๐ง๐ผ๐ฝ ๐ง๐ฒ๐ฐ๐ต ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - ๐๐ฟ๐ฎ๐ฐ๐ธ ๐ฌ๐ผ๐๐ฟ ๐ก๐ฒ๐
๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐
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๐๐ฎ๐๐ฎ :- https://pdlink.in/4dWkAMf
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Get Your Dream Tech Job In Your Dream Company๐ซ
โค1
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.me/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.me/free4unow_backup
Like if you need similar content ๐๐
โค3๐3
Real-world Data Science projects ideas: ๐ก๐
1. Credit Card Fraud Detection
๐ Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
๐ Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
๐ Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
๐ Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
๐ Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
๐ Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
๐ Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
๐ Pick 2โ3 projects aligned with your interests.
๐ Document everything on GitHub, and post about your learnings on LinkedIn.
Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29
React โค๏ธ for more
1. Credit Card Fraud Detection
๐ Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
๐ Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
๐ Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
๐ Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
๐ Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
๐ Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
๐ Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
๐ Pick 2โ3 projects aligned with your interests.
๐ Document everything on GitHub, and post about your learnings on LinkedIn.
Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29
React โค๏ธ for more
โค3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฐ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐ป๐ฒ๐ โ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ-๐๐ฟ๐ถ๐ฒ๐ป๐ฑ๐น๐ & ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ!๐
Ready to kickstart your career in Data Scienceโwithout spending a rupee?๐ฐ
These 4 beginner-friendly courses will help you build a strong foundation in data science by teaching you how to gather, clean, analyse, and visualise data๐๐
๐๐ฝ๐ฝ๐น๐ ๐๐ถ๐ป๐ธ๐:-๐
https://pdlink.in/45uXCtI
An initiative supported by NASSCOM and the Government of Indiaโ ๏ธ
Ready to kickstart your career in Data Scienceโwithout spending a rupee?๐ฐ
These 4 beginner-friendly courses will help you build a strong foundation in data science by teaching you how to gather, clean, analyse, and visualise data๐๐
๐๐ฝ๐ฝ๐น๐ ๐๐ถ๐ป๐ธ๐:-๐
https://pdlink.in/45uXCtI
An initiative supported by NASSCOM and the Government of Indiaโ ๏ธ
โค2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ณ ๐ฅ๐ฒ๐ฎ๐ฑ๐-๐๐ผ-๐จ๐๐ฒ ๐๐บ๐ฎ๐ถ๐น ๐๐ผ๐ฟ๐บ๐ฎ๐๐ ๐๐ผ ๐๐บ๐ฝ๐ฟ๐ฒ๐๐ ๐ฅ๐ฒ๐ฐ๐ฟ๐๐ถ๐๐ฒ๐ฟ๐๐
๐ฉ Struggling to write the perfect email to a recruiter?๐ฃ
Youโre not alone. The way you write your email can make or break your first impression๐ค
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TtQh64
Having the right email format makes all the differenceโ ๏ธ
๐ฉ Struggling to write the perfect email to a recruiter?๐ฃ
Youโre not alone. The way you write your email can make or break your first impression๐ค
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TtQh64
Having the right email format makes all the differenceโ ๏ธ
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.me/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.me/free4unow_backup
Like if you need similar content ๐๐
โค6
5โฃ frequently Asked SQL Interview Questions with Answers in data analyst interviews
๐1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).
๐2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.
๐3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).
๐4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.
;
๐5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).
Hope it helps :)
๐1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).
SELECT c.CustomerID, c. Name, AVG(o.Amount) AS AveragePurchase
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c. Name;
๐2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.
SELECT e1.DepartmentID, e1.EmployeeID, e1 .Name, e1.Salary
FROM Employees e1
WHERE Salary = (SELECT MIN(Salary) FROM Employees e2 WHERE e2.DepartmentID = e1.DepartmentID);
๐3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).
SELECT p.ProductID, p.ProductName
FROM Products p
LEFT JOIN Sales s ON p.ProductID = s.ProductID
WHERE s.ProductID IS NULL;
๐4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.
SELECT o.CustomerID, SUM(oi.Quantity) AS TotalQuantity
FROM Orders o
JOIN OrderItems oi ON o.OrderID = oi.OrderID
GROUP BY o.CustomerID
ORDER BY TotalQuantity DESC
LIMIT 1
;
๐5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).
