Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project:
1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.
2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.
3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.
4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.
5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.
6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.
7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.
8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.
9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.
10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.
1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.
2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.
3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.
4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.
5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.
6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.
7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.
8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.
9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.
10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.
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Which programming language should I use on interview?
Companies usually let you choose, in which case you should use your most comfortable language. If you know a bunch of languages, prefer one that lets you express more with fewer characters and fewer lines of code, like Python or Ruby. It keeps your whiteboard cleaner.
Try to stick with the same language for the whole interview, but sometimes you might want to switch languages for a question. E.g., processing a file line by line will be far easier in Python than in C++.
Sometimes, though, your interviewer will do this thing where they have a pet question thatโs, for example, C-specific. If you list C on your resume, theyโll ask it.
So keep that in mind! If youโre not confident with a language, make that clear on your resume. Put your less-strong languages under a header like โWorking Knowledge.โ
Companies usually let you choose, in which case you should use your most comfortable language. If you know a bunch of languages, prefer one that lets you express more with fewer characters and fewer lines of code, like Python or Ruby. It keeps your whiteboard cleaner.
Try to stick with the same language for the whole interview, but sometimes you might want to switch languages for a question. E.g., processing a file line by line will be far easier in Python than in C++.
Sometimes, though, your interviewer will do this thing where they have a pet question thatโs, for example, C-specific. If you list C on your resume, theyโll ask it.
So keep that in mind! If youโre not confident with a language, make that clear on your resume. Put your less-strong languages under a header like โWorking Knowledge.โ
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Machine Learning Algorithm:
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค2
Complete Roadmap to learn Machine Learning and Artificial Intelligence
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
โค2
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
โค2
๐ฅ Top Programming Languages to learn in 2025 - [Part 1] ๐ฅ
1. JavaScript
- learnjavascript.online
- https://t.iss.one/javascript_courses/1001
- learn-js.org
2. Java
- learnjavaonline.org
- javatpoint.com
3. C#
- learncs.org
- w3schools.com
4. TypeScript
- Typescriptlang.org
- learntypescript.dev
5. Rust
- rust-lang.org
- exercism.org
1. JavaScript
- learnjavascript.online
- https://t.iss.one/javascript_courses/1001
- learn-js.org
2. Java
- learnjavaonline.org
- javatpoint.com
3. C#
- learncs.org
- w3schools.com
4. TypeScript
- Typescriptlang.org
- learntypescript.dev
5. Rust
- rust-lang.org
- exercism.org
โค1
JavaScript (JS) roadmap:
1. Basic Fundamentals:
- Variables, data types, and operators.
- Control structures like loops and conditionals.
- Functions and scope.
2. DOM Manipulation:
- Access and modify HTML and CSS using JavaScript.
- Event handling.
3. Asynchronous Programming:
- Promises and async/await for handling asynchronous operations.
4. ES6 and Modern JavaScript:
- Arrow functions, template literals, and destructuring.
- Modules for code organization.
- Classes for object-oriented programming.
5. Popular Libraries and Frameworks:
- Learn libraries like jQuery or frameworks like React, Angular, or Vue depending on your project needs.
6. Package Management:
- Tools like npm or yarn for managing dependencies.
7. Build Tools:
- Webpack, Babel, and other tools for bundling and transpiling.
8. API Interaction:
- Fetch or Axios for making API requests.
9. State Management (For Frameworks):
- Redux for React, Vuex for Vue, etc.
10. Testing:
- Learn testing frameworks like Jest.
11. Version Control:
- Git for code versioning and collaboration.
12. Continuous Integration (CI) and Deployment:
- Travis CI, Jenkins, or others for automating testing and deployment.
13. Server-Side JavaScript (Optional):
- Node.js for server-side development.
14. Advanced Topics (Optional):
- WebSockets, WebRTC, Progressive Web Apps (PWAs), and more.
