. I just released a new Thanksgiving-themed guided Power BI project on YouTube. While the project covers the fun subject of Thanksgiving food, I show my real process of taking the data as given, cleaning it, and building and formatting a Power BI dashboard the same way I do as a senior data analyst. Also, I didn’t build the dashboard beforehand or play around much with the day, so while you watch it, you’ll get to see me run into and fix the same mistakes I would on the job. Check it out at the link in the comments.

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Just wrapped up another self-learning Power BI dashboard, and this time, it's all in for the Maven Sales Challenge from Maven Analytics (this is my first entry for any Maven Challenge)! 🌟 Designed specifically for sales managers, this dashboard lets you effortlessly monitor your team's quarterly performance with key metrics like : ✅Wins ✅Win % (Conversion) ✅Sales Values ✅Days to Close ✅Comparison among the Top 5 Agents Check out the full interactive Power BI report is in the link. I will admit, the DAX behind this one-page dashboard was quite the challenge for me. #mavensaleschallenge #powerbi #data #analytics #dataviz #portfolio #maven

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Learning SQL has been fun so far. With Alex Freberg's Data Analyst Bootcamp, I have learned the basics of database queries. I have learned to use the SELECT statement, the difference between GROUP BY and ORDER BY. It challenging at first but I am getting the hang of it. I will have to revisit the beginners' videos to solidify my understanding. #SQL #dataanalysis #dataanalyst

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How to Stand Out as a Data Scientist or Data Analyst in 2024 In today’s market, it’s not just about technical skills standing out requires a well-rounded approach. Here’s what applicants should focus on: 🛠️ Master the Right Tools: Sure, SQL, Python, and Tableau/Power BI are must-haves. But to really stand out, you need to dive deeper into areas like ML, AL, and cloud services. These can be your secret weapons in interviews. 📊 Build a Portfolio That Speaks: Real-world projects that solve actual business problems are gold. A portfolio that shows how you’ve used your skills to create impact will always trump theory. 🤝 Network Like It Matters (Because It Does): Connections are key. Whether through LinkedIn, industry meetups, or virtual conferences, building a strong network can unlock opportunities you didn’t even know existed. 💬 Develop Strong Soft Skills: It’s not enough to crunch numbers. Companies want data professionals who can communicate insights clearly and collaborate effectively with teams across the organization. Soft skills can set you apart from the crowd. 📚 Commit to Continuous Learning: The data field is always evolving. Staying updated with the latest trends in machine learning, AI, and data storytelling will keep you relevant and ahead of the competition. If you’re preparing for a data role in 2024, focus on building a profile that balances technical expertise, real-world experience, and networking. That’s how you’ll stand out. What are you doing to get ahead in the data field? Share your thoughts below!👇 #DataScience #DataAnalytics #CareerGrowth #Networking #SoftSkills #2024

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SQL problem of the day: Whew this was a fun SQL problem. The objective of this problem is to get the total amount for both paying and non-paying customers by date. I am only able to include records where non-paying customers have more downloads then paying customers. I start off by getting both my paying and non-paying customers in separate columns using the good-ole Case statement. I then proceed to make joins to all tables that I will be using. I was able to aggregate the columns by grouping by date. Using the Having clause, I filtered the data to where only non-paying customers have more downloads then paying customers. Finally I brought this query to a close by sorting the records by date ascending or oldest to newest.

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📊 **Poll Time!** 📊 **How does your team use Tableau Server?** 🗳️ Take a second to share your experience: - 🔹 **Data Collaboration** – Centralizing and sharing dashboards - 🔸 **Data Governance** – Controlling access and ensuring data security - 🔹 **Automated Reporting** – Scheduled updates and alerts - 🔸 **Self-Service Analytics** – Empowering users to explore data independently **Why Tableau Server?** For organizations focused on **data-driven decisions**, Tableau Server is a game-changer. It allows teams to **collaborate**, access **real-time insights**, and ensure data **security** across departments. By centralizing dashboards and setting up scheduled updates, teams can work with confidence, knowing they’re using the latest data. The result? Faster, smarter decisions and a more connected, data-driven culture! 💬 Drop your votes and share any unique ways your team uses Tableau Server. Let’s learn from each other! #TableauServer #DataCollaboration #BusinessIntelligence #DataGovernance #RealTimeData #SelfServiceAnalytics #SeniorBIDeveloper

