This is how data analytics teams work!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
❤1👍1
𝐃𝐚𝐲 𝟏- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊
When you're working with data in Power BI, it's common for clients to request changes to column names to better suit their reporting needs or align with organizational terminology. Let's say you've loaded data from an Excel file into Power BI, and your client asks you to rename certain columns for clarity or consistency.
𝐀𝐟𝐭𝐞𝐫 𝐦𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐢𝐧 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈, 𝐲𝐨𝐮 𝐦𝐢𝐠𝐡𝐭 𝐰𝐨𝐧𝐝𝐞𝐫 𝐰𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐧𝐞𝐰 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐚𝐝𝐝𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐄𝐱𝐜𝐞𝐥 𝐟𝐢𝐥𝐞. 𝐖𝐢𝐥𝐥 𝐭𝐡𝐞 𝐫𝐞𝐟𝐫𝐞𝐬𝐡𝐞𝐝 𝐝𝐚𝐭𝐚 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬𝐥𝐲 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐢𝐧𝐭𝐨 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐝𝐞𝐬𝐩𝐢𝐭𝐞 𝐭𝐡𝐞 𝐚𝐥𝐭𝐞𝐫𝐞𝐝 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞𝐬?
The key lies in how Power BI updates data. It looks at the structure of the data source to match fields and columns when refreshing. So, if you've renamed columns in Power BI, it's okay as long as the Excel file's structure hasn't changed. Power BI will still match the new data to the right columns. 🙌
When you're working with data in Power BI, it's common for clients to request changes to column names to better suit their reporting needs or align with organizational terminology. Let's say you've loaded data from an Excel file into Power BI, and your client asks you to rename certain columns for clarity or consistency.
𝐀𝐟𝐭𝐞𝐫 𝐦𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐢𝐧 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈, 𝐲𝐨𝐮 𝐦𝐢𝐠𝐡𝐭 𝐰𝐨𝐧𝐝𝐞𝐫 𝐰𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐧𝐞𝐰 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐚𝐝𝐝𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐄𝐱𝐜𝐞𝐥 𝐟𝐢𝐥𝐞. 𝐖𝐢𝐥𝐥 𝐭𝐡𝐞 𝐫𝐞𝐟𝐫𝐞𝐬𝐡𝐞𝐝 𝐝𝐚𝐭𝐚 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬𝐥𝐲 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐢𝐧𝐭𝐨 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐝𝐞𝐬𝐩𝐢𝐭𝐞 𝐭𝐡𝐞 𝐚𝐥𝐭𝐞𝐫𝐞𝐝 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞𝐬?
The key lies in how Power BI updates data. It looks at the structure of the data source to match fields and columns when refreshing. So, if you've renamed columns in Power BI, it's okay as long as the Excel file's structure hasn't changed. Power BI will still match the new data to the right columns. 🙌
👍10❤1
𝐃𝐚𝐲 𝟐- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊
I have X table in which a column named 'Employee Class' with inputs 'Highest Level', 'Mid-Level', and 'Entry-Level'.
When I put it in a slicer, it appears in either ascending order based on the first letter (Entry-Level, Highest Level, Mid-Level) or descending order (Mid-Level, Highest Level, Entry-Level).
However, Client wants it to be shown in the order: Highest Level, Mid-Level, and Entry-Level. How can I achieve this in Power BI?
𝐒𝐨𝐥.
𝟏. Go to Data view in Power BI Desktop
𝟐. Select the table containing the "Employee Class" column.
𝟑. Create a new column (e.g., "SortOrder") with a formula to assign numerical values based on your desired order:
𝐒𝐨𝐫𝐭𝐎𝐫𝐝𝐞𝐫 =
𝐒𝐖𝐈𝐓𝐂𝐇(
'𝐗'[𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞 𝐂𝐥𝐚𝐬𝐬],
"𝐇𝐢𝐠𝐡𝐞𝐬𝐭 𝐋𝐞𝐯𝐞𝐥", 𝟏,
"𝐌𝐢𝐝-𝐋𝐞𝐯𝐞𝐥", 𝟐,
"𝐄𝐧𝐭𝐫𝐲-𝐋𝐞𝐯𝐞𝐥", 𝟑,
"𝐍𝐀"
)
𝟒. In the Data view, select the "Employee Class" column. Go to the "Modeling" tab in the ribbon. Click on "Sort by Column" and choose the "SortOrder" column.
