Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Відкрити в Telegram
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
Показати більше
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 39 602 підписників, посідаючи 4 707 місце в категорії Освіта та 9 741 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 39 602 підписників.
За останніми даними від 27 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -39, а за останні 24 години на -1, 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 0.69% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 880 переглядів. Протягом першої доби публікація в середньому набирає 274 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 28 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.
39 602
Підписники
-124 години
-527 днів
-3930 днів
Архів дописів
📢 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗔𝗹𝗲𝗿𝘁 – Data Analytics with Artificial Intelligence
Upgrade your career with AI-powered data science skills.
Open for all. No Coding Background Required
📊 Learn Data Analytics with Artificial Intelligence from Scratch
🤖 AI Tools & Automation
📈 Build real world Projects for job ready portfolio
🎓 E&ICT IIT Roorkee Certification Program
🔥Deadline :- 22nd March
𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 👇 :-
https://pdlink.in/4tkErvS
Don't Miss This Opportunity. Get Placement Assistance With 5000+ Companies
Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume
📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)
🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)
🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)
📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)
🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)
📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)
🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.
Join for more: https://t.me/DataPortfolio
Hope this piece of information helps you
Data Analyst Interview Questions 👇
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
𝗙𝗿𝗲𝘀𝗵𝗲𝗿𝘀 𝗖𝗮𝗻 𝗚𝗲𝘁 𝗮 𝟯𝟬 𝗟𝗣𝗔 𝗝𝗼𝗯 𝗢𝗳𝗳𝗲𝗿 𝘄𝗶𝘁𝗵 𝗔𝗜 & 𝗗𝗦 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻😍
IIT Roorkee offering AI & Data Science Certification Program
💫Learn from IIT ROORKEE Professors
✅ Students & Fresher can apply
🎓 IIT Certification Program
💼 5000+ Companies Placement Support
Deadline: 22nd March 2026
📌 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-
https://pdlink.in/4kucM7E
Big Opportunity, Do join asap!
✅ Complete Data Analyst Interview Roadmap – What You MUST Know 📊💼
🔰 1. Data Analysis Fundamentals:
• Statistical Concepts: Mean, median, mode, standard deviation, variance, distributions (normal, binomial), hypothesis testing.
• Experimental Design: A/B testing, control groups, statistical significance.
• Data Visualization Principles: Choosing the right chart type, effective dashboard design, data storytelling.
📚 2. Technical Skills Mastery:
• SQL:
• SELECT, FROM, WHERE clauses
• JOINs (INNER, LEFT, RIGHT, FULL OUTER)
• Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
• GROUP BY and HAVING
• Window functions (RANK, ROW_NUMBER)
• Subqueries
• Excel:
• Pivot tables
• VLOOKUP, INDEX/MATCH
• Conditional formatting
• Data validation
• Charts and graphs
• Data Visualization Tools (choose at least one):
• Tableau
• Power BI
• Programming (Python or R - optional but highly valued):
• Data manipulation with Pandas (Python) or dplyr (R)
• Data visualization with Matplotlib, Seaborn (Python) or ggplot2 (R)
⚙️ 3. Data Wrangling and Cleaning:
• Handling Missing Data: Imputation techniques
• Data Transformation: Normalization, scaling
• Outlier Detection and Treatment
• Data Type Conversion
• Data Validation Techniques
💬 4. Problem-Solving Practice:
• Case Studies: Practice solving real-world business problems using data.
• Examples: Customer churn analysis, sales trend forecasting, marketing campaign optimization.
• Estimation Questions: Practice making reasonable estimates when data is limited.
💡 5. Business Acumen:
• Understand key business metrics (e.g., revenue, profit, customer lifetime value).
• Be able to connect data insights to business outcomes.
• Demonstrate an understanding of the industry you're interviewing for.
🧠 6. Communication Skills:
• Be able to clearly and concisely explain your findings to both technical and non-technical audiences.
• Practice presenting data in a visually compelling way.
