Let's start with the topics we gonna cover in this 30 Days of Data Science Series,
We will primarily focus on learning Data Science and Machine Learning Algorithms
Day 1: Linear Regression
- Concept: Predict continuous values.
- Implementation: Ordinary Least Squares.
- Evaluation: R-squared, RMSE.
Day 2: Logistic Regression
- Concept: Binary classification.
- Implementation: Sigmoid function.
- Evaluation: Confusion matrix, ROC-AUC.
Day 3: Decision Trees
- Concept: Tree-based model for classification/regression.
- Implementation: Recursive splitting.
- Evaluation: Accuracy, Gini impurity.
Day 4: Random Forest
- Concept: Ensemble of decision trees.
- Implementation: Bagging.
- Evaluation: Out-of-bag error, feature importance.
Day 5: Gradient Boosting
- Concept: Sequential ensemble method.
- Implementation: Boosting.
- Evaluation: Learning rate, number of estimators.
Day 6: Support Vector Machines (SVM)
- Concept: Classification using hyperplanes.
- Implementation: Kernel trick.
- Evaluation: Margin maximization, support vectors.
Day 7: k-Nearest Neighbors (k-NN)
- Concept: Instance-based learning.
- Implementation: Distance metrics.
- Evaluation: k-value tuning, distance functions.
Day 8: Naive Bayes
- Concept: Probabilistic classifier.
- Implementation: Bayes' theorem.
- Evaluation: Prior probabilities, likelihood.
Day 9: k-Means Clustering
- Concept: Partitioning data into k clusters.
- Implementation: Centroid initialization.
- Evaluation: Inertia, silhouette score.
Day 10: Hierarchical Clustering
- Concept: Nested clusters.
- Implementation: Agglomerative method.
- Evaluation: Dendrograms, linkage methods.
Day 11: Principal Component Analysis (PCA)
- Concept: Dimensionality reduction.
- Implementation: Eigenvectors, eigenvalues.
- Evaluation: Explained variance.
Day 12: Association Rule Learning
- Concept: Discover relationships between variables.
- Implementation: Apriori algorithm.
- Evaluation: Support, confidence, lift.
Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Concept: Density-based clustering.
- Implementation: Epsilon, min samples.
- Evaluation: Core points, noise points.
Day 14: Linear Discriminant Analysis (LDA)
- Concept: Linear combination for classification.
- Implementation: Fisher's criterion.
- Evaluation: Class separability.
Day 15: XGBoost
- Concept: Extreme Gradient Boosting.
- Implementation: Tree boosting.
- Evaluation: Regularization, parallel processing.
Day 16: LightGBM
- Concept: Gradient boosting framework.
- Implementation: Leaf-wise growth.
- Evaluation: Speed, accuracy.
Day 17: CatBoost
- Concept: Gradient boosting with categorical features.
- Implementation: Ordered boosting.
- Evaluation: Handling of categorical data.
Day 18: Neural Networks
- Concept: Layers of neurons for learning.
- Implementation: Backpropagation.
- Evaluation: Activation functions, epochs.
Day 19: Convolutional Neural Networks (CNNs)
- Concept: Image processing.
- Implementation: Convolutions, pooling.
- Evaluation: Feature maps, filters.
Day 20: Recurrent Neural Networks (RNNs)
- Concept: Sequential data processing.
- Implementation: Hidden states.
- Evaluation: Long-term dependencies.
Day 21: Long Short-Term Memory (LSTM)
- Concept: Improved RNN.
- Implementation: Memory cells.
- Evaluation: Forget gates, output gates.
Day 22: Gated Recurrent Units (GRU)
- Concept: Simplified LSTM.
- Implementation: Update gate.
- Evaluation: Performance, complexity.
Day 23: Autoencoders
- Concept: Data compression.
- Implementation: Encoder, decoder.
- Evaluation: Reconstruction error.
Day 24: Generative Adversarial Networks (GANs)
- Concept: Generative models.
- Implementation: Generator, discriminator.
- Evaluation: Adversarial loss.
Day 25: Transfer Learning
- Concept: Pre-trained models.
- Implementation: Fine-tuning.
- Evaluation: Domain adaptation.
We will primarily focus on learning Data Science and Machine Learning Algorithms
Day 1: Linear Regression
- Concept: Predict continuous values.
- Implementation: Ordinary Least Squares.
- Evaluation: R-squared, RMSE.
Day 2: Logistic Regression
- Concept: Binary classification.
- Implementation: Sigmoid function.
- Evaluation: Confusion matrix, ROC-AUC.
Day 3: Decision Trees
- Concept: Tree-based model for classification/regression.
- Implementation: Recursive splitting.
- Evaluation: Accuracy, Gini impurity.
