To be GOOD in Data Science you need to learn:
- Python
- SQL
- PowerBI
To be GREAT in Data Science you need to add:
- Business Understanding
- Knowledge of Cloud
- Many-many projects
But to LAND a job in Data Science you need to prove you can:
- Learn new things
- Communicate clearly
- Solve problems
#datascience
- Python
- SQL
- PowerBI
To be GREAT in Data Science you need to add:
- Business Understanding
- Knowledge of Cloud
- Many-many projects
But to LAND a job in Data Science you need to prove you can:
- Learn new things
- Communicate clearly
- Solve problems
#datascience
❤9👍2
Common Machine Learning Algorithms!
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
👍7
If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
❤21
Data Science isn't easy!
It’s the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fast—keep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
💡 Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
⏳ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
It’s the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fast—keep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
💡 Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
⏳ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
👍8❤2
Hey Guys👋,
The Average Salary Of a Data Scientist is 14LPA
𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍
We help you master the required skills.
Learn by doing, build Industry level projects
Register now for FREE👇 :
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Only few slots are available for FREE, join fast
ENJOY LEARNING 👍👍
The Average Salary Of a Data Scientist is 14LPA
𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍
We help you master the required skills.
Learn by doing, build Industry level projects
Register now for FREE👇 :
https://tracking.acciojob.com/g/PUfdDxgHR
Only few slots are available for FREE, join fast
ENJOY LEARNING 👍👍
👍5❤2
Time Complexity of 10 Most Popular ML Algorithms
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4️⃣ K-Nearest Neighbours (KNN) is simple but can become slow with large datasets due to distance calculations.
5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
6️⃣ Support Vector Machines (SVMs) – Training an SVM with a linear kernel has a time complexity of O(n²), while non-linear kernels (like RBF) can take O(n³), making them slow for large datasets. However, linear SVMs work well for high-dimensional but sparse data.
7️⃣ K-Means Clustering – The standard Lloyd’s algorithm has a time complexity of O(n * k * i * d), where n is the number of data points, k is the number of clusters, i is the number of iterations, and d is the number of dimensions. Convergence speed depends on initialization methods.
8️⃣ Principal Component Analysis (PCA) – PCA involves eigenvalue decomposition of the covariance matrix, leading to a time complexity of O(d³) + O(n * d²). It becomes computationally expensive for very high-dimensional data.
9️⃣ Neural Networks (Deep Learning) – The training complexity varies based on architecture but typically falls in the range of O(n * d * h) per iteration, where h is the number of hidden units. Large networks require GPUs or TPUs for efficient training.
🔟 Gradient Boosting (e.g., XGBoost, LightGBM, CatBoost) – Training complexity is O(n * d * log(n)) per iteration, making it slower than decision trees but highly efficient with optimizations like histogram-based learning.
Understanding these complexities helps in choosing the right algorithm based on dataset size, feature dimensions, and computational resources. 🚀
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4️⃣ K-Nearest Neighbours (KNN) is simple but can become slow with large datasets due to distance calculations.
5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
6️⃣ Support Vector Machines (SVMs) – Training an SVM with a linear kernel has a time complexity of O(n²), while non-linear kernels (like RBF) can take O(n³), making them slow for large datasets. However, linear SVMs work well for high-dimensional but sparse data.
7️⃣ K-Means Clustering – The standard Lloyd’s algorithm has a time complexity of O(n * k * i * d), where n is the number of data points, k is the number of clusters, i is the number of iterations, and d is the number of dimensions. Convergence speed depends on initialization methods.
8️⃣ Principal Component Analysis (PCA) – PCA involves eigenvalue decomposition of the covariance matrix, leading to a time complexity of O(d³) + O(n * d²). It becomes computationally expensive for very high-dimensional data.
9️⃣ Neural Networks (Deep Learning) – The training complexity varies based on architecture but typically falls in the range of O(n * d * h) per iteration, where h is the number of hidden units. Large networks require GPUs or TPUs for efficient training.
