Hurry up and enroll within Navratri offer and get low prices! 🫶🏻😍

WhatsApp at +91-9910986344

Hey everyone!

It's my dad's birthday today! 🎉🎂

To celebrate, I'm offering a huge discount on my live python course—now just ₹3000!

But hurry, this offer is only valid for the next 24 hours! ⏳

Grab your spot now! 🚀


Ping me on WhatsApp at +91-9910986344
Before the offer ends 😍🥳

The most popular programming languages:

1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala

According to the Latest GitHub Repositories

SQL, or Structured Query Language, is a domain-specific language used to manage and manipulate relational databases.

A - Aggregate Functions: Functions like COUNT, SUM, AVG, MIN, and MAX used to perform operations on data in a database.

B - BETWEEN: A SQL operator used to filter results within a specific range.

C - CREATE TABLE: SQL statement for creating a new table in a database.

D - DELETE: SQL statement used to delete records from a table.

E - EXISTS: SQL operator used in a subquery to test if a specified condition exists.

F - FOREIGN KEY: A field in a database table that is a primary key in another table, establishing a link between the two tables.

G - GROUP BY: SQL clause used to group rows that have the same values in specified columns.

H - HAVING: SQL clause used in combination with GROUP BY to filter the results.

I - INNER JOIN: SQL clause used to combine rows from two or more tables based on a related column between them.

J - JOIN: Combines rows from two or more tables based on a related column.

K - KEY: A field or set of fields in a database table that uniquely identifies each record.

L - LIKE: SQL operator used in a WHERE clause to search for a specified pattern in a column.

M - MODIFY: SQL command used to modify an existing database table.

N - NULL: Represents missing or undefined data in a database.

O - ORDER BY: SQL clause used to sort the result set in ascending or descending order.

P - PRIMARY KEY: A field in a table that uniquely identifies each record in that table.

Q - QUERY: A request for data from a database using SQL.

R - ROLLBACK: SQL command used to undo transactions that have not been saved to the database.

S - SELECT: SQL statement used to query the database and retrieve data.

T - TRUNCATE: SQL command used to delete all records from a table without logging individual row deletions.

U - UPDATE: SQL statement used to modify the existing records in a table.

V - VIEW: A virtual table based on the result of a SELECT query.

W - WHERE: SQL clause used to filter the results of a query based on a specified condition.

X - (E)XISTS: Used in conjunction with SELECT to test the existence of rows returned by a subquery.

Z - ZERO: Represents the absence of a value in numeric fields or the initial state of boolean fields.

Here you can find essential Pyspark Resources👇
https://www.instagram.com/codingdidi

Like this post if you need more 👍❤️

Hope it helps :)

Top 10 Advanced SQL Queries for Data Mastery

1. Recursive CTE (Common Table Expressions)
Use a recursive CTE to traverse hierarchical data, such as employees and their managers.

WITH RECURSIVE EmployeeHierarchy AS (
  SELECT employee_id, employee_name, manager_id
  FROM employees
  WHERE manager_id IS NULL
  UNION ALL
  SELECT e.employee_id, e.employee_name, e.manager_id
  FROM employees e
  JOIN EmployeeHierarchy eh ON e.manager_id = eh.employee_id
)
SELECT * 
FROM EmployeeHierarchy;

2. Pivoting Data
Turn row data into columns (e.g., show product categories as separate columns).

SELECT *
FROM (
    SELECT TO_CHAR(order_date, 'YYYY-MM') AS month, product_category, sales_amount
    FROM sales
) AS pivot_data
PIVOT (
    SUM(sales_amount)
    FOR product_category IN ('Electronics', 'Clothing', 'Books')
) AS pivoted_sales;

3. Window Functions
Calculate a running total of sales based on order date.

SELECT 
  order_date, 
  sales_amount, 
  SUM(sales_amount) OVER (ORDER BY order_date) AS running_total
FROM sales;

4. Ranking with Window Functions
Rank employees’ salaries within each department.

SELECT 
  department, 
  employee_name, 
  salary,
  RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank
FROM employees;