• Be prepared to answer behavioral questions about your teamwork and problem-solving abilities.
📝 7. Resume and Portfolio:
• Highlight relevant skills and experience.
• Showcase your projects with clear descriptions and quantifiable results.
• Include links to your GitHub, Tableau Public profile, or personal website.
🔄 8. Mock Interviews and Feedback:
• Practice with friends, mentors, or online platforms.
• Focus on both technical proficiency and communication skills.
• Seek feedback on your approach and presentation.
🎯 Tips:
• Focus on demonstrating your ability to solve real-world business problems with data.
• Be prepared to explain your thought process and justify your choices.
• Show enthusiasm for data and a desire to learn.
👍 Tap ❤️ if you found this helpful!
𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 😍
Choose the Right Career Path in 2026
Learn → Level Up → Get Hired
🎯 Join this FREE Career Guidance Session & find:
✔ The right tech career for YOU
✔ Skills companies are hiring for
✔ Step-by-step roadmap to get a job
👇 𝗦𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘀𝗽𝗼𝘁 𝗻𝗼𝘄 (𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝘀𝗲𝗮𝘁𝘀)
https://pdlink.in/4sNAyhW
Date & Time :- 18th March 2026 , 7:00 PM
✅ How to Build a Job-Ready Data Analytics Portfolio 💼📊
1️⃣ Pick Solid Datasets
• Public: Kaggle, UCI ML Repo, data.gov
• Business-like: e-commerce, churn, marketing spend, HR attrition
• Size: 5k–200k rows, relatively clean
2️⃣ Create 3 Signature Projects
• SQL: Customer Cohort & Retention (joins, window functions)
• BI: Executive Sales Dashboard (Power BI/Tableau, drill-through, DAX/calculated fields)
• Python: Marketing ROI & Attribution (pandas, seaborn, A/B test basics)
3️⃣ Tell a Story, Not Just Charts
• Problem → Approach → Insight → Action
• Add one business recommendation per insight
4️⃣ Document Like a Pro
• README: problem, data source, methods, results, next steps
• Screenshots or GIFs of dashboards
• Repo structure: /data, /notebooks, /sql, /reports
5️⃣ Show Measurable Impact
• “Reduced reporting time by 70% with automated Power BI pipeline”
• “Identified 12% churn segment with a retention playbook”
6️⃣ Make It Easy to Review
• Share live dashboards (Publish to Web), short Loom/YouTube walkthrough
• Include SQL snippets
• Pin top 3 projects on GitHub and LinkedIn Featured
7️⃣ Iterate With Feedback
• Post drafts on LinkedIn, ask “What would you improve?”
• Apply suggestions, track updates in a CHANGELOG
🎯 Goal: 3 projects, 3 stories, 3 measurable outcomes.
💬 Double Tap ❤️ For More!
🚀 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲?
Tech companies are hiring developers with React, JavaScript, Node.js & MongoDB skills.
This Full Stack Development Program helps you learn everything from scratch with real projects.
💡 Perfect for:
* Beginners
* Students
* Career switchers
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇:-
https://pdlink.in/4hO7rWY
⚡ Don’t miss this chance to enter the high-paying tech industry!