Day 4: Random Forest
- Concept: Ensemble of decision trees.
- Implementation: Bagging.
- Evaluation: Out-of-bag error, feature importance.
Day 5: Gradient Boosting
- Concept: Sequential ensemble method.
- Implementation: Boosting.
- Evaluation: Learning rate, number of estimators.
Day 6: Support Vector Machines (SVM)
- Concept: Classification using hyperplanes.
- Implementation: Kernel trick.
- Evaluation: Margin maximization, support vectors.
Day 7: k-Nearest Neighbors (k-NN)
- Concept: Instance-based learning.
- Implementation: Distance metrics.
- Evaluation: k-value tuning, distance functions.
Day 8: Naive Bayes
- Concept: Probabilistic classifier.
- Implementation: Bayes' theorem.
- Evaluation: Prior probabilities, likelihood.
Day 9: k-Means Clustering
- Concept: Partitioning data into k clusters.
- Implementation: Centroid initialization.
- Evaluation: Inertia, silhouette score.
Day 10: Hierarchical Clustering
- Concept: Nested clusters.
- Implementation: Agglomerative method.
- Evaluation: Dendrograms, linkage methods.
Day 11: Principal Component Analysis (PCA)
- Concept: Dimensionality reduction.
- Implementation: Eigenvectors, eigenvalues.
- Evaluation: Explained variance.
Day 12: Association Rule Learning
- Concept: Discover relationships between variables.
- Implementation: Apriori algorithm.
- Evaluation: Support, confidence, lift.
Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Concept: Density-based clustering.
- Implementation: Epsilon, min samples.
- Evaluation: Core points, noise points.
Day 14: Linear Discriminant Analysis (LDA)
- Concept: Linear combination for classification.
- Implementation: Fisher's criterion.
- Evaluation: Class separability.
Day 15: XGBoost
- Concept: Extreme Gradient Boosting.
- Implementation: Tree boosting.
- Evaluation: Regularization, parallel processing.
Day 16: LightGBM
- Concept: Gradient boosting framework.
- Implementation: Leaf-wise growth.
- Evaluation: Speed, accuracy.
Day 17: CatBoost
- Concept: Gradient boosting with categorical features.
- Implementation: Ordered boosting.
- Evaluation: Handling of categorical data.
Day 18: Neural Networks
- Concept: Layers of neurons for learning.
- Implementation: Backpropagation.
- Evaluation: Activation functions, epochs.
Day 19: Convolutional Neural Networks (CNNs)
- Concept: Image processing.
- Implementation: Convolutions, pooling.
- Evaluation: Feature maps, filters.
Day 20: Recurrent Neural Networks (RNNs)
- Concept: Sequential data processing.
- Implementation: Hidden states.
- Evaluation: Long-term dependencies.
Day 21: Long Short-Term Memory (LSTM)
- Concept: Improved RNN.
- Implementation: Memory cells.
- Evaluation: Forget gates, output gates.
Day 22: Gated Recurrent Units (GRU)
- Concept: Simplified LSTM.
- Implementation: Update gate.
- Evaluation: Performance, complexity.
Day 23: Autoencoders
- Concept: Data compression.
- Implementation: Encoder, decoder.
- Evaluation: Reconstruction error.
Day 24: Generative Adversarial Networks (GANs)
- Concept: Generative models.
- Implementation: Generator, discriminator.
- Evaluation: Adversarial loss.
Day 25: Transfer Learning
- Concept: Pre-trained models.
- Implementation: Fine-tuning.
- Evaluation: Domain adaptation.
👍6❤4
Day 26: Reinforcement Learning
- Concept: Learning through interaction.
- Implementation: Q-learning.
- Evaluation: Reward function, policy.
Day 27: Bayesian Networks
- Concept: Probabilistic graphical models.
- Implementation: Conditional dependencies.
- Evaluation: Inference, learning.
Day 28: Hidden Markov Models (HMM)
- Concept: Time series analysis.
- Implementation: Transition probabilities.
- Evaluation: Viterbi algorithm.
Day 29: Feature Selection Techniques
- Concept: Improving model performance.
- Implementation: Filter, wrapper methods.
- Evaluation: Feature importance.
Day 30: Hyperparameter Optimization
- Concept: Model tuning.
- Implementation: Grid search, random search.
- Evaluation: Cross-validation.
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Like if you want me to continue this series 😄❤️
ENJOY LEARNING 👍👍
- Concept: Learning through interaction.
- Implementation: Q-learning.
- Evaluation: Reward function, policy.
Day 27: Bayesian Networks
- Concept: Probabilistic graphical models.
- Implementation: Conditional dependencies.
- Evaluation: Inference, learning.
Day 28: Hidden Markov Models (HMM)
- Concept: Time series analysis.
