9 things every beginner programmer should stop doing:
โ Copy-pasting code without understanding it
โฉ Skipping the fundamentals to learn advanced stuff
๐ Rewriting the same code instead of reusing functions
๐ฆ Ignoring file/folder structure in projects
โ ๏ธ Not handling errors or exceptions
๐ง Memorizing syntax instead of learning logic
โณ Waiting for the โperfect ideaโ to start coding
๐ Jumping between tutorials without building anything
๐ค Giving up too early when things get hard
#coding #tips
โ Copy-pasting code without understanding it
โฉ Skipping the fundamentals to learn advanced stuff
๐ Rewriting the same code instead of reusing functions
๐ฆ Ignoring file/folder structure in projects
โ ๏ธ Not handling errors or exceptions
๐ง Memorizing syntax instead of learning logic
โณ Waiting for the โperfect ideaโ to start coding
๐ Jumping between tutorials without building anything
๐ค Giving up too early when things get hard
#coding #tips
๐6
Top 10 machine Learning algorithms
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
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1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
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๐5
7 Essential Data Science Techniques to Master ๐
Machine Learning for Predictive Modeling
Machine learning is the backbone of predictive analytics. Techniques like linear regression, decision trees, and random forests can help forecast outcomes based on historical data. Whether you're predicting customer churn, stock prices, or sales trends, understanding these models is key to making data-driven predictions.
Feature Engineering to Improve Model Performance
Raw data is rarely ready for analysis. Feature engineering involves creating new variables from your existing data that can improve the performance of your machine learning models. For example, you might transform timestamps into time features (hour, day, month) or create aggregated metrics like moving averages.
Clustering for Data Segmentation
Unsupervised learning techniques like K-Means or DBSCAN are great for grouping similar data points together without predefined labels. This is perfect for tasks like customer segmentation, market basket analysis, or anomaly detection, where patterns are hidden in your data that you need to uncover.
Time Series Forecasting
Predicting future events based on historical data is one of the most common tasks in data science. Time series forecasting methods like ARIMA, Exponential Smoothing, or Facebook Prophet allow you to capture seasonal trends, cycles, and long-term patterns in time-dependent data.
Natural Language Processing (NLP)
NLP techniques are used to analyze and extract insights from text data. Key applications include sentiment analysis, topic modeling, and named entity recognition (NER). NLP is particularly useful for analyzing customer feedback, reviews, or social media data.
Dimensionality Reduction with PCA
When working with high-dimensional data, reducing the number of variables without losing important information can improve the performance of machine learning models. Principal Component Analysis (PCA) is a popular technique to achieve this by projecting the data into a lower-dimensional space that captures the most variance.
Anomaly Detection for Identifying Outliers
Detecting unusual patterns or anomalies in data is essential for tasks like fraud detection, quality control, and system monitoring. Techniques like Isolation Forest, One-Class SVM, and Autoencoders are commonly used in data science to detect outliers in both supervised and unsupervised contexts.
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Machine Learning for Predictive Modeling
Machine learning is the backbone of predictive analytics. Techniques like linear regression, decision trees, and random forests can help forecast outcomes based on historical data. Whether you're predicting customer churn, stock prices, or sales trends, understanding these models is key to making data-driven predictions.
Feature Engineering to Improve Model Performance
Raw data is rarely ready for analysis. Feature engineering involves creating new variables from your existing data that can improve the performance of your machine learning models. For example, you might transform timestamps into time features (hour, day, month) or create aggregated metrics like moving averages.
Clustering for Data Segmentation
Unsupervised learning techniques like K-Means or DBSCAN are great for grouping similar data points together without predefined labels. This is perfect for tasks like customer segmentation, market basket analysis, or anomaly detection, where patterns are hidden in your data that you need to uncover.
Time Series Forecasting
Predicting future events based on historical data is one of the most common tasks in data science. Time series forecasting methods like ARIMA, Exponential Smoothing, or Facebook Prophet allow you to capture seasonal trends, cycles, and long-term patterns in time-dependent data.