✅8-Week Beginner Roadmap to Learn Data Analysis 📊
🗓️ Week 1: Excel & Data Basics
Goal: Master data organization and analysis basics
Topics: Excel formulas, functions, PivotTables, data cleaning
Tools: Microsoft Excel, Google Sheets
Mini Project: Analyze sales or survey data with PivotTables
🗓️ Week 2: SQL Fundamentals
Goal: Learn to query databases efficiently
Topics: SELECT, WHERE, JOIN, GROUP BY, subqueries
Tools: MySQL, PostgreSQL, SQLite
Mini Project: Query sample customer or sales database
🗓️ Week 3: Data Visualization Basics
Goal: Create meaningful charts and graphs
Topics: Bar charts, line charts, scatter plots, dashboards
Tools: Tableau, Power BI, Excel charts
Mini Project: Build dashboard to analyze sales trends
🗓️ Week 4: Data Cleaning & Preparation
Goal: Handle messy data for analysis
Topics: Handling missing values, duplicates, data types
Tools: Excel, Python (Pandas) basics
Mini Project: Clean and prepare real-world dataset for analysis
🗓️ Week 5: Statistics for Data Analysis
Goal: Understand key statistical concepts
Topics: Descriptive stats, distributions, correlation, hypothesis testing
Tools: Excel, Python (SciPy, NumPy)
Mini Project: Analyze survey data & draw insights
🗓️ Week 6: Advanced SQL & Database Concepts
Goal: Optimize queries & explore database design basics
Topics: Window functions, indexes, normalization
Tools: SQL Server, MySQL
Mini Project: Complex query for sales and customer analysis
🗓️ Week 7: Automating Analysis with Python
Goal: Use Python for repetitive data tasks
Topics: Pandas automation, data aggregation, visualization scripting
Tools: Jupyter Notebook, Pandas, Matplotlib
Mini Project: Automate monthly sales report generation
🗓️ Week 8: Capstone Project + Reporting
Goal: End-to-end analysis and presentation
Project Ideas: Customer segmentation, sales forecasting, churn analysis
Tools: Tableau/Power BI for visualization + Python/SQL for backend
Bonus: Present findings in a polished report or dashboard
💡 Tips:
⦁ Practice querying and analysis on public datasets (Kaggle, data.gov)
⦁ Join data challenges and community projects
💬 Tap ❤️ for the detailed explanation of each topic!
🤖 𝗔𝗜 + 𝗗𝗮𝘁𝗮 = 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗝𝗼𝗯𝘀
Start your journey in Data Analytics & Data Science with AI Certification and gain skills companies are actively hiring for.
📊 Data Analysis
🐍 Python Programming
🤖 Machine Learning
📈 AI-Driven Insights
🔥 Perfect for College Students ,Freshers & Professionals
1️⃣𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3OD9jI1
2️⃣𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 :- https://pdlink.in/4kucM7E
3️⃣𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4ay4wPG
4️⃣𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/3ZtIZm9
5️⃣𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 :- https://pdlink.in/4rMivIA
Don't Miss This Opportunity . Get Placement Assistance With 5000+ Companies
📈 Want to Excel at Data Analytics? Master These Essential Skills! ☑️
Core Concepts:
• Statistics & Probability – Understand distributions, hypothesis testing
• Excel – Pivot tables, formulas, dashboards
Programming:
• Python – NumPy, Pandas, Matplotlib, Seaborn
• R – Data analysis & visualization
• SQL – Joins, filtering, aggregation
Data Cleaning & Wrangling:
• Handle missing values, duplicates
• Normalize and transform data
Visualization:
• Power BI, Tableau – Dashboards
• Plotly, Seaborn – Python visualizations
• Data Storytelling – Present insights clearly
Advanced Analytics:
• Regression, Classification, Clustering
• Time Series Forecasting
• A/B Testing & Hypothesis Testing
ETL & Automation:
• Web Scraping – BeautifulSoup, Scrapy
• APIs – Fetch and process real-world data
• Build ETL Pipelines
Tools & Deployment:
• Jupyter Notebook / Colab
• Git & GitHub
• Cloud Platforms – AWS, GCP, Azure
• Google BigQuery, Snowflake
Hope it helps :)
💻 𝗙𝗥𝗘𝗘 𝗘𝘅𝗰𝗲𝗹 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 – 𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗕𝗮𝘀𝗶𝗰𝘀
Still using Excel only for simple tables?
Learn how professionals use Excel for data analysis, insights & reporting.
✔ Real business use cases
✔ Must-know Excel formulas
✔ Data cleaning & analysis
✔ Career guidance
📅 13 March | ⏰ 6 PM
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :-
https://pdlink.in/4bEDmIw
🚀 Upgrade your Excel skills today!
