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.
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.
โค3
HTTP status codes โ quick cheat sheet
โ 200 OK: request succeeded
๐ 201 Created: new resource saved
๐ 204 No Content: success, nothing to return
๐ 301 Moved Permanently: use new URL
โช๏ธ 302 Found: temporary redirect
๐งพ 304 Not Modified: use cached version
๐ 400 Bad Request: invalid input
๐ชช 401 Unauthorized: missing/invalid auth
๐ซ 403 Forbidden: authenticated but not allowed
โ 404 Not Found: resource doesnโt exist
โณ 408 Request Timeout: client took too long
๐งฏ 409 Conflict: state/version clash
๐ฅ 500 Internal Server Error: server crashed
๐ ๏ธ 502 Bad Gateway: upstream failed
๐ธ๏ธ 503 Service Unavailable: overloaded/maintenance
โ 504 Gateway Timeout: upstream too slow
tips
โข return precise codes; donโt default to 200/500
โข include a machine-readable error body (code, message, details)
โข never leak stack traces in production
โข pair 304 with ETag/If-None-Match for caching
โ 200 OK: request succeeded
๐ 201 Created: new resource saved
๐ 204 No Content: success, nothing to return
๐ 301 Moved Permanently: use new URL
โช๏ธ 302 Found: temporary redirect
๐งพ 304 Not Modified: use cached version
๐ 400 Bad Request: invalid input
๐ชช 401 Unauthorized: missing/invalid auth
๐ซ 403 Forbidden: authenticated but not allowed
โ 404 Not Found: resource doesnโt exist
โณ 408 Request Timeout: client took too long
๐งฏ 409 Conflict: state/version clash
๐ฅ 500 Internal Server Error: server crashed
๐ ๏ธ 502 Bad Gateway: upstream failed
๐ธ๏ธ 503 Service Unavailable: overloaded/maintenance
โ 504 Gateway Timeout: upstream too slow
tips
โข return precise codes; donโt default to 200/500
โข include a machine-readable error body (code, message, details)
โข never leak stack traces in production
โข pair 304 with ETag/If-None-Match for caching
โค4
Don't overwhelm to learn Git,๐
Git is only this much๐๐
1.Core:
โข git init
โข git clone
โข git add
โข git commit
โข git status
โข git diff
โข git checkout
โข git reset
โข git log
โข git show
โข git tag
โข git push
โข git pull
2.Branching:
โข git branch
โข git checkout -b
โข git merge
โข git rebase
โข git branch --set-upstream-to
โข git branch --unset-upstream
โข git cherry-pick
3.Merging:
โข git merge
โข git rebase
4.Stashing:
โข git stash
โข git stash pop
โข git stash list
โข git stash apply
โข git stash drop
5.Remotes:
โข git remote
โข git remote add
โข git remote remove
โข git fetch
โข git pull
โข git push
โข git clone --mirror
6.Configuration:
โข git config
โข git global config
โข git reset config
7. Plumbing:
โข git cat-file
โข git checkout-index
โข git commit-tree
โข git diff-tree
โข git for-each-ref
โข git hash-object
โข git ls-files
โข git ls-remote
โข git merge-tree
โข git read-tree
โข git rev-parse
โข git show-branch
โข git show-ref
โข git symbolic-ref
โข git tag --list
โข git update-ref
8.Porcelain:
โข git blame
โข git bisect
โข git checkout
โข git commit
โข git diff
โข git fetch
โข git grep
โข git log
โข git merge
โข git push
โข git rebase
โข git reset
โข git show
โข git tag
9.Alias:
โข git config --global alias.<alias> <command>
10.Hook:
โข git config --local core.hooksPath <path>
โ Best Telegram channels to get free coding & data science resources
https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
โ Free Courses with Certificate:
https://t.iss.one/free4unow_backup
Git is only this much๐๐
1.Core:
โข git init
โข git clone
โข git add
โข git commit
โข git status
โข git diff
โข git checkout
โข git reset
โข git log
โข git show
โข git tag
โข git push
โข git pull
2.Branching:
โข git branch
โข git checkout -b
โข git merge
โข git rebase
โข git branch --set-upstream-to
โข git branch --unset-upstream
โข git cherry-pick
3.Merging:
โข git merge
โข git rebase
4.Stashing:
โข git stash
โข git stash pop
โข git stash list
โข git stash apply
โข git stash drop
5.Remotes:
โข git remote
โข git remote add
โข git remote remove
โข git fetch
โข git pull
โข git push
โข git clone --mirror
6.Configuration:
โข git config
โข git global config
โข git reset config
7. Plumbing:
โข git cat-file
โข git checkout-index
โข git commit-tree
โข git diff-tree
โข git for-each-ref
โข git hash-object
โข git ls-files
โข git ls-remote
โข git merge-tree
โข git read-tree
โข git rev-parse
โข git show-branch
โข git show-ref
โข git symbolic-ref
โข git tag --list
โข git update-ref
8.Porcelain:
โข git blame
โข git bisect
โข git checkout
โข git commit
โข git diff
โข git fetch
โข git grep
โข git log
โข git merge
โข git push
โข git rebase
โข git reset
โข git show
โข git tag
9.Alias:
โข git config --global alias.<alias> <command>
10.Hook:
โข git config --local core.hooksPath <path>
โ Best Telegram channels to get free coding & data science resources
https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
โ Free Courses with Certificate:
https://t.iss.one/free4unow_backup
โค2๐2
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 ๐๐
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 ๐๐
โค6
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');
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');
โค4
โ
Top Programming Basics Interview Questions with Answers ๐ง ๐ป
1๏ธโฃ 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:
2๏ธโฃ What are data types?