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💡SQL Interview Practice: TikTok User Sign-Ups 💡 I recently tackled a practice SQL interview question that had me thinking about confirmation processes for new users! Here’s the question and my approach—would love to hear if anyone has alternative solutions! 📅 Date: Nov 13, 2024 🌐 Website: Dataford 🔎 Topic: Second Day Confirmation 💡 Question: Assuming the emails table has information on the email addresses of new TikTok users (along with their signup dates), and the texts table contains details about the text confirmation actions. Identify users who didn't confirm on their first day of signup but did confirm on the second day. 📜 Table Schema: emails table: email_id: Unique identifier for each email user_id: Identifier for the sign-up user signup_date: Time when the user signed up texts table: text_id: Identifier for each text record email_id: Foreign key linking to emails signup_action: Action type (confirmation, pending, etc.) action_date: Date of the action 📦 Expected Output (Example): user_id 1322 💡My Thought Process: To identify these users: Calculate the difference between signup_date and action_date, and filter where the difference equals 1 (meaning the confirmation occurred the day after the user signed up). ⚙️ My Solution: See the attached code snippet image below! 📢 Call to Action: Any other ideas for improving or simplifying this query? I’d love to see alternative solutions from the community! If you find this helpful, feel free to ... 👍 React 💬 Comment ♻️ Share #SQL #DataAnalysis #DataScience #InterviewPrep #TechCommunity

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One consistent area where I see Senior Data Analysts outperform base-level (or junior) Data Analysts: Working smart & efficient. And I don't just mean being really fast with technical tools. I mean: - Automating repetitive tasks - Learning when to say "no" or "yes, but.." - Creating self-service tools for stakeholders - Being proactive to steer the team in the right direction - Prioritizing the highest-value work over exciting projects You do these things ... you really stand out. #data #analytics

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💡 **Unlocking Hidden Gems in Tableau Desktop** 💡 Did you know that **Tableau Desktop** has some powerful features that often go unnoticed? These “hidden gems” can make a big difference in how we build and interact with dashboards. Here are a few that I’ve found incredibly helpful: 1️⃣ **Duplicate as Crosstab**: Ever need to view your visualization in table form? Right-click and select "Duplicate as Crosstab." It’s a fast way to create a detailed view, especially for data validation! 2️⃣ **Custom Data Roles**: You can map fields to specific geographic roles (like Zip Codes or States) to boost location-based insights. Perfect for adding context to regional data with just a few clicks! 3️⃣ **Performance Recorder**: This tool allows you to analyze how long each element of your dashboard takes to load. Use it to identify and optimize slow-loading charts, improving the end-user experience. 4️⃣ **Show/Hide Containers**: Create cleaner dashboards by adding a "show/hide" button for containers. This lets viewers expand sections only when needed, maximizing space and keeping dashboards uncluttered. 5️⃣ **Table Calculations for Dynamic Insights**: Table calculations allow you to create dynamic, context-based metrics like running totals or percent changes without altering the underlying data. There’s always something new to learn in Tableau Desktop that can help us create even more **effective and efficient** visualizations! 💬 What’s a hidden feature in Tableau Desktop that you can’t live without? Let’s share tips and boost our Tableau skills together! #TableauDesktop #DataVisualization #TableauTips #BusinessIntelligence #HiddenFeatures #DataEfficiency #SeniorBIDeveloper #TableauDevelopment

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Here is my submission to Maven Analytics commuter challenge! In this challenge, I was tasked with analyzing the MTA's post-covid ridership trends. If you are interested in the insights I gathered, view the project below! #mavencommuterchallenge https://lnkd.in/dAuujjTd

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Certifications don't mean much in the data job hunt. But some do. Proprietary, exam-based certs are the way to go. Excel 1. Microsoft Office Specialist: Excel Associate    ↳ Intermediate 2. Microsoft Office Specialist: Excel Expert    ↳ Advanced Tableau 1. Tableau Desktop Specialist    ↳ Level: Associate 2. Tableau Certified Data Analyst    ↳ Level: Professional 3. Tableau Server Certified Associate    ↳ Level: Professional Power BI 1. Microsoft Certified: Data Analyst Associate    ↳ Intermediate Azure 1. Microsoft Power Platform Solution Architect Expert    ↳Advanced Power Automate 1. Microsoft Power Platform Solution Architect Expert    ↳ Advanced Snowflake 1. SnowPro Core Certification    ↳ Intermediate 2. SnowPro Advanced Certification    ↳ Advanced SQL (Not many options, unfortunately) 1. Microsoft: Azure Database Administrator Associate    ↳ Intermediate Python 1. Entry-Level Python Programmer (PCEP)    ↳ Level: Entry-level 2. Associate in Python Programming (PCAP)    ↳ Level: Associate 3. Professional in Python Programming 1 (PCPP-1)    ↳ Level: Professional 4. Professional in Python Programming 2 (PCPP-2)    ↳ Level: Professional These take a bit longer to obtain... But they'll stand out more than a typical online course. Put all certs from online courses on your LinkedIn. Put only official certs from exams on your resume. Hope this helps a bit. ___ Any certs that I missed? 💻 Repost if you found this useful ♻️

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