𝟓. Insert a slicer by dragging the "Employee Class" field in Power BI Desktop.
The slicer should now display the "Employee Class" values in the order: Highest Level, Mid-Level, Entry-Level.
I have X table in which a column named 'Employee Class' with inputs 'Highest Level', 'Mid-Level', and 'Entry-Level'.
When I put it in a slicer, it appears in either ascending order based on the first letter (Entry-Level, Highest Level, Mid-Level) or descending order (Mid-Level, Highest Level, Entry-Level).
However, Client wants it to be shown in the order: Highest Level, Mid-Level, and Entry-Level. How can I achieve this in Power BI?
𝐒𝐨𝐥.
𝟏. Go to Data view in Power BI Desktop
𝟐. Select the table containing the "Employee Class" column.
𝟑. Create a new column (e.g., "SortOrder") with a formula to assign numerical values based on your desired order:
𝐒𝐨𝐫𝐭𝐎𝐫𝐝𝐞𝐫 =
𝐒𝐖𝐈𝐓𝐂𝐇(
'𝐗'[𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞 𝐂𝐥𝐚𝐬𝐬],
"𝐇𝐢𝐠𝐡𝐞𝐬𝐭 𝐋𝐞𝐯𝐞𝐥", 𝟏,
"𝐌𝐢𝐝-𝐋𝐞𝐯𝐞𝐥", 𝟐,
"𝐄𝐧𝐭𝐫𝐲-𝐋𝐞𝐯𝐞𝐥", 𝟑,
"𝐍𝐀"
)
𝟒. In the Data view, select the "Employee Class" column. Go to the "Modeling" tab in the ribbon. Click on "Sort by Column" and choose the "SortOrder" column.
𝟓. Insert a slicer by dragging the "Employee Class" field in Power BI Desktop.
The slicer should now display the "Employee Class" values in the order: Highest Level, Mid-Level, Entry-Level.
👍7❤1
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
I’ve launched a new YouTube playlist dedicated to teaching statistics from the ground up. This series covers fundamental concepts in a simple and structured way, making it perfect for beginners looking to build a strong foundation in statistics.
Watch the playlist here: Statistics from Basics – YouTube- https://www.youtube.com/playlist?list=PLEt4gT_dNBRGCn0tsZpd14uA2q3s64ekq
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
I’ve launched a new YouTube playlist dedicated to teaching statistics from the ground up. This series covers fundamental concepts in a simple and structured way, making it perfect for beginners looking to build a strong foundation in statistics.
Watch the playlist here: Statistics from Basics – YouTube- https://www.youtube.com/playlist?list=PLEt4gT_dNBRGCn0tsZpd14uA2q3s64ekq
YouTube
Statistics complete playlist
Statistics complete videos
👍4❤1
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
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
👍7❤1
Most interview guides are outdated. Here's what real data science interviews in 2025 are asking — and why they’re NOT on your prep site. Save this before your next technical round!
❤1👍1
🎉 New Launch: Data Analysis with Python – Recorded Course 🎉
Ready to master Data Analysis with Python from the comfort of your home?
📦 What's Included in the Full Course (₹2000):
✅ 1-on-1 Doubt Solving Sessions (5 sessions × 15 mins at your flexible timings)
✅ Comprehensive Learning Material (PDF notes, Assignments, and .ipynb files for every session)
✅ Lifetime Access to Course Recordings
💡 Only Need the Learning Material & Recordings?
Get the Self-Learning Package for just ₹1299
✅ PDF Notes + Assignments + .ipynb Files
✅ Lifetime Access to All Recordings
📌 No deadlines. No pressure. Learn at your own pace with expert support!
Ping me on whatsapp +91-9910986344
Ready to master Data Analysis with Python from the comfort of your home?