📘 SQL Challenges for Data Analytics – With Explanation 🧠
(Beginner ➡️ Advanced)
1️⃣ Select Specific Columns
SELECT name, email FROM users;name and email columns from the users table.
✔️ Used when you don’t want all columns from a table.
2️⃣ Filter Records with WHERE
SELECT * FROM users WHERE age > 30;WHERE clause filters rows where age is greater than 30.
✔️ Used for applying conditions on data.
3️⃣ ORDER BY Clause
SELECT * FROM users ORDER BY registered_at DESC;registered_at in descending order.
✔️ Helpful to get latest data first.
4️⃣ Aggregate Functions (COUNT, AVG)
SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;COUNT(*) counts total rows (users).
- AVG(age) calculates the average age.
✔️ Used for quick stats from tables.
5️⃣ GROUP BY Usage
SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;city and counts users in each group.
✔️ Use when you want grouped summaries.
6️⃣ JOIN Tables
SELECT users.name, orders.amount
FROM users
JOIN orders ON users.id = orders.user_id;users and orders on matching IDs.
✔️ Essential when combining data from multiple tables.
7️⃣ Use of HAVING
SELECT city, COUNT(*) AS total
FROM users
GROUP BY city
HAVING COUNT(*) > 5;WHERE, but used with aggregates. This filters cities with more than 5 users.
✔️ **Use HAVING after GROUP BY.**
8️⃣ Subqueries
SELECT * FROM users
WHERE salary > (SELECT AVG(salary) FROM users);SELECT name,
CASE
WHEN age < 18 THEN 'Teen'
WHEN age <= 40 THEN 'Adult'
ELSE 'Senior'
END AS age_group
FROM users;SELECT name, city, score,
RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank
FROM users;
📢 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗔𝗹𝗲𝗿𝘁 – 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗪𝗶𝘁𝗵 𝗔𝗜
Upgrade your career with AI-powered data analytics skills.
📊 Learn Data Analytics from Scratch
🤖 AI Tools & Automation
📈 Data Visualization & Insights
🎓 Certification Program
🔥 Highly demanded skill in today’s job market.
𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-
https://pdlink.in/4syEItX
🚀 Perfect for Students ,Freshers & Working Professionals
🔥 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Upgrade your career with one of the most in-demand tech skills of 2026!
✔ Artificial Intelligence
✔ Machine Learning
✔ Python for Data Science
✔ Real-World Projects
🎓 Get Certified & Build Your Tech Career
𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-
https://pdlink.in/4qHVFkI
🚀 Perfect for Students ,Freshers & Working Professionals
𝗗𝗲𝘃𝗢𝗽𝘀 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀😍
- Bridge the Gap Between Your Current Skills and What DevOps Roles Demand
- Know The Roadmap To Become DevOps Engineer In 2026
Eligibility :- Students ,Freshers & Working Professionals
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :-
https://pdlink.in/40YmeqV
( Limited Slots ..Hurry Up🏃♂️ )
Date & Time :- March 10 , 2026 , 7:00 PM
✅ 🔤 A–Z of Data Analyst Terms 📊💻🚀
A – A/B Testing
Experiment comparing two versions to see which performs better.
B – Business Intelligence (BI)
Technologies and processes for analyzing business data.
C – Correlation
Measure of relationship between two variables.
D – Data Cleaning
Process of fixing or removing incorrect/incomplete data.
E – ETL (Extract, Transform, Load)
Process of moving and preparing data for analysis.
F – Forecasting
Predicting future trends based on historical data.
G – Granularity
Level of detail in data (daily, monthly, yearly).
H – Hypothesis
Assumption made for testing using data.
I – Insight
Meaningful interpretation derived from data analysis.
J – Join
Combining data from multiple tables.
K – KPI (Key Performance Indicator)
Metric used to measure performance.
L – Linear Regression
Statistical method to model relationship between variables.
M – Metrics
Quantifiable measures used to track performance.
N – Normalization
Organizing data to reduce redundancy.
O – Outlier
Data point significantly different from others.
P – Pivot Table
Tool to summarize and analyze data.