This roadmap covers the foundational knowledge and key steps in a JavaScript developer's journey. You can explore more deeply into areas that align with your specific goals and projects.
1. Basic Fundamentals:
- Variables, data types, and operators.
- Control structures like loops and conditionals.
- Functions and scope.
2. DOM Manipulation:
- Access and modify HTML and CSS using JavaScript.
- Event handling.
3. Asynchronous Programming:
- Promises and async/await for handling asynchronous operations.
4. ES6 and Modern JavaScript:
- Arrow functions, template literals, and destructuring.
- Modules for code organization.
- Classes for object-oriented programming.
5. Popular Libraries and Frameworks:
- Learn libraries like jQuery or frameworks like React, Angular, or Vue depending on your project needs.
6. Package Management:
- Tools like npm or yarn for managing dependencies.
7. Build Tools:
- Webpack, Babel, and other tools for bundling and transpiling.
8. API Interaction:
- Fetch or Axios for making API requests.
9. State Management (For Frameworks):
- Redux for React, Vuex for Vue, etc.
10. Testing:
- Learn testing frameworks like Jest.
11. Version Control:
- Git for code versioning and collaboration.
12. Continuous Integration (CI) and Deployment:
- Travis CI, Jenkins, or others for automating testing and deployment.
13. Server-Side JavaScript (Optional):
- Node.js for server-side development.
14. Advanced Topics (Optional):
- WebSockets, WebRTC, Progressive Web Apps (PWAs), and more.
This roadmap covers the foundational knowledge and key steps in a JavaScript developer's journey. You can explore more deeply into areas that align with your specific goals and projects.
โค1
HTML Tags List.pdf
115.1 KB
๐ฐ Free HTML Tag List ๐๐
React โค๏ธ for more like this
Well done guys, will share the cloud opportunity next week ๐
React โค๏ธ for more like this
Well done guys, will share the cloud opportunity next week ๐
โค1
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐ข๐ง๐ ๐๐๐๐๐ฌ๐ฌ๐๐ซ๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
๐๐จ๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ญ๐๐ฌ๐๐ญ:
df = pd.read_csv('your_dataset.csv')
๐๐ง๐ข๐ญ๐ข๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐ฌ๐ฉ๐๐๐ญ๐ข๐จ๐ง:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐๐๐ฅ๐ฎ๐๐ฌ:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Like for more โค๏ธ
ENJOY LEARNING ๐๐
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
๐๐จ๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ญ๐๐ฌ๐๐ญ:
df = pd.read_csv('your_dataset.csv')
๐๐ง๐ข๐ญ๐ข๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐ฌ๐ฉ๐๐๐ญ๐ข๐จ๐ง:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐๐๐ฅ๐ฎ๐๐ฌ:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Like for more โค๏ธ
ENJOY LEARNING ๐๐
โค2
Data Analytics Interview Questions
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
โค3
๐๐๐+ ๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐
- Data Analytics
- BigData
- Artificial Intelligence
- Cloud Computing
- Data Science
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4dJ27Ta
Enroll For FREE & Get Certified ๐
- Data Analytics
- BigData
- Artificial Intelligence
- Cloud Computing
- Data Science
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
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Enroll For FREE & Get Certified ๐