📦 What's Included in the Full Course (₹2000):
✅ 1-on-1 Doubt Solving Sessions (5 sessions × 15 mins at your flexible timings)
✅ Comprehensive Learning Material (PDF notes, Assignments, and .ipynb files for every session)
✅ Lifetime Access to Course Recordings
💡 Only Need the Learning Material & Recordings?
Get the Self-Learning Package for just ₹1299
✅ PDF Notes + Assignments + .ipynb Files
✅ Lifetime Access to All Recordings
📌 No deadlines. No pressure. Learn at your own pace with expert support!
Ping me on whatsapp +91-9910986344
🚨 Join Our Discord Community! 🎓📊
Hey #CodingFam! I'm super excited to invite you to new Discord server – made just for YOU 🔥
If you're a student, job seeker, or self-learner trying to build a career in Data Science, Python, SQL, Power BI, or ML — this is the ultimate space you’ve been waiting for 💻💡
🧠 What’s Inside?
✅ Topic-wise roadmaps (Python, ML, SQL, etc.)
✅ Daily goals & learning challenges
✅ Project ideas & resume boosters
✅ Notes, resources, and YouTube playlists
✅ Real-time help & doubt-solving
✅ Career guidance + Interview prep
💬 Why Discord?
Because learning shouldn't feel lonely!
With Discord, we can:
👉 Interact instantly in focused channels
👉 Stay updated through announcements
👉 Ask & answer doubts in real time
👉 Build a support system with learners like you
👉 Get exclusive tips, content drops & live session alerts
🎯 Whether you're just starting out or already in the game, this community will help you stay consistent, stay motivated, and level up your skills — together 💪
🔗 Click to join: https://discord.gg/khBWeH5T
(It's FREE & beginner-friendly!)
Let’s build, learn, and grow together 👩💻👨💻
See you on the server! 💙
Hey #CodingFam! I'm super excited to invite you to new Discord server – made just for YOU 🔥
If you're a student, job seeker, or self-learner trying to build a career in Data Science, Python, SQL, Power BI, or ML — this is the ultimate space you’ve been waiting for 💻💡
🧠 What’s Inside?
✅ Topic-wise roadmaps (Python, ML, SQL, etc.)
✅ Daily goals & learning challenges
✅ Project ideas & resume boosters
✅ Notes, resources, and YouTube playlists
✅ Real-time help & doubt-solving
✅ Career guidance + Interview prep
💬 Why Discord?
Because learning shouldn't feel lonely!
With Discord, we can:
👉 Interact instantly in focused channels
👉 Stay updated through announcements
👉 Ask & answer doubts in real time
👉 Build a support system with learners like you
👉 Get exclusive tips, content drops & live session alerts
🎯 Whether you're just starting out or already in the game, this community will help you stay consistent, stay motivated, and level up your skills — together 💪
🔗 Click to join: https://discord.gg/khBWeH5T
(It's FREE & beginner-friendly!)
Let’s build, learn, and grow together 👩💻👨💻
See you on the server! 💙
Discord
Join the Codingdidi Discord Server!
Check out the Codingdidi community on Discord - hang out with 32 other members and enjoy free voice and text chat.
❤5
What’s the biggest challenge you face in learning AI/ML?
Anonymous Poll
33%
Understanding math behind it
24%
Lack of projects/practice
28%
Too many resources, don’t know where to start
7%
Time management
9%
Staying consistent
❤3
https://youtu.be/Bd6EeCdDRiw -- Github in one complete video
YouTube
Git & GitHub Full Course for Beginners (2025) 🚀 | Master GitHub, Repos, SSH, Pull Requests & More!
GitHub for Beginners to Pro: Complete Guide in One Video
Are you new to Git or GitHub and feeling overwhelmed? This beginner-friendly Git & GitHub full course (2025 edition) is all you need to go from zero to confident! 💻🚀
In this comprehensive video, you'll…
Are you new to Git or GitHub and feeling overwhelmed? This beginner-friendly Git & GitHub full course (2025 edition) is all you need to go from zero to confident! 💻🚀
In this comprehensive video, you'll…