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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
Show more๐ Analytical overview of Telegram channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) in the English language segment is an active participant. Currently, the community unites 39 490 subscribers, ranking 4 752 in the Education category and 10 399 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 39 490 subscribers.
According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 197 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.73%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 079 views. Within the first day, a publication typically gains 400 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as analytic, dataset, visualization, sql, learning.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โCovering 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_dataโ
Thanks to the high frequency of updates (latest data received on 10 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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SQL can be simpleโif you learn it the smart way..
If youโre aiming to become a data analyst, mastering SQL is non-negotiable.
Hereโs a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! ๐ช
Here you can find essential SQL Interview Resources๐
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Like this post if you need more ๐โค๏ธ
Hope it helps :)
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๏ธ
Must-Know SQL Functions
Guys, today weโre diving into some essential SQL functions that can make your queries powerful and efficient!
1๏ธโฃ 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;
2๏ธโฃ 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;
3๏ธโฃ 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;
#SQL #LearnSQL #DataSkills
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๐ 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 havenโt 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;
Youโll see all orders โ even if thereโs 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 โค๏ธ if you're ready for the next one: ๐
๐ฆ Subqueries & Nested Queries.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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Path 2 (More Focus on Python)
๐๐
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)
๐๐
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 โฆ
โฆ most of the time, a data analyst cleans and transforms data.
So โฆ letโs practice that.
How?
Well โฆ 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.
Nowโฆ
Download the dataset and start your analysis.
You'll see that, most of the timeโฆ
โฆ numbers donโt match.
There are no patterns.
Data is incorrect and doesnโt make sense.
And thatโs 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.
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๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐
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All The Best ๐
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