SELECT CustomerID, MIN(OrderDate) AS EarliestOrderDate
FROM Orders
GROUP BY CustomerID
Hope it helps :)
โค2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
๐ You donโt need to break the bank to break into AI!๐ชฉ
If youโve been searching for beginner-friendly, certified AI learningโGoogle Cloud has you covered๐ค๐จโ๐ป
๐๐ข๐ง๐ค๐:-
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๐All taught by industry-leading instructorsโ ๏ธ
๐ You donโt need to break the bank to break into AI!๐ชฉ
If youโve been searching for beginner-friendly, certified AI learningโGoogle Cloud has you covered๐ค๐จโ๐ป
๐๐ข๐ง๐ค๐:-
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๐All taught by industry-leading instructorsโ ๏ธ
โค1
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ง๐ผ๐ฝ ๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐ด๐ด๐น๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ถ๐๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐๐บ๐ฝ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
Want to break into Data Science but not sure where to start?๐
These free Kaggle micro-courses are the perfect launchpad โ beginner-friendly, self-paced, and yes, they come with certifications!๐จโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4l164FN
No subscription. No hidden fees. Just pure learning from a trusted platformโ ๏ธ
Want to break into Data Science but not sure where to start?๐
These free Kaggle micro-courses are the perfect launchpad โ beginner-friendly, self-paced, and yes, they come with certifications!๐จโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4l164FN
No subscription. No hidden fees. Just pure learning from a trusted platformโ ๏ธ
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ + ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
Ready to upgrade your career without spending a dime?โจ๏ธ
From Generative AI to Project Management, get trained by global tech leaders and earn certificates that carry real value on your resume and LinkedIn profile!๐ฒ๐
๐๐ข๐ง๐ค๐:-
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Designed to equip you with in-demand skills and industry-recognised certifications๐โ ๏ธ
Ready to upgrade your career without spending a dime?โจ๏ธ
From Generative AI to Project Management, get trained by global tech leaders and earn certificates that carry real value on your resume and LinkedIn profile!๐ฒ๐
๐๐ข๐ง๐ค๐:-
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Designed to equip you with in-demand skills and industry-recognised certifications๐โ ๏ธ
๐ฐ C++ Roadmap for Beginners 2025
โโโ ๐ง Introduction to C++ & How It Works
โโโ ๐งฐ Setting Up Environment (IDE, Compiler)
โโโ ๐ Basic Syntax & Structure
โโโ ๐ข Variables, Data Types & Constants
โโโ โ Operators (Arithmetic, Relational, Logical, Bitwise)
โโโ ๐ Flow Control (if, else, switch)
โโโ ๐ Loops (for, while, do...while)
โโโ ๐งฉ Functions (Declaration, Definition, Recursion)
โโโ ๐ฆ Arrays, Strings & Vectors
โโโ ๐งฑ Pointers & References
โโโ ๐งฎ Dynamic Memory Allocation (new, delete)
โโโ ๐ Structures & Unions
โโโ ๐ Object-Oriented Programming (Classes, Objects, Inheritance, Polymorphism)
โโโ ๐ File Handling in C++
โโโ โ ๏ธ Exception Handling
โโโ ๐ง STL (Standard Template Library - vector, map, set, etc.)
โโโ ๐งช Mini Projects (Bank System, Student Record, etc.)
Like for the detailed explanation โค๏ธ
#c #programming
โโโ ๐ง Introduction to C++ & How It Works
โโโ ๐งฐ Setting Up Environment (IDE, Compiler)
โโโ ๐ Basic Syntax & Structure
โโโ ๐ข Variables, Data Types & Constants
โโโ โ Operators (Arithmetic, Relational, Logical, Bitwise)
โโโ ๐ Flow Control (if, else, switch)
โโโ ๐ Loops (for, while, do...while)
โโโ ๐งฉ Functions (Declaration, Definition, Recursion)
โโโ ๐ฆ Arrays, Strings & Vectors
โโโ ๐งฑ Pointers & References
โโโ ๐งฎ Dynamic Memory Allocation (new, delete)
โโโ ๐ Structures & Unions
โโโ ๐ Object-Oriented Programming (Classes, Objects, Inheritance, Polymorphism)
โโโ ๐ File Handling in C++
โโโ โ ๏ธ Exception Handling
โโโ ๐ง STL (Standard Template Library - vector, map, set, etc.)
โโโ ๐งช Mini Projects (Bank System, Student Record, etc.)
