To be GOOD in Data Science you need to learn:

- Python
- SQL
- PowerBI

To be GREAT in Data Science you need to add:

- Business Understanding
- Knowledge of Cloud
- Many-many projects

But to LAND a job in Data Science you need to prove you can:

- Learn new things
- Communicate clearly
- Solve problems

#DataScience #LearnPython

𝗧𝗵𝗲 𝟰 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝗯 (𝗘𝘃𝗲𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲) 💼

Recruiters don’t want to see more certificates—they want proof you can solve real-world problems. That’s where the right projects come in. Not toy datasets, but projects that demonstrate storytelling, problem-solving, and impact.

Here are 4 killer projects that’ll make your portfolio stand out 👇

🔹 1. Exploratory Data Analysis (EDA) on Real-World Dataset

Pick a messy dataset from Kaggle or public sources. Show your thought process.

✅ Clean data using Pandas
✅ Visualize trends with Seaborn/Matplotlib
✅ Share actionable insights with graphs and markdown

Bonus: Turn it into a Jupyter Notebook with detailed storytelling

🔹 2. Predictive Modeling with ML

Solve a real problem using machine learning. For example:

✅ Predict customer churn using Logistic Regression
✅ Predict housing prices with Random Forest or XGBoost
✅ Use scikit-learn for training + evaluation

Bonus: Add SHAP or feature importance to explain predictions

🔹 3. SQL-Powered Business Dashboard

Use real sales or ecommerce data to build a dashboard.

✅ Write complex SQL queries for KPIs
✅ Visualize with Power BI or Tableau
✅ Show trends: Revenue by Region, Product Performance, etc.

Bonus: Add filters & slicers to make it interactive

🔹 4. End-to-End Data Science Pipeline Project

Build a complete pipeline from scratch.

✅ Collect data via web scraping (e.g., IMDb, LinkedIn Jobs)
✅ Clean + Analyze + Model + Deploy
✅ Deploy with Streamlit/Flask + GitHub + Render

Bonus: Add a blog post or LinkedIn write-up explaining your approach

🎯 One solid project > 10 certificates.

Make it visible. Make it valuable. Share it confidently.

Here's link to download the detailed pdf: https://topmate.io/codingdidi/1529351

Some interview questions related to Data science

1- what is difference between structured data and unstructured data.

2- what is multicollinearity.and how to remove them

3- which algorithms you use to find the most correlated features in the datasets.

4- define entropy

5- what is the workflow of principal component analysis

6- what are the applications of principal component analysis not with respect to dimensionality reduction

7- what is the Convolutional neural network. Explain me its working

Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:

1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.

2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.

3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.

4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.

5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.

6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.

7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.

Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!

Pandas Notes: https://topmate.io/codingdidi/1044154

Python Notes: https://topmate.io/codingdidi/1241233

Happy Learning!!

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!

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. 🙌

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.

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

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

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50 OOPS Interview questions

🚨 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! 💙

https://youtu.be/Bd6EeCdDRiw -- Github in one complete video

How to build a Data Science portfolio that truly stands out?