Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
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Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data
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Сітка:📈 Аналітичний огляд Telegram-каналу Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Канал Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 39 605 підписників, посідаючи 4 700 місце в категорії Освіта та 9 668 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 39 605 підписників.
За останніми даними від 28 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -44, а за останні 24 години на -4, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.22%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.70% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 878 переглядів. Протягом першої доби публікація в середньому набирає 277 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як analytic, dataset, visualization, sql, learning.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
Ads/ Promo: @love_data”
Завдяки високій частоті оновлень (останні дані отримано 29 жовтня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
39 605
Підписники
-424 години
-497 днів
-4430 днів
Архів дописів
𝗧𝗶𝗿𝗲𝗱 𝗼𝗳 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗴𝗼𝗼𝗱 𝗔𝗜/𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲?😍
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Must-Know SQL Functions
Guys, today we’re diving into some essential SQL functions that can make your queries powerful and efficient!
1️⃣ Aggregate Functions:
SUM(): Adds up values.
AVG(): Finds the average.
COUNT(): Counts rows.
MIN() / MAX(): Gets the smallest or largest value.
Example:
SELECT department, COUNT(*) AS num_employees, AVG(salary) AS avg_salary
FROM Employees
GROUP BY department;
2️⃣ String Functions:
CONCAT(): Combines strings.
SUBSTRING(): Extracts part of a string.
UPPER() / LOWER(): Changes case.
Example:
SELECT CONCAT(first_name, ' ', last_name) AS full_name
FROM Employees;
3️⃣ Date Functions:
NOW(): Returns the current date and time.
DATEDIFF(): Calculates the difference between dates.
YEAR() / MONTH(): Extracts year/month from a date.
Example:
SELECT name, DATEDIFF(NOW(), hire_date) AS days_employed
FROM Employees;
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🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF)
JOINS help you combine data from two or more tables based on a related column (usually a primary key and a foreign key).
1. INNER JOIN
Returns only matching rows between two tables.
SELECT customers.name, orders.order_id
FROM customers
INNER JOIN orders ON customers.id = orders.customer_id;
This returns only those customers who have placed at least one order.
2. LEFT JOIN (or LEFT OUTER JOIN)
Returns all rows from the left table, and matched rows from the right table. If no match, you'll see NULLs.
SELECT customers.name, orders.order_id
FROM customers
LEFT JOIN orders ON customers.id = orders.customer_id;
This shows all customers, including those who haven’t placed any orders.
3. RIGHT JOIN (or RIGHT OUTER JOIN)
Returns all rows from the right table, and matching rows from the left.
SELECT customers.name, orders.order_id
FROM customers
RIGHT JOIN orders ON customers.id = orders.customer_id;
You’ll see all orders — even if there’s no corresponding customer info.
4. FULL JOIN (or FULL OUTER JOIN)
Returns all rows from both tables. If there's no match, it returns NULLs.
Note: MySQL doesn't support FULL JOIN directly; use UNION of LEFT and RIGHT joins instead.
5. SELF JOIN
You join a table with itself. Great for hierarchical relationships.
SELECT e.name AS employee, m.name AS manager
FROM employees e
JOIN employees m ON e.manager_id = m.id;
This shows each employee along with their manager's name.
Pro Tip: Be careful with NULLs and always define clear join conditions to avoid cartesian products.
React with ❤️ if you're ready for the next one: 👇
📦 Subqueries & Nested Queries.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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Path 2 (More Focus on Python)
👇👇
Free Resources: https://t.me/pythonanalyst/102
Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts and tools. Focus on Python for data analysis, using libraries like Pandas and Matplotlib.
Days 4-7: Dive into SQL basics for data retrieval and manipulation. There are many free online resources and tutorials available.
Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects. Start with small datasets from sources like Kaggle or publicly available datasets. Analyze the data, create visualizations, and document your findings. Make use of Jupyter Notebooks for your projects.
Week 3: Intermediate Skills
Days 15-21: Explore more advanced topics such as data cleaning, feature engineering, and statistical analysis. Learn about more advanced visualization libraries like Seaborn and Plotly.
Days 22-23: Start a personal project that relates to your interests. This could be related to a hobby or a topic you're passionate about.
Week 4: Portfolio Completion
Days 24-28: Continue working on your personal project, applying what you've learned. Make sure your project has clear objectives, data analysis, visualizations, and conclusions.
Day 29: Create a portfolio website using platforms like GitHub Pages, where you can showcase your projects along with explanations and code.
Day 30: Write a blog post summarizing your journey and the key lessons you've learned during this intense month.
Throughout the month, engage with online communities and forums related to data analysis to seek help when needed and learn from others. Remember, building a portfolio is not just about quantity but also about the quality of your work and your ability to articulate your analysis effectively.
While this plan is intensive, it's essential to manage expectations. You may not become an expert data analyst in a month, but you can certainly create a portfolio that demonstrates your enthusiasm, dedication, and foundational skills in data analysis, which can be a valuable starting point for your career.
Hope it helps :)
Build Data Analyst Portfolio in 1 month
Path 1 (More focus on SQL & then on Python)
👇👇
Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.me/sqlanalyst/74
Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.
Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.
Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.
Hope it helps :)
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Kaggle Datasets are often too perfect for real-world scenarios.
I'm about to share a method for real-life data analysis.
You see …
… most of the time, a data analyst cleans and transforms data.
So … let’s practice that.
How?
Well … you can use ChatGPT.
Just write this prompt:
Create a downloadable CSV dataset of 10,000 rows of financial credit card transactions with 10 columns of customer data so I can perform some data analysis to segment customers.
Now…
Download the dataset and start your analysis.
You'll see that, most of the time…
… numbers don’t match.
There are no patterns.
Data is incorrect and doesn’t make sense.
And that’s good.
Now you know what a data analyst deals with.
Your job is to make sense of that dataset.
To create a story that justifies the numbers.
This is how you can mimic real-life work using A.I.
𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 😍
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All The Best 🎊
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Select your experience & Complete The Registration Process
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Data Analytics project ideas to build your portfolio in 2025:
1. Sales Data Analysis Dashboard
Analyze sales trends, seasonal patterns, and product performance.
Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.
2. Customer Segmentation
Use clustering (K-means, hierarchical) on customer data to identify groups.
Provide actionable marketing insights.
3. Social Media Sentiment Analysis
Analyze tweets or reviews using NLP to gauge public sentiment.
Visualize positive, negative, and neutral trends over time.
4. Churn Prediction Model
Analyze customer data to predict who might leave a service.
Use logistic regression, decision trees, or random forest.
5. Financial Data Analysis
Study stock prices, moving averages, and volatility.
Create an interactive dashboard with key metrics.
6. Healthcare Analytics
Analyze patient data for disease trends or hospital resource usage.
Use visualization to highlight key findings.
7. Website Traffic Analysis
Use Google Analytics data to identify user behavior patterns.
Suggest improvements for user engagement and conversion.
8. Employee Attrition Analysis
Analyze HR data to find factors leading to employee turnover.
Use statistical tests and visualization.
React ❤️ for more
𝟲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀!)😍
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👨💻 𝟓 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐍𝐞𝐞𝐝𝐬 𝐢𝐧 𝐚𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📊
🔸𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 & 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).
🔸𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.
🔸𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
It’s important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your model’s performance.
🔸𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.
🔸𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
Once you’ve built a model, it’s important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.
🔍 𝐏𝐫𝐨 𝐓𝐢𝐩: Keep practicing by working on real projects or using online platforms to improve these skills!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
Hope this helps you 😊
#ai #datascience
𝟯 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
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