Like for the detailed explanation โค๏ธ
#c #programming
โค4
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฐ ๐๐ฅ๐๐ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐ ๐ถ๐ป ๐ง๐ฒ๐ฐ๐ต (๐ก๐ผ ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
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Taught by world-class professors!โ ๏ธ
Dreaming of learning from Harvard โ without spending a rupee?๐ฐ
Youโre in luck! These 4 beginner-friendly courses from Harvard University are completely free, self-paced, & beginner-approved๐จโ๐๐
๐๐ข๐ง๐ค๐:-
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Taught by world-class professors!โ ๏ธ
โค3
We're Hiring: Data Scientist at RBL Bank!
Are you passionate about turning data into insights and impact? We're on the lookout for a Data Scientist who brings hands-on experience in Python, SQL, and Tableau to join our dynamic and fast-growing analytics team.
What Youโll Be Doing:
Translating complex business problems into data-driven solutions
Creating insightful dashboards, reports, and machine learning models
Collaborating closely with cross-functional teams to enable smarter decisions
What Weโre Looking For:
Proficiency in Python (Pandas, scikit-learn, etc.)
Strong command over SQL for data extraction and transformation
Experience with Tableau or any other BI tools for visual storytelling
If this sounds like you โ or someone you know โ feel free to DM or send your resume to amit.singh8@rblbank.com.
Are you passionate about turning data into insights and impact? We're on the lookout for a Data Scientist who brings hands-on experience in Python, SQL, and Tableau to join our dynamic and fast-growing analytics team.
What Youโll Be Doing:
Translating complex business problems into data-driven solutions
Creating insightful dashboards, reports, and machine learning models
Collaborating closely with cross-functional teams to enable smarter decisions
What Weโre Looking For:
Proficiency in Python (Pandas, scikit-learn, etc.)
Strong command over SQL for data extraction and transformation
Experience with Tableau or any other BI tools for visual storytelling
If this sounds like you โ or someone you know โ feel free to DM or send your resume to amit.singh8@rblbank.com.
โค2
๐๐ฑ๐๐ข๐ญ๐ข๐ง๐ ๐๐ฉ๐ฉ๐จ๐ซ๐ญ๐ฎ๐ง๐ข๐ญ๐ฒ ๐๐ญ ๐๐๐ซ๐๐จ โ ๐๐โ๐ซ๐ ๐๐ข๐ซ๐ข๐ง๐ ๐ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐ ๐๐ง๐ญ๐๐ซ๐ง! ๐
Are you looking for a hands-on learning experience in ๐๐/๐๐, ๐๐ / ๐๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐๐ซ ๐ฏ๐ข๐ฌ๐ข๐จ๐ง, ๐๐ง๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐?
Weโre expanding our team and seeking Data Science Interns. If youโre passionate about ๐ฅ๐๐ฏ๐๐ซ๐๐ ๐ข๐ง๐ ๐๐ ๐ญ๐จ ๐ซ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง๐ข๐ณ๐ ๐๐ซ๐จ๐ง๐๐ฌ, ๐ฆ๐๐ฉ๐ฉ๐ข๐ง๐ , ๐ฌ๐ฎ๐ซ๐ฏ๐๐ฒ๐ข๐ง๐ , ๐ฆ๐ข๐ง๐ข๐ง๐ , ๐๐ง๐ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌโand you have strong skills in Python, Deep learning, and Computer Visionโletโs connect.
https://aus.keka.com/careers/jobdetails/86596
Are you looking for a hands-on learning experience in ๐๐/๐๐, ๐๐ / ๐๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐๐ซ ๐ฏ๐ข๐ฌ๐ข๐จ๐ง, ๐๐ง๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐?
Weโre expanding our team and seeking Data Science Interns. If youโre passionate about ๐ฅ๐๐ฏ๐๐ซ๐๐ ๐ข๐ง๐ ๐๐ ๐ญ๐จ ๐ซ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง๐ข๐ณ๐ ๐๐ซ๐จ๐ง๐๐ฌ, ๐ฆ๐๐ฉ๐ฉ๐ข๐ง๐ , ๐ฌ๐ฎ๐ซ๐ฏ๐๐ฒ๐ข๐ง๐ , ๐ฆ๐ข๐ง๐ข๐ง๐ , ๐๐ง๐ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌโand you have strong skills in Python, Deep learning, and Computer Visionโletโs connect.
https://aus.keka.com/careers/jobdetails/86596
โค1