โ
Learn New Skills FREE ๐ฐ
1. Web Development โ
โ๏ธ https://t.iss.one/webdevcoursefree
2. CSS โ
โ๏ธ https://css-tricks.com
3. JavaScript โ
โ๏ธ https://t.iss.one/javascript_courses
4. React โ
โ๏ธ https://react-tutorial.app
5. Data Engineering โ
โ๏ธ https://t.iss.one/sql_engineer
6. Data Science โ
โ๏ธ https://t.iss.one/datasciencefun
7. Python โ
โ๏ธ https://pythontutorial.net
8. SQL โ
โ๏ธ https://t.iss.one/sqlanalyst
9. Git and GitHub โ
โ๏ธ https://GitFluence.com
10. Blockchain โ
โ๏ธ https://t.iss.one/Bitcoin_Crypto_Web
11. Mongo DB โ
โ๏ธ https://mongodb.com
12. Node JS โ
โ๏ธ https://nodejsera.com
13. English Speaking โ
โ๏ธ https://t.iss.one/englishlearnerspro
14. C#โ
โ๏ธ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excelโ
โ๏ธ https://t.iss.one/excel_analyst
16. Generative AIโ
โ๏ธ https://t.iss.one/generativeai_gpt
17. Java
โ๏ธ https://t.iss.one/Java_Programming_Notes
18. Artificial Intelligence
โ๏ธ https://t.iss.one/machinelearning_deeplearning
19. Data Structure & Algorithms
โ๏ธ https://t.iss.one/dsabooks
20. Backend Development
โ๏ธ https://imp.i115008.net/rn2nyy
21. Python for AI
โ๏ธ https://deeplearning.ai/short-courses/ai-python-for-beginners/
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1. Web Development โ
โ๏ธ https://t.iss.one/webdevcoursefree
2. CSS โ
โ๏ธ https://css-tricks.com
3. JavaScript โ
โ๏ธ https://t.iss.one/javascript_courses
4. React โ
โ๏ธ https://react-tutorial.app
5. Data Engineering โ
โ๏ธ https://t.iss.one/sql_engineer
6. Data Science โ
โ๏ธ https://t.iss.one/datasciencefun
7. Python โ
โ๏ธ https://pythontutorial.net
8. SQL โ
โ๏ธ https://t.iss.one/sqlanalyst
9. Git and GitHub โ
โ๏ธ https://GitFluence.com
10. Blockchain โ
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11. Mongo DB โ
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12. Node JS โ
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13. English Speaking โ
โ๏ธ https://t.iss.one/englishlearnerspro
14. C#โ
โ๏ธ https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excelโ
โ๏ธ https://t.iss.one/excel_analyst
16. Generative AIโ
โ๏ธ https://t.iss.one/generativeai_gpt
17. Java
โ๏ธ https://t.iss.one/Java_Programming_Notes
18. Artificial Intelligence
โ๏ธ https://t.iss.one/machinelearning_deeplearning
19. Data Structure & Algorithms
โ๏ธ https://t.iss.one/dsabooks
20. Backend Development
โ๏ธ https://imp.i115008.net/rn2nyy
21. Python for AI
โ๏ธ https://deeplearning.ai/short-courses/ai-python-for-beginners/
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Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
โค1
๐ Data Analyst Roadmap (2025)
Master the Skills That Top Companies Are Hiring For!
๐ 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
๐ 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
๐ 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
๐ 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
๐ 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
๐ 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
๐ 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
๐ 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
๐ 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
๐ 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
โจ React โค๏ธ for more
Master the Skills That Top Companies Are Hiring For!