5. Finding Gaps in Sequences
Identify missing values in a sequential dataset (e.g., order numbers).

WITH Sequences AS (
  SELECT MIN(order_number) AS start_seq, MAX(order_number) AS end_seq
  FROM orders
)
SELECT start_seq + 1 AS missing_sequence
FROM Sequences
WHERE NOT EXISTS (
  SELECT 1
  FROM orders o
  WHERE o.order_number = Sequences.start_seq + 1
);

6. Unpivoting Data
Convert columns into rows to simplify analysis of multiple attributes.

SELECT 
  product_id, 
  attribute_name, 
  attribute_value
FROM products
UNPIVOT (
  attribute_value FOR attribute_name IN (color, size, weight)
) AS unpivoted_data;

7. Finding Consecutive Events
Check for consecutive days/orders for the same product using LAG().

WITH ConsecutiveOrders AS (
  SELECT 
    product_id, 
    order_date,
    LAG(order_date) OVER (PARTITION BY product_id ORDER BY order_date) AS prev_order_date
  FROM orders
)
SELECT product_id, order_date, prev_order_date
FROM ConsecutiveOrders
WHERE order_date - prev_order_date = 1;

8. Aggregation with the FILTER Clause
Calculate selective averages (e.g., only for the Sales department).

SELECT 
  department,
  AVG(salary) FILTER (WHERE department = 'Sales') AS avg_salary_sales
FROM employees
GROUP BY department;

9. JSON Data Extraction
Extract values from JSON columns directly in SQL.

SELECT 
  order_id, 
  customer_id,
  order_details ->> 'product' AS product_name,
  CAST(order_details ->> 'quantity' AS INTEGER) AS quantity
FROM orders;

10. Using Temporary Tables
Create a temporary table for intermediate results, then join it with other tables.

-- Create a temporary table
CREATE TEMPORARY TABLE temp_product_sales AS
SELECT product_id, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_id;

-- Use the temp table
SELECT p.product_name, t.total_sales
FROM products p
JOIN temp_product_sales t ON p.product_id = t.product_id;

Why These Matter
Advanced SQL queries let you handle complex data manipulation and analysis tasks with ease. From traversing hierarchical relationships to reshaping data (pivot/unpivot) and working with JSON, these techniques expand your ability to derive insights from relational databases.

Keep practicing these queries to solidify your SQL expertise and make more data-driven decisions!

Here you can find essential Pyspark Resources👇
https://www.instagram.com/codingdidi

Like this post if you need more 👍❤️

Hope it helps :)

#sql #dataanalyst

15+ interview SQL questions, including both technical and non-technical questions, along with their answers.

1. What is SQL?
   - Answer: SQL (Structured Query Language) is a standard programming language specifically designed for managing and manipulating relational databases.

2. What are the different types of SQL statements?
   - Answer: SQL statements can be classified into DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and TCL (Transaction Control Language).

3. What is a primary key?
   - Answer: A primary key is a field (or combination of fields) in a table that uniquely identifies each row/record in that table.

4. What is a foreign key?
   - Answer: A foreign key is a field (or collection of fields) in one table that uniquely identifies a row of another table or the same table. It establishes a link between the data in two tables.

5. What are joins? Explain different types of joins.
   - Answer: A join is an SQL operation for combining records from two or more tables. Types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).

6. What is normalization?
   - Answer: Normalization is the process of organizing data to reduce redundancy and improve data integrity. This typically involves dividing a database into two or more tables and defining relationships between them.

7. What is denormalization?
   - Answer: Denormalization is the process of combining normalized tables into fewer tables to improve database read performance, sometimes at the expense of write performance and data integrity.

8. What is stored procedure?
   - Answer: A stored procedure is a prepared SQL code that you can save and reuse. So, if you have an SQL query that you write frequently, you can save it as a stored procedure and then call it to execute it.

9. What is an index?
   - Answer: An index is a database object that improves the speed of data retrieval operations on a table at the cost of additional storage and maintenance overhead.

10. What is a view in SQL?
    - Answer: A view is a virtual table based on the result set of an SQL query. It contains rows and columns, just like a real table, but does not physically store the data.

11. What is a subquery?
    - Answer: A subquery is an SQL query nested inside a larger query. It is used to return data that will be used in the main query as a condition to further restrict the data to be retrieved.