Natural Language Processing (NLP)
NLP techniques are used to analyze and extract insights from text data. Key applications include sentiment analysis, topic modeling, and named entity recognition (NER). NLP is particularly useful for analyzing customer feedback, reviews, or social media data.
Dimensionality Reduction with PCA
When working with high-dimensional data, reducing the number of variables without losing important information can improve the performance of machine learning models. Principal Component Analysis (PCA) is a popular technique to achieve this by projecting the data into a lower-dimensional space that captures the most variance.
Anomaly Detection for Identifying Outliers
Detecting unusual patterns or anomalies in data is essential for tasks like fraud detection, quality control, and system monitoring. Techniques like Isolation Forest, One-Class SVM, and Autoencoders are commonly used in data science to detect outliers in both supervised and unsupervised contexts.
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๐6๐ฅฐ1
5 Key Steps in Building a Data Science Pipeline ๐๐ง
Data Collection ๐ฅ
The first step is gathering the raw data. This could come from multiple sources like APIs, databases, or even scraping websites. The data needs to be comprehensive, relevant, and high quality to ensure that your analysis yields accurate results.
Data Preprocessing & Cleaning ๐งน
Raw data is often messy and inconsistent. The preprocessing phase involves handling missing values, correcting errors, and removing duplicates. Techniques like normalization, scaling, and encoding categorical variables are also essential at this stage to ensure your models work effectively.
Exploratory Data Analysis (EDA) ๐
EDA helps you understand the structure and patterns in your data before diving deeper. Youโll generate summary statistics, visualizations, and correlation matrices to uncover hidden insights and identify potential problems that need to be addressed during modeling.
Model Selection & Training ๐๏ธโโ๏ธ
Choose the right machine learning algorithms based on the problem at hand, whether itโs classification, regression, or clustering. Train multiple models and fine-tune hyperparameters to find the best-performing one. Techniques like cross-validation are often used to ensure your modelโs reliability.
Model Evaluation & Deployment ๐
Once your model is trained, you need to evaluate its performance using appropriate metrics like accuracy, precision, recall, or F1-score for classification tasks, or RMSE for regression. Once youโve validated the model, deploy it to start making predictions on new data.
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Data Collection ๐ฅ
The first step is gathering the raw data. This could come from multiple sources like APIs, databases, or even scraping websites. The data needs to be comprehensive, relevant, and high quality to ensure that your analysis yields accurate results.
Data Preprocessing & Cleaning ๐งน
Raw data is often messy and inconsistent. The preprocessing phase involves handling missing values, correcting errors, and removing duplicates. Techniques like normalization, scaling, and encoding categorical variables are also essential at this stage to ensure your models work effectively.
Exploratory Data Analysis (EDA) ๐
EDA helps you understand the structure and patterns in your data before diving deeper. Youโll generate summary statistics, visualizations, and correlation matrices to uncover hidden insights and identify potential problems that need to be addressed during modeling.
Model Selection & Training ๐๏ธโโ๏ธ
Choose the right machine learning algorithms based on the problem at hand, whether itโs classification, regression, or clustering. Train multiple models and fine-tune hyperparameters to find the best-performing one. Techniques like cross-validation are often used to ensure your modelโs reliability.
Model Evaluation & Deployment ๐
Once your model is trained, you need to evaluate its performance using appropriate metrics like accuracy, precision, recall, or F1-score for classification tasks, or RMSE for regression. Once youโve validated the model, deploy it to start making predictions on new data.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
๐6โค1
Statistics Roadmap for Data Science!