Q – Query
Request to retrieve specific data.
R – Regression Analysis
Technique for predicting relationships between variables.
S – Segmentation
Dividing data into groups for analysis.
T – Trend Analysis
Identifying patterns over time.
U – Unstructured Data
Data without predefined format (text, images).
V – Visualization
Presenting data graphically (charts, dashboards).
W – Warehouse (Data Warehouse)
Central repository for integrated data.
X – X-Axis
Horizontal axis in charts.
Y – YoY (Year-over-Year)
Comparison of metrics from one year to another.
Z – Z-Score
Statistical measurement of how far a value is from mean.
❤️ Double Tap for More
🚀 Scale Google Ads With Agency Billing
Run and scale your Google Ads campaigns with flexible agency advertising solutions.
Perfect for advertisers, affiliates and media buyers.
✔️ Discounted advertising solutions
✔️ Monthly invoicing available
✔️ Suitable for scaling campaigns
✔️ Long-term cooperation
⚡ Limited onboarding for new advertisers.
📞 Contact: https://t.me/adscredit
📢 Channel: https://t.me/googleadscredits
🚀𝗚𝗲𝘁 𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗜𝗜𝗧's & 𝗜𝗜𝗠
Dreaming of studying at an IIT and building a career in AI ? This is your chance
✅ Prestigious IIT Certification
✅ Learn directly from IIT Professors
✅ Placement Assistance with 5000+ Companies
💡 Today’s top companies are actively looking for professionals with AI skills.
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-
𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 :- https://pdlink.in/4kucM7E
𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 :- https://pdlink.in/4rMivIA
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗪𝗶𝘁𝗵 𝗔𝗜 :- https://pdlink.in/4ay4wPG
⏳ Limited seats – Register before the link expires!
✅ Useful Platform to Practice SQL Programming 🧠🖥️
Learning SQL is just the first step — practice is what builds real skill. Here are the best platforms for hands-on SQL:
1️⃣ LeetCode – For Interview-Oriented SQL Practice
• Focus: Real interview-style problems
• Levels: Easy to Hard
• Schema + Sample Data Provided
• Great for: Data Analyst, Data Engineer, FAANG roles
✔ Tip: Start with Easy → filter by “Database” tag
✔ Popular Section: Database → Top 50 SQL Questions
Example Problem: “Find duplicate emails in a user table” → Practice filtering, GROUP BY, HAVING
2️⃣ HackerRank – Structured & Beginner-Friendly
• Focus: Step-by-step SQL track
• Has certification tests (SQL Basic, Intermediate)
• Problem sets by topic: SELECT, JOINs, Aggregations, etc.
✔ Tip: Follow the full SQL track
✔ Bonus: Company-specific challenges
Try: “Revising Aggregations – The Count Function” → Build confidence with small wins
3️⃣ Mode Analytics – Real-World SQL in Business Context
• Focus: Business intelligence + SQL
• Uses real-world datasets (e.g., e-commerce, finance)
• Has an in-browser SQL editor with live data
✔ Best for: Practicing dashboard-level queries
✔ Tip: Try the SQL case studies & tutorials
4️⃣ StrataScratch – Interview Questions from Real Companies
• 500+ problems from companies like Uber, Netflix, Google
• Split by company, difficulty, and topic
✔ Best for: Intermediate to advanced level
✔ Tip: Try “Hard” questions after doing 30–50 easy/medium
5️⃣ DataLemur – Short, Practical SQL Problems
• Crisp and to the point
• Good UI, fast learning
• Real interview-style logic
✔ Use when: You want fast, smart SQL drills
📌 How to Practice Effectively:
• Spend 20–30 mins/day
• Focus on JOINs, GROUP BY, HAVING, Subqueries
• Analyze problem → write → debug → re-write
• After solving, explain your logic out loud
🧪 Practice Task:
Try solving 5 SQL questions from LeetCode or HackerRank this week. Start with SELECT, WHERE, and GROUP BY.
💬 Tap ❤️ for more!
