Data Analytics Pattern Identification....;;

Trend Analysis: Examining data over time to identify upward or downward trends.

Seasonal Patterns: Identifying recurring patterns or trends based on seasons or specific time periods

Correlation: Understanding relationships between variables and how changes in one may affect another.

Outlier Detection: Identifying data points that deviate significantly from the overall pattern.

Clustering: Grouping similar data points together to find natural patterns within the data.

Classification: Categorizing data into predefined classes or groups based on certain features.

Regression Analysis: Predicting a dependent variable based on the values of independent variables.

Frequency Distribution: Analyzing the distribution of values within a dataset.

Pattern Recognition: Identifying recurring structures or shapes within the data.

Text Analysis: Extracting insights from unstructured text data through techniques like sentiment analysis or topic modeling.

These patterns help organizations make informed decisions, optimize processes, and gain a deeper understanding of their data.

Data Structures Cheatsheet 👆

HTTP status codes — quick cheat sheet

✅ 200 OK: request succeeded
🆕 201 Created: new resource saved
📝 204 No Content: success, nothing to return
🔀 301 Moved Permanently: use new URL
↪️ 302 Found: temporary redirect
🧾 304 Not Modified: use cached version

🙅 400 Bad Request: invalid input
🪪 401 Unauthorized: missing/invalid auth
🚫 403 Forbidden: authenticated but not allowed
❓ 404 Not Found: resource doesn’t exist
⏳ 408 Request Timeout: client took too long
🧯 409 Conflict: state/version clash

💥 500 Internal Server Error: server crashed
🛠️ 502 Bad Gateway: upstream failed
🕸️ 503 Service Unavailable: overloaded/maintenance
⌛ 504 Gateway Timeout: upstream too slow

tips
• return precise codes; don’t default to 200/500
• include a machine-readable error body (code, message, details)
• never leak stack traces in production
• pair 304 with ETag/If-None-Match for caching

Don't overwhelm to learn Git,🙌

Git is only this much👇😇

1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull

2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick

3.Merging:
• git merge
• git rebase

4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop

5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror

6.Configuration:
• git config
• git global config
• git reset config

7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref

8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag

9.Alias:
• git config --global alias.<alias> <command>

10.Hook:
• git config --local core.hooksPath <path>

✅ Best Telegram channels to get free coding & data science resources
https://t.iss.one/addlist/4q2PYC0pH_VjZDk5

✅ Free Courses with Certificate:
https://t.iss.one/free4unow_backup

Here are some essential data science concepts from A to Z:

A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.

B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.

C - Clustering: A technique used to group similar data points together based on certain characteristics.

D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.

E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.

F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.

G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.

H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.

I - Imputation: The process of filling in missing values in a dataset using statistical methods.

J - Joint Probability: The probability of two or more events occurring together.

K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.

L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.

M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.

N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.

O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.

P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.

Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.

R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.

S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.

T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.

U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.

V - Validation Set: A subset of data used to evaluate the performance of a model during training.

W - Web Scraping: The process of extracting data from websites for analysis and visualization.

X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.

Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.

Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.

Credits: https://t.iss.one/free4unow_backup

Like if you need similar content 😄👍

Here are the 5 SQL questions you can practice this weekend.



1. Write an SQL query to show, for each segment, the total number of users and the number of users who booked a flight in April 2022.

2. Write a query to identify users whose first booking was a hotel booking.

3. Write a query to calculate the number of days between the first and last booking of the user with user_id = 1.

4. Write a query to count the number of flight and hotel bookings in each user segment for the year 2022.

5. Find, for each segment, the user who made the earliest booking in April 2022, and also return how many total bookings that user made in April 2022.

create table booking_table (
booking_id varchar(10),
booking_date date,
user_id varchar(10),
line_of_business varchar(20)
);

insert into booking_table (booking_id, booking_date, user_id, line_of_business) values
('b1', '2022-03-23', 'u1', 'Flight'),
('b2', '2022-03-27', 'u2', 'Flight'),
('b3', '2022-03-28', 'u1', 'Hotel'),
('b4', '2022-03-31', 'u4', 'Flight'),
('b5', '2022-04-02', 'u1', 'Hotel'),
('b6', '2022-04-02', 'u2', 'Flight'),
('b7', '2022-04-06', 'u5', 'Flight'),
('b8', '2022-04-06', 'u6', 'Hotel'),
('b9', '2022-04-06', 'u2', 'Flight'),
('b10', '2022-04-10', 'u1', 'Flight'),
('b11', '2022-04-12', 'u4', 'Flight'),
('b12', '2022-04-16', 'u1', 'Flight'),
('b13', '2022-04-19', 'u2', 'Flight'),
('b14', '2022-04-20', 'u5', 'Hotel'),
('b15', '2022-04-22', 'u6', 'Flight'),
('b16', '2022-04-26', 'u4', 'Hotel'),
('b17', '2022-04-28', 'u2', 'Hotel'),
('b18', '2022-04-30', 'u1', 'Hotel'),
('b19', '2022-05-04', 'u4', 'Hotel'),
('b20', '2022-05-06', 'u1', 'Flight');