Phase 1: Fundamentals of Statistics
1๏ธโฃ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics
2๏ธโฃ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions
Phase 2: Intermediate Statistics
3๏ธโฃ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals
4๏ธโฃ Regression Analysis
-Linear Regression
-Diagnostics and Validation
Phase 3: Advanced Topics
5๏ธโฃ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics
6๏ธโฃ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering
Phase 4: Statistical Learning and Machine Learning
7๏ธโฃ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning
Phase 5: Practical Application
8๏ธโฃ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)
9๏ธโฃ Projects and Case Studies
-Capstone Project
-Case Studies
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
Phase 1: Fundamentals of Statistics
1๏ธโฃ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics
2๏ธโฃ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions
Phase 2: Intermediate Statistics
3๏ธโฃ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals
4๏ธโฃ Regression Analysis
-Linear Regression
-Diagnostics and Validation
Phase 3: Advanced Topics
5๏ธโฃ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics
6๏ธโฃ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering
Phase 4: Statistical Learning and Machine Learning
7๏ธโฃ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning
Phase 5: Practical Application
8๏ธโฃ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)
9๏ธโฃ Projects and Case Studies
-Capstone Project
-Case Studies
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
๐8โค3๐1
3 ways to keep your data science skills up-to-date
1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.
2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.
3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.
2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.
3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
๐7
Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Letโs say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns โ โOh, they have four legs, fur, floppy ears...โ
Next time the kid sees a new picture, they might say, โThatโs a dog!โ โ even if theyโve never seen that exact dog before.
Thatโs what machine learning does โ but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like โthis is a dogโ, โthis is not a dogโ).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more โค๏ธ
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Letโs say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns โ โOh, they have four legs, fur, floppy ears...โ
Next time the kid sees a new picture, they might say, โThatโs a dog!โ โ even if theyโve never seen that exact dog before.
Thatโs what machine learning does โ but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like โthis is a dogโ, โthis is not a dogโ).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more โค๏ธ
๐6โค4
Advanced Data Science Concepts ๐
1๏ธโฃ Feature Engineering & Selection
Handling Missing Values โ Imputation techniques (mean, median, KNN).
Encoding Categorical Variables โ One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization โ StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction โ PCA, t-SNE, UMAP, LDA.
2๏ธโฃ Machine Learning Optimization
Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization.
Model Validation โ Cross-validation, Bootstrapping.
Class Imbalance Handling โ SMOTE, Oversampling, Undersampling.
Ensemble Learning โ Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3๏ธโฃ Deep Learning & Neural Networks
Neural Network Architectures โ CNNs, RNNs, Transformers.
Activation Functions โ ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms โ SGD, Adam, RMSprop.
Transfer Learning โ Pre-trained models like BERT, GPT, ResNet.
4๏ธโฃ Time Series Analysis
Forecasting Models โ ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series โ Lag features, Rolling statistics.
Anomaly Detection โ Isolation Forest, Autoencoders.
5๏ธโฃ NLP (Natural Language Processing)
Text Preprocessing โ Tokenization, Stemming, Lemmatization.
Word Embeddings โ Word2Vec, GloVe, FastText.
Sequence Models โ LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis โ TF-IDF, Attention Mechanism.
6๏ธโฃ Computer Vision
Image Processing โ OpenCV, PIL.
Object Detection โ YOLO, Faster R-CNN, SSD.
Image Segmentation โ U-Net, Mask R-CNN.
7๏ธโฃ Reinforcement Learning
Markov Decision Process (MDP) โ Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) โ Policy improvement techniques.
Multi-Agent RL โ Competitive and cooperative learning.
8๏ธโฃ MLOps & Model Deployment
Model Monitoring & Versioning โ MLflow, DVC.
Cloud ML Services โ AWS SageMaker, GCP AI Platform.
API Deployment โ Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic โค๏ธ
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Hope this helps you ๐
1๏ธโฃ Feature Engineering & Selection
Handling Missing Values โ Imputation techniques (mean, median, KNN).
Encoding Categorical Variables โ One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization โ StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction โ PCA, t-SNE, UMAP, LDA.
2๏ธโฃ Machine Learning Optimization
Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization.
