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If I had to start learning #dataanalyst all over again, I'd follow this: 1- Learn SQL: ---- Joins (Inner, Left, Full outer and Self) ---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) ---- Group by and Having clause ---- CTE and Subquery ---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc) 2- Learn Excel: ---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc) ---- Logical Functions (IF, AND, OR, NOT) ---- Lookup and Reference (VLookup, INDEX, MATCH etc) ---- Pivot Table, Filters, Slicers 3- Learn BI Tools: ---- Data Integration and ETL (Extract, Transform, Load) ---- Report Generation ---- Data Exploration and Ad-hoc Analysis ---- Dashboard Creation 4- Learn Python (Pandas) Optional: ---- Data Structures, Data Cleaning and Preparation ---- Data Manipulation ---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins) ---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

SQL Mindmap https://www.linkedin.com/posts/sql-analysts_many-people-charge-too-much-to-teach-sql-activity-7219679815837057024-DAQc?

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Complete Topics to Master SQL in 2024 👇👇 https://www.linkedin.com/posts/sql-analysts_complete-topics-to-master-sql-in-2024-activity-7219196705206857728-cMBz

5 most asked SQL Interview Questions for Data Engineer jobs 👇👇 https://t.me/sql_engineer/76

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𝐇𝐨𝐰 𝐭𝐨 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝟏. 𝐄𝐱𝐜𝐞𝐥- Learn formulas, Pivot tables, Lookup, VBA Macros. 𝟐. 𝐒𝐐𝐋- Joins, Windows, CTE is the most important 𝟑. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈- Power Query Editor(PQE), DAX, MCode, RLS 𝟒. 𝐏𝐲𝐭𝐡𝐨𝐧- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries) 5. Practice SQL and Python questions on platforms like 𝐇𝐚𝐜𝐤𝐞𝐫𝐑𝐚𝐧𝐤 or 𝐖𝟑𝐒𝐜𝐡𝐨𝐨𝐥𝐬. 6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc). 7. Learn to use 𝐀𝐈/𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐭𝐨𝐨𝐥𝐬 like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now) 8. Get hands-on experience with one cloud platform: 𝐀𝐳𝐮𝐫𝐞, 𝐀𝐖𝐒, 𝐨𝐫 𝐆𝐂𝐏 9. Work on at least two end-to-end projects. 10. Prepare an ATS-friendly resume and start applying for jobs. 11. Prepare for interviews by going through common interview questions on Google and YouTube. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Websites to practice SQL queries 👇👇 https://www.linkedin.com/posts/sql-analysts_learning-sql-is-not-enough-you-need-to-practice-activity-7217873462596386816-E65q? Like for more ❤️

Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview? I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit. The right time to start applying for data analyst positions depends on a few factors: 1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs. 2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies. 3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles. 4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process. Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods. Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume. Hope it helps :)

I hate to tell you this but... Bootcamps that tell you they can get you a 6-figure data analyst job within 6 weeks (or even 6 months) are lying to you. Don't focus on the salary that you might get. Instead, focus on... - learning the tools - starting your portfolio - revamping your resume - getting active on LinkedIn - putting the skills into practice I guarantee you'll be more successful.

Being analytical is a skill, but it's more of a mindset and a second nature Focusing on just numbers could be analysis, but doesn't necessarily mean you're analytical. E.g. "Sales dropped in Q1 by 5% as compared to Q1 last year in XYZ Region." What caused this exactly? Season? Event? Product reviews/quality? Customer service decline? Marketing spend? PR? Supply chain? Stock depletion? Price increase? Rebranding? Or, when validating the data and understanding the root causes, having a very limited approach. "If that data is missing, it's missing..." Why is it missing? Is it the source? Is it the business decision to not undertake an activity for a time period? Was it there yesterday? Was it supposed to be there? Who can I talk to for understanding the root cause? A LOT of business users I know are more analytical than the data people in their teams. So what makes you analytical? - It's the questions you ask yourself - It's the dots you connect - It's the different avenues you explore - It's the inferences you make - It's the bigger picture you look at It's not just numbers or data.

Hey guys 👋 Since many of you requested for data analytics recorded video lectures, here you go! 👇👇 https://topmate.io/analyst/1068350 It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge. Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your data analytics journey... All the best!👍✌️

Data Analysis is not Power BI. Data Analysis is not Python. Data Analysis is not Excel. Data Analysis is not SQL. Data Analysis is the silent hero pulling strings behind the curtain to transform raw, unstructured data into meaningful insights. It's an art form that goes beyond the tools. Perhaps it's time we shift our focus from the tools to the art and science of data analysis itself.

Top Python Modules 👇👇 https://www.linkedin.com/posts/sql-analysts_many-people-charge-too-much-to-teach-python-activity-7214621481136234496-kcR3

10 Steps to Landing a High Paying Job in Data Analytics 1. Learn SQL - joins & windowing functions is most important 2. Learn Excel- pivoting, lookup, vba, macros is must 3. Learn Dashboarding on POWER BI/ Tableau 4. ⁠Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries 5. ⁠Know basics of descriptive statistics 6. ⁠With AI/ copilot integrated in every tool, know how to use it and add to your projects 7. ⁠Have hands on any 1 cloud platform- AZURE/AWS/GCP 8. ⁠WORK on atleast 2 end to end projects and create a portfolio of it 9. ⁠Prepare an ATS friendly resume & start applying 10. ⁠Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those. Give more interview to boost your chances through consistent practice & feedback 😄👍

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