create table user_table (
user_id varchar(10),
segment varchar(10)
);

insert into user_table (user_id, segment) values
('u1', 's1'),
('u2', 's1'),
('u3', 's1'),
('u4', 's2'),
('u5', 's2'),
('u6', 's3'),
('u7', 's3'),
('u8', 's3'),
('u9', 's3'),
('u10', 's3');

✅ Top Programming Basics Interview Questions with Answers 🧠💻

1️⃣ What is a variable?
Answer:
A variable is a named container used to store data in a program. Its value can change during execution.
Example:

name = "Alice"
age = 25

2️⃣ What are data types?
Answer:
Data types define the kind of value a variable can hold. Common types:
– int: Integer (e.g., 5)
– float: Decimal (e.g., 3.14)
– char / str: Character or String
– bool: Boolean (True/False)

3️⃣ What are operators in programming?
Answer:
Operators perform operations on variables/values.
– Arithmetic: +, -, *, /
– Comparison: ==,!=, >, <
– Logical: &&, ||,! (or and, or, not)
– Assignment: =, +=, -=

4️⃣ What is type casting?
Answer:
Type casting means converting one data type to another.
Example (Python):

x = int("5")     # Converts string to integer

5️⃣ What is the purpose of comments in code?
Answer:
Comments are used to explain code. They're ignored during execution.
– Single-line: // comment or # comment
– Multi-line:

"""
This is a
multi-line comment
"""

6️⃣ How do you take input and display output?
Answer:
Python Example:

name = input("Enter your name: ")
print("Hello", name)

C++ Example:

cin >> name;
cout << "Hello " << name;

7️⃣ What is the difference between a statement and an expression?
Answer:
– Expression: Returns a value (e.g., 2 + 3)
– Statement: Performs an action (e.g., x = 5)

8️⃣ What is the difference between compile-time and run-time?
Answer:
– Compile-time: Errors detected before execution (e.g., syntax errors)
– Run-time: Errors during execution (e.g., divide by zero)

💬 Double Tap ❤️ for more!

⚡ 25 Browser Extensions to Supercharge Your Coding Workflow 🚀

✅ JSON Viewer
✅ Octotree (GitHub code tree)
✅ Web Developer Tools
✅ Wappalyzer (tech stack detector)
✅ React Developer Tools
✅ Redux DevTools
✅ Vue js DevTools
✅ Angular DevTools
✅ ColorZilla
✅ WhatFont
✅ CSS Peeper
✅ Axe DevTools (accessibility)
✅ Page Ruler Redux
✅ Lighthouse
✅ Check My Links
✅ EditThisCookie
✅ Tampermonkey
✅ Postman Interceptor
✅ RESTED
✅ GraphQL Playground
✅ XPath Helper
✅ Gitpod Browser Extension
✅ Codeium for Chrome
✅ TabNine Assistant
✅ Grammarly (for cleaner docs & commits)

🔥 React ❤️ if you’re using at least one of these!

💡 10 Smart Programming Habits Every Developer Should Build 👨‍💻🧠

1️⃣ Write clean, readable code
→ Code is read more often than it’s written. Clarity > cleverness.

2️⃣ Break big problems into small parts
→ Divide and conquer. Small functions are easier to debug and reuse.

3️⃣ Use meaningful commit messages
→ “Fixed stuff” doesn’t help. Be specific: “Fix null check on login form.”

4️⃣ Keep learning new tools & languages
→ Tech evolves fast. Stay curious and adaptable.

5️⃣ Write tests, even basic ones
→ Prevent future bugs. Start with simple unit tests.

6️⃣ Use a linter and formatter
→ Tools like ESLint, Black, or Prettier keep your code clean automatically.

7️⃣ Document your code
→ Write docstrings or inline comments to explain logic clearly.

8️⃣ Review your code before pushing
→ Catch silly mistakes early. Think of it as proofreading your code.

9️⃣ Optimize only when needed
→ First make it work, then make it fast.

🔟 Contribute to open source or side projects
→ Practice, network, and learn from real-world codebases.

💬 Tap ❤️ if you found this helpful!