Roadmap to become a data analyst
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
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If you want to Excel at using the most used database language in the world, learn these powerful SQL features:
• Wildcards (%, _) – Flexible pattern matching
• Window Functions – ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• Common Table Expressions (CTEs) – WITH for better readability
• Recursive Queries – Handle hierarchical data
• STRING Functions – LEFT(), RIGHT(), LEN(), TRIM(), UPPER(), LOWER()
• Date Functions – DATEDIFF(), DATEADD(), FORMAT()
• Pivot & Unpivot – Transform row data into columns
• Aggregate Functions – SUM(), AVG(), COUNT(), MIN(), MAX()
• Joins & Self Joins – Master INNER, LEFT, RIGHT, FULL, SELF JOIN
• Indexing – Speed up queries with CREATE INDEX
Like it if you need a complete tutorial on all these topics! 👍❤️
#sql
• Wildcards (%, _) – Flexible pattern matching
• Window Functions – ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• Common Table Expressions (CTEs) – WITH for better readability
• Recursive Queries – Handle hierarchical data
• STRING Functions – LEFT(), RIGHT(), LEN(), TRIM(), UPPER(), LOWER()
• Date Functions – DATEDIFF(), DATEADD(), FORMAT()
• Pivot & Unpivot – Transform row data into columns
• Aggregate Functions – SUM(), AVG(), COUNT(), MIN(), MAX()
• Joins & Self Joins – Master INNER, LEFT, RIGHT, FULL, SELF JOIN
• Indexing – Speed up queries with CREATE INDEX
Like it if you need a complete tutorial on all these topics! 👍❤️
#sql
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How to Become a Data Analyst from Scratch! 🚀
Whether you're starting fresh or upskilling, here's your roadmap:
➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required
Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope it helps :)
Whether you're starting fresh or upskilling, here's your roadmap:
➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required
Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope it helps :)
👍13❤5🥰1🎉1
SQL best practices:
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
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Common Requirements for data analyst role 👇
👉 Must be proficient in writing complex SQL Queries.
👉 Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
👉 Connecting data sources, importing data, and transforming data for Business intelligence.
👉 Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView
👉 Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.
Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.
*Here are some essential WhatsApp Channels with important resources:*
❯ Jobs ➟ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
❯ SQL ➟ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
❯ Power BI ➟ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❯ Data Analysts ➟ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
❯ Python ➟ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.
Like this post if you want me to start the interview series 👍❤️
Hope it helps :)
👉 Must be proficient in writing complex SQL Queries.
👉 Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
👉 Connecting data sources, importing data, and transforming data for Business intelligence.
👉 Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView
👉 Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.
Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.
*Here are some essential WhatsApp Channels with important resources:*
❯ Jobs ➟ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
❯ SQL ➟ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
❯ Power BI ➟ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❯ Data Analysts ➟ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
❯ Python ➟ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.
Like this post if you want me to start the interview series 👍❤️
Hope it helps :)
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🔍 Real-World Data Analyst Tasks & How to Solve Them
As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!
📌 Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
✅ Solution (Using Pandas in Python):
💡 Tip: Always check for inconsistent spellings and incorrect date formats!
📌 Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
✅ Solution (Using SQL):
💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!
📌 Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
✅ Solution (Using Power BI / Tableau):
👉 Add KPI Cards to show total sales & profit
👉 Use a Line Chart for monthly trends
👉 Create a Bar Chart for top-selling products
👉 Use Filters/Slicers for better interactivity
💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!
📌 Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
✅ Solution (Using Pandas in Python):
import pandas as pd
df = pd.read_csv('sales_data.csv')
df.drop_duplicates(inplace=True) # Remove duplicate rows
df.fillna(0, inplace=True) # Fill missing values with 0
print(df.head())
💡 Tip: Always check for inconsistent spellings and incorrect date formats!
📌 Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
✅ Solution (Using SQL):
SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue
FROM Sales
GROUP BY MONTH(SaleDate)
ORDER BY Total_Revenue DESC;
💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!
📌 Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
✅ Solution (Using Power BI / Tableau):
👉 Add KPI Cards to show total sales & profit
👉 Use a Line Chart for monthly trends
👉 Create a Bar Chart for top-selling products
👉 Use Filters/Slicers for better interactivity
💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Must-Know Power BI Charts & When to Use Them
1. Bar/Column Chart
Use for: Comparing values across categories
Example: Sales by region, revenue by product
2. Line Chart
Use for: Trends over time
Example: Monthly website visits, stock price over years
3. Pie/Donut Chart
Use for: Showing proportions of a whole
Example: Market share by brand, budget distribution
4. Table/Matrix
Use for: Detailed data display with multiple dimensions
Example: Sales by product and month, performance by employee and region
5. Card/KPI
Use for: Displaying single important metrics
Example: Total Revenue, Current Month’s Profit
6. Area Chart
Use for: Showing cumulative trends
Example: Cumulative sales over time
7. Stacked Bar/Column Chart
Use for: Comparing total and subcategories
Example: Sales by region and product category
8. Clustered Bar/Column Chart
Use for: Comparing multiple series side-by-side
Example: Revenue and Profit by product
9. Waterfall Chart
Use for: Visualizing increment/decrement over a value
Example: Profit breakdown – revenue, costs, taxes
10. Scatter Chart
Use for: Relationship between two numerical values
Example: Marketing spend vs revenue, age vs income
11. Funnel Chart
Use for: Showing steps in a process
Example: Sales pipeline, user conversion funnel
12. Treemap
Use for: Hierarchical data in a nested format
Example: Sales by category and sub-category
13. Gauge Chart
Use for: Progress toward a goal
Example: % of sales target achieved
Hope it helps :)
#powerbi
1. Bar/Column Chart
Use for: Comparing values across categories
Example: Sales by region, revenue by product
2. Line Chart
Use for: Trends over time
Example: Monthly website visits, stock price over years
3. Pie/Donut Chart
Use for: Showing proportions of a whole
Example: Market share by brand, budget distribution
4. Table/Matrix
Use for: Detailed data display with multiple dimensions
Example: Sales by product and month, performance by employee and region
5. Card/KPI
Use for: Displaying single important metrics
Example: Total Revenue, Current Month’s Profit
6. Area Chart
Use for: Showing cumulative trends
Example: Cumulative sales over time
7. Stacked Bar/Column Chart
Use for: Comparing total and subcategories
Example: Sales by region and product category
8. Clustered Bar/Column Chart
Use for: Comparing multiple series side-by-side
Example: Revenue and Profit by product
9. Waterfall Chart
Use for: Visualizing increment/decrement over a value
Example: Profit breakdown – revenue, costs, taxes
10. Scatter Chart
Use for: Relationship between two numerical values
Example: Marketing spend vs revenue, age vs income
11. Funnel Chart
Use for: Showing steps in a process
Example: Sales pipeline, user conversion funnel
12. Treemap
Use for: Hierarchical data in a nested format
Example: Sales by category and sub-category
13. Gauge Chart
Use for: Progress toward a goal
Example: % of sales target achieved
Hope it helps :)
#powerbi
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7 Must-Have Tools for Data Analysts in 2025:
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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