Model Validation โ Cross-validation, Bootstrapping.
Class Imbalance Handling โ SMOTE, Oversampling, Undersampling.
Ensemble Learning โ Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3๏ธโฃ Deep Learning & Neural Networks
Neural Network Architectures โ CNNs, RNNs, Transformers.
Activation Functions โ ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms โ SGD, Adam, RMSprop.
Transfer Learning โ Pre-trained models like BERT, GPT, ResNet.
4๏ธโฃ Time Series Analysis
Forecasting Models โ ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series โ Lag features, Rolling statistics.
Anomaly Detection โ Isolation Forest, Autoencoders.
5๏ธโฃ NLP (Natural Language Processing)
Text Preprocessing โ Tokenization, Stemming, Lemmatization.
Word Embeddings โ Word2Vec, GloVe, FastText.
Sequence Models โ LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis โ TF-IDF, Attention Mechanism.
6๏ธโฃ Computer Vision
Image Processing โ OpenCV, PIL.
Object Detection โ YOLO, Faster R-CNN, SSD.
Image Segmentation โ U-Net, Mask R-CNN.
7๏ธโฃ Reinforcement Learning
Markov Decision Process (MDP) โ Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) โ Policy improvement techniques.
Multi-Agent RL โ Competitive and cooperative learning.
8๏ธโฃ MLOps & Model Deployment
Model Monitoring & Versioning โ MLflow, DVC.
Cloud ML Services โ AWS SageMaker, GCP AI Platform.
API Deployment โ Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic โค๏ธ
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Hope this helps you ๐
๐8โค1
Guys, We Did It!
We just crossed 1 Lakh followers on WhatsApp โ and Iโm dropping something massive for you all!
Iโm launching a Data Science Learning Series โ where I will cover essential Data Science & Machine Learning concepts from basic to advanced level covering real-world projects with step-by-step explanations, hands-on examples, and quizzes to test your skills after every major topic.
Hereโs what weโll cover in the coming days:
Week 1: Data Science Foundations
- What is Data Science?
- Where is DS used in real life?
- Data Analyst vs Data Scientist vs ML Engineer
- Tools used in DS (with icons & examples)
- DS Life Cycle (Step-by-step)
- Mini Quiz: Week 1 Topics
Week 2: Python for Data Science (Basics Only)
- Variables, Data Types, Lists, Dicts (with real-world data)
- Loops & Conditional Statements
- Functions (only basics)
- Importing CSV, Viewing Data
- Intro to Pandas DataFrame
- Mini Quiz: Python Topics
Week 3: Data Cleaning & Preparation
- Handling Missing Data
- Duplicates, Outliers (conceptual + pandas code)
- Data Type Conversions
- Renaming Columns, Reindexing
- Combining Datasets
- Mini Quiz: Choose the right method (dropna vs fillna, etc.)
Week 4: Data Exploration & Visualization
- Descriptive Stats (mean, median, std)
- GroupBy, Value_counts
- Visualizing with Pandas (plot, bar, hist)
- Matplotlib & Seaborn (basic use only)
- Correlation & Heatmaps
- Mini Quiz: Match chart type with goal
Week 5: Feature Engineering + Intro to ML
What is Feature Engineering?
Encoding (Label, One-Hot), Scaling
Train-Test Split, ML Pipeline
Supervised vs Unsupervised
Linear Regression: Concept Only
Mini Quiz: Regression or Classification?
Week 6: Model Building & Evaluation
- Train a Linear Regression Model
- Logistic Regression (basic example)
- Model Evaluation (Accuracy, Precision, Recall)
- Confusion Matrix (explanation)
- Overfitting & Underfitting (concepts)
- Mini Quiz: Model Evaluation Scenarios
Week 7: Real-World Projects
- Project 1: Predict House Prices
- Project 2: Classify Emails as Spam
- Project 3: Explore Titanic Dataset
- How to structure your project
- What to upload on GitHub
- Mini Quiz: Whatโs missing in this project?