๐ 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
๐ 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
๐ 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
๐ 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
๐ 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
๐ 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
๐ 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
๐ 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
๐ 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
๐ 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
โจ React โค๏ธ for more
โค1
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
โข Python for Data Science โ Pandas, NumPy, Matplotlib, Seaborn
โข SQL for Data Extraction โ SELECT, JOIN, GROUP BY, CTEs, Window Functions
โข Data Cleaning & Preprocessing โ Handling missing data, outliers, duplicates
โข Exploratory Data Analysis (EDA) โ Visualizing data trends
Machine Learning (ML):
โข Supervised Learning โ Linear Regression, Decision Trees, Random Forest
โข Unsupervised Learning โ Clustering, PCA, Anomaly Detection
โข Model Evaluation โ Cross-validation, Confusion Matrix, ROC-AUC
โข Hyperparameter Tuning โ Grid Search, Random Search
Deep Learning (DL):
โข Neural Networks โ TensorFlow, PyTorch, Keras
โข CNNs & RNNs โ Image & sequential data processing
โข Transformers & LLMs โ GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
โข Hadoop & Spark โ Handling large datasets
โข AWS, GCP, Azure โ Cloud-based data science solutions
โข MLOps โ Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
โข Probability & Hypothesis Testing โ P-values, T-tests, Chi-square
โข Linear Algebra & Calculus โ Matrices, Vectors, Derivatives
โข Time Series Analysis โ ARIMA, Prophet, LSTMs
Real-World Applications:
โข Recommendation Systems โ Personalized AI suggestions
โข NLP (Natural Language Processing) โ Sentiment Analysis, Chatbots
โข AI-Powered Business Insights โ Data-driven decision-making
React โค๏ธ for more
Core Data Science Skills:
โข Python for Data Science โ Pandas, NumPy, Matplotlib, Seaborn
โข SQL for Data Extraction โ SELECT, JOIN, GROUP BY, CTEs, Window Functions
โข Data Cleaning & Preprocessing โ Handling missing data, outliers, duplicates
โข Exploratory Data Analysis (EDA) โ Visualizing data trends
Machine Learning (ML):
โข Supervised Learning โ Linear Regression, Decision Trees, Random Forest
โข Unsupervised Learning โ Clustering, PCA, Anomaly Detection
โข Model Evaluation โ Cross-validation, Confusion Matrix, ROC-AUC
โข Hyperparameter Tuning โ Grid Search, Random Search
Deep Learning (DL):
โข Neural Networks โ TensorFlow, PyTorch, Keras
โข CNNs & RNNs โ Image & sequential data processing
โข Transformers & LLMs โ GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
โข Hadoop & Spark โ Handling large datasets
โข AWS, GCP, Azure โ Cloud-based data science solutions
โข MLOps โ Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
โข Probability & Hypothesis Testing โ P-values, T-tests, Chi-square
โข Linear Algebra & Calculus โ Matrices, Vectors, Derivatives
โข Time Series Analysis โ ARIMA, Prophet, LSTMs
Real-World Applications:
โข Recommendation Systems โ Personalized AI suggestions
โข NLP (Natural Language Processing) โ Sentiment Analysis, Chatbots
โข AI-Powered Business Insights โ Data-driven decision-making
React โค๏ธ for more
โค1
Forwarded from SQL Programming Resources
๐ฐ ๐๐ฒ๐๐ ๐๐ฟ๐ฒ๐ฒ ๐ฆ๐ค๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐๐
Want to break into Data Analytics?๐ซ
It all starts with SQL โ the language every data analyst needs to master. Whether youโre analyzing trends, pulling business reports, or cleaning datasets, SQL is at the heart of it all๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44oj5Ds
Perfect for students, freshers, job seekers, or anyone transitioning into techโ ๏ธ
Want to break into Data Analytics?๐ซ
It all starts with SQL โ the language every data analyst needs to master. Whether youโre analyzing trends, pulling business reports, or cleaning datasets, SQL is at the heart of it all๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44oj5Ds
Perfect for students, freshers, job seekers, or anyone transitioning into techโ ๏ธ
Future-Proof Skills for Data Analysts in 2025 & Beyond
1๏ธโฃ AI-Powered Analytics ๐ค Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2๏ธโฃ Generative AI for Data Analysis ๐ง Use AI for generating SQL queries, writing Python scripts, and automating data storytelling.
3๏ธโฃ Real-Time Data Processing โก Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4๏ธโฃ DataOps & MLOps ๐ Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5๏ธโฃ Knowledge of Graph Databases ๐ Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6๏ธโฃ Advanced Data Privacy & Ethics ๐ Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7๏ธโฃ No-Code & Low-Code Analytics ๐ ๏ธ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8๏ธโฃ API & Web Scraping Skills ๐ Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9๏ธโฃ Cross-Disciplinary Collaboration ๐ค Work with product managers, engineers, and business leaders to drive data-driven strategies.