Answer:
Data types define the kind of value a variable can hold. Common types:
โ int: Integer (e.g., 5)
โ float: Decimal (e.g., 3.14)
โ char / str: Character or String
โ bool: Boolean (True/False)
3๏ธโฃ What are operators in programming?
Answer:
Operators perform operations on variables/values.
โ Arithmetic: +, -, *, /
โ Comparison: ==,!=, >, <
โ Logical: &&, ||,! (or and, or, not)
โ Assignment: =, +=, -=
4๏ธโฃ What is type casting?
Answer:
Type casting means converting one data type to another.
Example (Python):
5๏ธโฃ What is the purpose of comments in code?
Answer:
Comments are used to explain code. They're ignored during execution.
โ Single-line: // comment or # comment
โ Multi-line:
6๏ธโฃ How do you take input and display output?
Answer:
Python Example:
C++ Example:
7๏ธโฃ What is the difference between a statement and an expression?
Answer:
โ Expression: Returns a value (e.g., 2 + 3)
โ Statement: Performs an action (e.g., x = 5)
8๏ธโฃ What is the difference between compile-time and run-time?
Answer:
โ Compile-time: Errors detected before execution (e.g., syntax errors)
โ Run-time: Errors during execution (e.g., divide by zero)
๐ฌ Double Tap โค๏ธ for more!
1๏ธโฃ 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
2๏ธโฃ What are data types?
Answer:
Data types define the kind of value a variable can hold. Common types:
โ int: Integer (e.g., 5)
โ float: Decimal (e.g., 3.14)
โ char / str: Character or String
โ bool: Boolean (True/False)
3๏ธโฃ What are operators in programming?
Answer:
Operators perform operations on variables/values.
โ Arithmetic: +, -, *, /
โ Comparison: ==,!=, >, <
โ Logical: &&, ||,! (or and, or, not)
โ Assignment: =, +=, -=
4๏ธโฃ What is type casting?
Answer:
Type casting means converting one data type to another.
Example (Python):
x = int("5") # Converts string to integer
5๏ธโฃ What is the purpose of comments in code?
Answer:
Comments are used to explain code. They're ignored during execution.
โ Single-line: // comment or # comment
โ Multi-line:
"""
This is a
multi-line comment
"""
6๏ธโฃ 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;
7๏ธโฃ What is the difference between a statement and an expression?
Answer:
โ Expression: Returns a value (e.g., 2 + 3)
โ Statement: Performs an action (e.g., x = 5)
8๏ธโฃ What is the difference between compile-time and run-time?
Answer:
โ Compile-time: Errors detected before execution (e.g., syntax errors)
โ Run-time: Errors during execution (e.g., divide by zero)
๐ฌ Double Tap โค๏ธ for more!
โค10
โก 25 Browser Extensions to Supercharge Your Coding Workflow ๐
โ JSON Viewer
โ Octotree (GitHub code tree)
โ Web Developer Tools
โ Wappalyzer (tech stack detector)
โ React Developer Tools
โ Redux DevTools
โ Vue js DevTools
โ Angular DevTools
โ ColorZilla
โ WhatFont
โ CSS Peeper
โ Axe DevTools (accessibility)
โ Page Ruler Redux
โ Lighthouse
โ Check My Links
โ EditThisCookie
โ Tampermonkey
โ Postman Interceptor
โ RESTED
โ GraphQL Playground
โ XPath Helper
โ Gitpod Browser Extension
โ Codeium for Chrome
โ TabNine Assistant
โ Grammarly (for cleaner docs & commits)
๐ฅ React โค๏ธ if youโre using at least one of these!
โ JSON Viewer
โ Octotree (GitHub code tree)
โ Web Developer Tools
โ Wappalyzer (tech stack detector)
โ React Developer Tools
โ Redux DevTools
โ Vue js DevTools
โ Angular DevTools
โ ColorZilla
โ WhatFont
โ CSS Peeper
โ Axe DevTools (accessibility)
โ Page Ruler Redux
โ Lighthouse
โ Check My Links
โ EditThisCookie
โ Tampermonkey
โ Postman Interceptor
โ RESTED
โ GraphQL Playground
โ XPath Helper
โ Gitpod Browser Extension
โ Codeium for Chrome
โ TabNine Assistant
โ Grammarly (for cleaner docs & commits)
๐ฅ React โค๏ธ if youโre using at least one of these!
โค7๐ฅฐ2
๐๐/๐ ๐ ๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐น๐ฐ๐น๐ฎ๐๐๐
Kickstart Your AI & Machine Learning Career
- Leverage your skills in the AI-driven job market
- Get exposed to the Generative AI Tools, Technologies, and Platforms
Eligibility :- Working Professionals & Graduates
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/47fcsF5
Date :- October 30, 2025 Time:-7:00 PM
Kickstart Your AI & Machine Learning Career
- Leverage your skills in the AI-driven job market
- Get exposed to the Generative AI Tools, Technologies, and Platforms
Eligibility :- Working Professionals & Graduates
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/47fcsF5
Date :- October 30, 2025 Time:-7:00 PM