12. What are aggregate functions in SQL?
    - Answer: Aggregate functions perform a calculation on a set of values and return a single value. Examples include COUNT, SUM, AVG (average), MIN (minimum), and MAX (maximum).

13. Difference between DELETE and TRUNCATE?
    - Answer: DELETE removes rows one at a time and logs each delete, while TRUNCATE removes all rows in a table without logging individual row deletions. TRUNCATE is faster but cannot be rolled back.

14. What is a UNION in SQL?
    - Answer: UNION is an operator used to combine the result sets of two or more SELECT statements. It removes duplicate rows between the various SELECT statements.

15. What is a cursor in SQL?
    - Answer: A cursor is a database object used to retrieve, manipulate, and navigate through a result set one row at a time.

16. What is trigger in SQL?
    - Answer: A trigger is a set of SQL statements that automatically execute or "trigger" when certain events occur in a database, such as INSERT, UPDATE, or DELETE.

17. Difference between clustered and non-clustered indexes?
    - Answer: A clustered index determines the physical order of data in a table and can only be one per table. A non-clustered index, on the other hand, creates a logical order and can be many per table.

18. Explain the term ACID.
    - Answer: ACID stands for Atomicity, Consistency, Isolation, and Durability.


IF you're looking forward to learn python, dm now on Whatsapp +91-9910986344

You will be 20𝐱 better at SQL

If you cover these topics in sequence:


𝗦𝗤𝗟 𝗕𝗮𝘀𝗶𝗰

1. SELECT and WHERE Clauses | Filtering and retrieving data efficiently
2. GROUP BY and HAVING | Aggregating data with conditional logic
3. JOINs (INNER, LEFT, RIGHT, FULL) | Combining data from multiple tables
4. DISTINCT and LIMIT | Handling duplicates and limiting results

𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲

1. Subqueries | Using queries inside queries for complex filtering
2. Window Functions (ROW_NUMBER, RANK, DENSE_RANK) | Analyzing data over partitions
3. CASE Statements | Conditional logic within your queries
4. Common Table Expressions (CTEs) | Simplifying complex queries for readability

𝗦𝗤𝗟 𝗔𝗱𝘃𝗮𝗻𝗰𝗲
1. Recursive CTEs | Solving hierarchical and iterative problems
2. Pivot and Unpivot | Reshaping your data for better insights
3. Temporary Tables | Storing intermediate results for complex operations
4. Optimizing SQL Queries | Improving performance with indexing and query plans


Like if you want more post like this!! 🤩

Power BI Interview Questions Asked Bajaj Auto Ltd

1. Self Introduction
2. What are your roles and responsibilities of your project?
3. Difference between Import Mode and Direct Mode?
4. What kind of projects have you worked on Domain?
5. How do you handle complex data transformations in Power Query? Can you provide an example of a challenging transformation you implemented?
6. What challenges you faced while doing a projects?
7. Types of Refreshes in Power BI?
8. What is DAX in Power BI?
9. How do you perform data cleansing and transformation in Power BI?
10. How do you connect to data sources in Power BI?
11. What are the components in Power BI?
12. What is Power Pivot will do in Power BI?
13. Write a query to fetch top 5 employees having highest salary?
14. Write a query to find 2nd highest salary from employee table?
15. Difference between Rank function & Dense Rank function in SQL?
16. Difference between Power BI Desktop & Power BI Service?
17. How will you optimize Power BI reports?
18. What are the difficulties you have faced when doing a projects?
19. How can you optimize a SQL query?
20. What is Indexes?
21. How ETL process happen in Power BI?
22. What is difference between Star schema & Snowflake schema and how will know when to use which schemas respectively?
23. How will you perform filtering & it's types?
24. What is Bookmarks?
25. Difference between Drilldown and Drill through in Power BI?
26. Difference between Calculated column and measure?
27. Difference between Slicer and Filter?
28. What is a use Pandas, Matplotlib, seaborn Libraries?
29. Difference between Sum and SumX?
30. Do you have any questions?

🎉 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 (4 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!

📩 DM me or comment "Python" to get started today!

9910986344
codingdidi2024@gmail.com

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