๐ Continuous Learning & Adaptability ๐ Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1๏ธโฃ AI-Powered Analytics ๐ค Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2๏ธโฃ Generative AI for Data Analysis ๐ง Use AI for generating SQL queries, writing Python scripts, and automating data storytelling.
3๏ธโฃ Real-Time Data Processing โก Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4๏ธโฃ DataOps & MLOps ๐ Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5๏ธโฃ Knowledge of Graph Databases ๐ Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6๏ธโฃ Advanced Data Privacy & Ethics ๐ Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7๏ธโฃ No-Code & Low-Code Analytics ๐ ๏ธ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8๏ธโฃ API & Web Scraping Skills ๐ Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9๏ธโฃ Cross-Disciplinary Collaboration ๐ค Work with product managers, engineers, and business leaders to drive data-driven strategies.
๐ Continuous Learning & Adaptability ๐ Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐ฏ๐ ๐๐ป ๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ | Across India๐
Companies Hiring:- Google, Microsoft, Cognizant, Infosys, TCS & Many More
Roles:- Data Analysts ,Data Scientits ,Software Engineers & Other roles
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
โ Start applying to jobs that fit your profile and boost your career growth!
Companies Hiring:- Google, Microsoft, Cognizant, Infosys, TCS & Many More
Roles:- Data Analysts ,Data Scientits ,Software Engineers & Other roles
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
โ Start applying to jobs that fit your profile and boost your career growth!
SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.
Letโs dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
๐ญ. ๐ฆ๐ค๐ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
๐ฎ. ๐ฆ๐ค๐ ๐ถ๐ป ๐๐ฎ๐-๐๐ผ-๐๐ฎ๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฆ๐ค๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
Letโs dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
๐ญ. ๐ฆ๐ค๐ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
๐ฎ. ๐ฆ๐ค๐ ๐ถ๐ป ๐๐ฎ๐-๐๐ผ-๐๐ฎ๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฆ๐ค๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
โค1
๐ง๐ผ๐ฝ ๐ฑ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ๐ฆ๐ฐ๐ฟ๐ถ๐ฝ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Learning JavaScript doesnโt have to be boring anymore!๐ซ
If endless tutorials make your eyes glaze over, weโve got just the thing โ these super fun & interactive platforms turn learning JavaScript into a game๐จโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3T4yYbP
Perfect for daily practice, weekend sprints, or anyone who learns better with hands-on interaction!9โ ๏ธ
Learning JavaScript doesnโt have to be boring anymore!๐ซ
If endless tutorials make your eyes glaze over, weโve got just the thing โ these super fun & interactive platforms turn learning JavaScript into a game๐จโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3T4yYbP
Perfect for daily practice, weekend sprints, or anyone who learns better with hands-on interaction!9โ ๏ธ
SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1๏ธโฃ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2๏ธโฃ Basic SQL Commands
Let's start with some fundamental queries:
๐น SELECT โ Retrieve Data
๐น WHERE โ Filter Data
๐น ORDER BY โ Sort Data
๐น LIMIT โ Restrict Number of Results
๐น DISTINCT โ Remove Duplicates
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
๐๐
https://t.iss.one/mysqldata
Like this post if you want me to continue covering all the topics! ๐โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#sql
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1๏ธโฃ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2๏ธโฃ Basic SQL Commands
Let's start with some fundamental queries:
๐น SELECT โ Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns
๐น WHERE โ Filter Data
SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary
๐น ORDER BY โ Sort Data
SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first)
๐น LIMIT โ Restrict Number of Results
SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees
๐น DISTINCT โ Remove Duplicates
SELECT DISTINCT department FROM employees; -- Show unique departments
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
๐๐
https://t.iss.one/mysqldata
Like this post if you want me to continue covering all the topics! ๐โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#sql
โค1