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Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

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📈 Analytical overview of Telegram channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 51 869 subscribers.

According to the latest data from 16 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 537 over the last 30 days and by 19 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.21%. Within the first 24 hours after publication, content typically collects 1.26% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 740 views. Within the first day, a publication typically gains 654 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun”

Thanks to the high frequency of updates (latest data received on 17 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

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Subscribers

+1924 hours

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+53730 days

3 740

Post views

~ 65424 hours

~ 90248 hours

7.21%

Engagement rate

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Posts per day

Ads index

beta

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Whould I upload next video on SQL?

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Complete Guide to Data Analytics for Beginners 👇👇 https://youtu.be/1-T-VBjLpJo?si=fo_RhbXC46Hg-FVE

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Let’s go back to the basics...! Here’s what you do to become a Data Analyst - Learn SQL (best skill to have) - Learn Excel (hidden requirement) - Learn a BI tool (for nice portfolio projects) Don’t stop there you still have work to do - Create a portfolio - Learn how to create an appealing resume - Learn how to answer interview questions (STAR method) After this, my favorite, networking - Comment on posts - Start posting yourself - Reach out to all the recruiters It can take you anywhere from a couple of months to a year! It all depends on how much time you can dedicate each day! But the longer you wait, the longer it will take! Get after it...!

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Perfect Resume Template

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Will AI Tools for Data Analysis Replace Data Analysts? AI and Data Analysis are two closely related scientific areas, that have been developing rapidly for the last several years. As technology continues to evolve, the question arises: Will AI tools for data analysis replace data analysts? This article aims to describe how AI is related to Data Analysis, what it can do, and will AI tools for data analysis replace data analysts. Starting with the introduction to AI and its fundamental aspects, to how it is going to affect the world in the distant future, the article addresses that and also focuses on how AI is associated with Data analysis. The moderate generation of AI comprises Machine Learning, Deep Learning, and Generative AI. While generative AI is the capability to produce materials and contents like images, sound, and music, Machine Learning is a specific type of GI that prepares an algorithm to feed information to make a prediction.

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🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics. 👉🏻 Basic Aggregation function: 1️⃣ AVG 2️⃣ COUNT 3️⃣ SUM 4️⃣ MIN 5️⃣ MAX 👉🏻 JOINS 1️⃣ Left 2️⃣ Inner 3️⃣ Self (Important, Practice questions on self join) 👉🏻 Windows Function (Important) 1️⃣ Learn how partitioning works 2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE) 3️⃣ Use Cases of LEAD & LAG functions 4️⃣ Use cases of Aggregate window functions 👉🏻 GROUP BY 👉🏻 WHERE vs HAVING 👉🏻 CASE STATEMENT 👉🏻 UNION vs Union ALL 👉🏻 LOGICAL OPERATORS Other Commonly used functions: 👉🏻 IFNULL 👉🏻 COALESCE 👉🏻 ROUND 👉🏻 Working with Date Functions 1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY 2️⃣ Calculating date differences 👉🏻CTE 👉🏻Views & Triggers (optional) Here is an amazing resources to learn & practice SQL: https://t.me/sqlanalyst/195 Hope it helps in your SQL learning 📚

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Checklist to become a Data Analyst

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Mercedes Interview Questions & Answers.pdf0.51 KB

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𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀): - Acts as a bridge between the business side and the IT side of an organization. - Gathers and analyzes business requirements. - Conducts stakeholder meetings. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈): - Focuses on data analysis, reporting, and data visualization using BI tools. - Extracts and transforms data from various sources into meaningful insights to support decision-making. - Builds dashboards and reports. - Identifies trends and patterns in data. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency. 𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.

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Starting exploratory data analysis (EDA) can be tricky. Many of us often feel lost at the beginning. Here's a simple way to get on track: start by creating hypothesis questions and defining KPIs based on your dataset and the field you are working in. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐠𝐮𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐄𝐃𝐀: 1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒀𝒐𝒖𝒓 𝑭𝒊𝒆𝒍𝒅: Learn about the industry and the specific problems you're trying to solve. This will help you know what to look for in your data. 2. 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒚 𝑲𝒆𝒚 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: Decide on the most important KPIs for your analysis. These should align with your business goals and provide clear insights. 3. 𝑪𝒓𝒆𝒂𝒕𝒆 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒆𝒔: Formulate questions that your EDA will try to answer. This keeps your analysis focused and purposeful. Using these steps will make your EDA process smoother and ensure your results are valuable and relevant.

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If you're thinking about building a data analytics projects, you don't need another book, video, or blog post. Just start. You'll learn 10x more by failing big time than by reading someone else's advice 🤷♂️

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Data Analytics Dream Song 👇👇 https://youtu.be/WCTBf7zfq9M?si=76NhVgI0TaB4v_OJ

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Shiny tools like Power BI and Tableau can be tempting to jump into right away. Don't fall into that trap! Before you dive into data visualization, learn SQL first. Why? It's the language of databases and, even if you don't use it in your job, it helps you learn: - databases - data modeling - data storytelling My recommendation? Learn at least... - the fundamentals of SQL syntax (SELECT, FROM) - how to get the data you want (WHERE, HAVING, JOIN) - how to aggregate data (GROUP BY, COUNT, SUM, AVG, MIN, MAX) I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

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I don't have a math or statistics degree. I taught myself SQL, Python, and data visualization tools through online courses and countless practice hours. I've worked on dozens of projects and helped make data-driven decisions. But some days, I still feel like I don't know enough. I look at certain projects and think, "Do I really have enough experience?" Imposter syndrome doesn't care how long you've been in the field. Here's what I've learned along the way: 1/ The field is vast: Data analytics is huge. It's okay not to know everything. Nobody does. 2/ Learning never stops: Every project teaches me something new. That's not a weakness; it's the nature of the job. 3/ My perspective matters: My non-traditional background brings unique insights to problem-solving. 4/ Mistakes are normal: I've made errors in my analysis. It happens. It's how we learn and improve. 5/ Celebrate the wins: When a stakeholder uses my insights to make a decision, that's a win. I try to remember these moments. I still catch myself thinking, "Am I good enough?" when faced with a challenging project. But then I remind myself of how far I've come. I've learned to reframe "I don't know this" to "I don't know this yet." To my fellow data enthusiasts feeling the same way: Your journey is valid. Your skills are valuable. You belong here. 💪 I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

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please avoid making excuses or procrastinating. The provided data analytics resources are more than sufficient for your learning and growth in this field. Stay focused, be consistent, and make the most of these materials. If you're unsure where to start, begin with the SQL tutorials. I'll also include resources for practicing SQL problems online. The key is to take the initiative. Once you start, you'll better understand how everything works. Engage in the hands-on projects mentioned in the sessions. I aim to enhance this product in the future without requiring any extra courses. Feel free to reach out to me if you need any help or guidance. All the best for your future endeavors!

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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!👍✌️

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Power BI Learning Plan in 2024 👇👇 https://t.me/sqlspecialist/745

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SQL Window functions

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SQL Window functions

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Repost from Data Analyst Jobs

Many people ask this common question “Can I get a job with just SQL and Excel?” or “Can I get a job with just Power BI and Python?”. The answer to all of those questions is yes. There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those. However, the combination of tools you learn impacts the total number of jobs you are qualified for. For example, let’s say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs. If you have a success rate of landing a job you’re qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job. Does this mean you should go out there and learn every single skill any data analyst job requires? NO! It’s about finding the core tools that many jobs want. And, in my opinion, those tools are SQL, Excel, and a visualization tool. With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs. So, you can land a job with whatever tools you’re comfortable with. But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.