🔟 Gradient Boosting (e.g., XGBoost, LightGBM, CatBoost) – Training complexity is O(n * d * log(n)) per iteration, making it slower than decision trees but highly efficient with optimizations like histogram-based learning.
Understanding these complexities helps in choosing the right algorithm based on dataset size, feature dimensions, and computational resources. 🚀
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
👍10❤4🤩2
Data Scientists & Analysts – Let’s Talk About Mistakes!
Most people focus on learning new skills, but avoiding bad habits is just as important.
Here are 7 common mistakes that are slowing down your data career (and how to fix them):
1. Only Learning Tools, Not Problem-Solving
SQL, Python, Power BI… great. But can you actually solve business problems?
Tools change. Thinking like a problem-solver will always make you valuable.
2. Writing Messy, Hard-to-Read Code
Your future self (or your team) should understand your code instantly.
❌ Overly complex logic
❌ No comments or structure
❌ Hardcoded values everywhere
Clean, structured code = professional.
3. Ignoring Data Storytelling
You found a key insight—now what?
If you can’t communicate it effectively, decision-makers won’t act on it.
Learn to simplify, visualize, and tell a compelling data story.
4. Avoiding SQL & Relying Too Much on Excel
Yes, Excel is powerful, but SQL is non-negotiable for working with large datasets.
Stop dragging data into Excel—query it directly and automate your workflow.
5. Overcomplicating Models Instead of Improving Data
A simple model with clean data beats a complex one with garbage input.
Before tweaking algorithms, focus on:
✅ Cleaning & preprocessing
✅ Handling missing values
✅ Understanding the dataset deeply
6. Not Asking “Why?” Enough
You pulled some numbers. Cool. But why do they matter?
Great analysts dig deeper:
✅ Why is revenue dropping?
✅ Why are users churning?
✅ Why does this pattern exist?
Asking “why” makes you 10x better.
7. Ignoring Soft Skills & Networking
Being good at data is great. But if no one knows you exist, you’ll get stuck.
✅ Engage on LinkedIn/Twitter
✅ Share insights & projects
✅ Network with peers & mentors
Opportunities come from people, not just skills.
🔥 The Bottom Line?
Being a great data professional isn’t just about technical skills—it’s about thinking, communicating, and solving problems.
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
Most people focus on learning new skills, but avoiding bad habits is just as important.
Here are 7 common mistakes that are slowing down your data career (and how to fix them):
1. Only Learning Tools, Not Problem-Solving
SQL, Python, Power BI… great. But can you actually solve business problems?
Tools change. Thinking like a problem-solver will always make you valuable.
2. Writing Messy, Hard-to-Read Code
Your future self (or your team) should understand your code instantly.
❌ Overly complex logic
❌ No comments or structure
❌ Hardcoded values everywhere
Clean, structured code = professional.
3. Ignoring Data Storytelling
You found a key insight—now what?
If you can’t communicate it effectively, decision-makers won’t act on it.
Learn to simplify, visualize, and tell a compelling data story.
4. Avoiding SQL & Relying Too Much on Excel
Yes, Excel is powerful, but SQL is non-negotiable for working with large datasets.
Stop dragging data into Excel—query it directly and automate your workflow.
5. Overcomplicating Models Instead of Improving Data
A simple model with clean data beats a complex one with garbage input.
Before tweaking algorithms, focus on:
✅ Cleaning & preprocessing
✅ Handling missing values
✅ Understanding the dataset deeply
6. Not Asking “Why?” Enough
You pulled some numbers. Cool. But why do they matter?
Great analysts dig deeper:
✅ Why is revenue dropping?
✅ Why are users churning?
✅ Why does this pattern exist?
Asking “why” makes you 10x better.
7. Ignoring Soft Skills & Networking
Being good at data is great. But if no one knows you exist, you’ll get stuck.
✅ Engage on LinkedIn/Twitter
✅ Share insights & projects
✅ Network with peers & mentors
Opportunities come from people, not just skills.
🔥 The Bottom Line?
Being a great data professional isn’t just about technical skills—it’s about thinking, communicating, and solving problems.
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
❤3👍3