Week 8: Career Boost Week
- Resume Tips for DS Roles
- Portfolio Tips (GitHub/Notion/PDF)
- Best Platforms to Apply (Internship + Job)
- 15 Most Common DS Interview Qs
- Mock Interview Questions for Practice
- Final Recap Quiz
React with โค๏ธ if you're ready for this new journey
Join our WhatsApp channel now: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
We just crossed 1 Lakh followers on WhatsApp โ and Iโm dropping something massive for you all!
Iโm launching a Data Science Learning Series โ where I will cover essential Data Science & Machine Learning concepts from basic to advanced level covering real-world projects with step-by-step explanations, hands-on examples, and quizzes to test your skills after every major topic.
Hereโs what weโll cover in the coming days:
Week 1: Data Science Foundations
- What is Data Science?
- Where is DS used in real life?
- Data Analyst vs Data Scientist vs ML Engineer
- Tools used in DS (with icons & examples)
- DS Life Cycle (Step-by-step)
- Mini Quiz: Week 1 Topics
Week 2: Python for Data Science (Basics Only)
- Variables, Data Types, Lists, Dicts (with real-world data)
- Loops & Conditional Statements
- Functions (only basics)
- Importing CSV, Viewing Data
- Intro to Pandas DataFrame
- Mini Quiz: Python Topics
Week 3: Data Cleaning & Preparation
- Handling Missing Data
- Duplicates, Outliers (conceptual + pandas code)
- Data Type Conversions
- Renaming Columns, Reindexing
- Combining Datasets
- Mini Quiz: Choose the right method (dropna vs fillna, etc.)
Week 4: Data Exploration & Visualization
- Descriptive Stats (mean, median, std)
- GroupBy, Value_counts
- Visualizing with Pandas (plot, bar, hist)
- Matplotlib & Seaborn (basic use only)
- Correlation & Heatmaps
- Mini Quiz: Match chart type with goal
Week 5: Feature Engineering + Intro to ML
What is Feature Engineering?
Encoding (Label, One-Hot), Scaling
Train-Test Split, ML Pipeline
Supervised vs Unsupervised
Linear Regression: Concept Only
Mini Quiz: Regression or Classification?
Week 6: Model Building & Evaluation
- Train a Linear Regression Model
- Logistic Regression (basic example)
- Model Evaluation (Accuracy, Precision, Recall)
- Confusion Matrix (explanation)
- Overfitting & Underfitting (concepts)
- Mini Quiz: Model Evaluation Scenarios
Week 7: Real-World Projects
- Project 1: Predict House Prices
- Project 2: Classify Emails as Spam
- Project 3: Explore Titanic Dataset
- How to structure your project
- What to upload on GitHub
- Mini Quiz: Whatโs missing in this project?
Week 8: Career Boost Week
- Resume Tips for DS Roles
- Portfolio Tips (GitHub/Notion/PDF)
- Best Platforms to Apply (Internship + Job)
- 15 Most Common DS Interview Qs
- Mock Interview Questions for Practice
- Final Recap Quiz
React with โค๏ธ if you're ready for this new journey
Join our WhatsApp channel now: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
โค12๐2
Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
โค5๐5
Essential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐5
This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
๐4โค2
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
Like this post if you need a complete tutorial on essential data science topics! ๐โค๏ธ
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
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
Like this post if you need a complete tutorial on essential data science topics! ๐โค๏ธ
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค6๐5
5 Algorithms you must know as a data scientist ๐ฉโ๐ป ๐งโ๐ป
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
๐3
๐ Complete Roadmap to Become a Data Scientist in 5 Months
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐10โค3
Amazon Interview Process for Data Scientist position
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐6
Guys, Big Announcement!
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
โค2๐2
Some important questions to crack data science interview
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
ENJOY LEARNING ๐๐
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
ENJOY LEARNING ๐๐
โค9๐4