- Implementation: Transition probabilities.
- Evaluation: Viterbi algorithm.
Day 29: Feature Selection Techniques
- Concept: Improving model performance.
- Implementation: Filter, wrapper methods.
- Evaluation: Feature importance.
Day 30: Hyperparameter Optimization
- Concept: Model tuning.
- Implementation: Grid search, random search.
- Evaluation: Cross-validation.
Share this channel with your real friends: https://news.1rj.ru/str/datasciencefun
Like if you want me to continue this series 😄❤️
ENJOY LEARNING 👍👍
👍8
Important Topics to become a data scientist
[Advanced Level]
👇👇
1. Mathematics
Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification
2. Probability
Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution
3. Statistics
Introduction to Statistics
Data Denoscription
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression
4. Programming
Python:
Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn
R Programming:
R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny
DataBase:
SQL
MongoDB
Data Structures
Web scraping
Linux
Git
5. Machine Learning
How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage
6. Deep Learning
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7. Feature Engineering
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
8. Natural Language Processing
Text Classification
Word Vectors
9. Data Visualization Tools
BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense
10. Deployment
Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django
Join @datasciencefun to learning important data science and machine learning concepts
ENJOY LEARNING 👍👍
[Advanced Level]
👇👇
1. Mathematics
Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification
2. Probability
Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution
3. Statistics
Introduction to Statistics
Data Denoscription
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression
4. Programming
Python:
Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn
R Programming:
R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny
DataBase:
SQL
MongoDB
Data Structures
Web scraping
Linux
Git
5. Machine Learning
How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage
6. Deep Learning
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7. Feature Engineering
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
8. Natural Language Processing
Text Classification
Word Vectors
9. Data Visualization Tools
BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense
10. Deployment
Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django
Join @datasciencefun to learning important data science and machine learning concepts
ENJOY LEARNING 👍👍
👍8❤4
Forecasting vs. Predictive Analytics: The Obama Example
Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting.
Forecasting vs. Predictive Analytics
Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded.
Impact of Predictive Analytics
The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.
Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting.
Forecasting vs. Predictive Analytics
Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded.
Impact of Predictive Analytics
The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.
👏1
The 'bias machine': How Google tells you what you want to hear
"We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question.
Type in "Is Kamala Harris a good Democratic candidate", and Google paints a rosy picture. Search results are constantly changing, but last week, the first link was a Pew Research Center poll showing that "Harris energises Democrats". Next is an Associated Press article noscriptd "Majority of Democrats think Kamala Harris would make a good president", and the following links were similar. But if you've been hearing negative things about Harris, you might ask if she's a "bad" Democratic candidate instead. Fundamentally, that's an identical question, but Google's results are far more pessimistic.
"It's been easy to forget how bad Kamala Harris is," said an article from Reason Magazine in the top spot.
Source-Link: BBC
"We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question.
Type in "Is Kamala Harris a good Democratic candidate", and Google paints a rosy picture. Search results are constantly changing, but last week, the first link was a Pew Research Center poll showing that "Harris energises Democrats". Next is an Associated Press article noscriptd "Majority of Democrats think Kamala Harris would make a good president", and the following links were similar. But if you've been hearing negative things about Harris, you might ask if she's a "bad" Democratic candidate instead. Fundamentally, that's an identical question, but Google's results are far more pessimistic.
"It's been easy to forget how bad Kamala Harris is," said an article from Reason Magazine in the top spot.
Source-Link: BBC
👍1
7 best GitHub repositories to break into data analytics and data science:
1. 100-Days-Of-ML-Code
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcftdA57)
- 𝐒𝐭𝐚𝐫𝐬: ~42k
2. awesome-datascience
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcFYYwx9)
- 𝐒𝐭𝐚𝐫𝐬: ~22.7k
3. Data-Science-For-Beginners
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/d_zZBadF)
- 𝐒𝐭𝐚𝐫𝐬: ~14.5k
4. data-science-interviews
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dkN4RZjH)
- 𝐒𝐭𝐚𝐫𝐬: ~5.8k
5. Coding and ML System Design
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/gXFaaaQR)
- 𝐒𝐭𝐚𝐫𝐬: ~3.5k
6. Machine Learning Interviews from MAANG
- 𝐋𝐢𝐧𝐤: https://lnkd.in/gq_huuZD
- 𝐒𝐭𝐚𝐫𝐬: 8.1k
7. data-science-ipython-notebooks
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dPmQuPB9)
- 𝐒𝐭𝐚𝐫𝐬: ~27.2k
These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
1. 100-Days-Of-ML-Code
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcftdA57)
- 𝐒𝐭𝐚𝐫𝐬: ~42k
2. awesome-datascience
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcFYYwx9)
- 𝐒𝐭𝐚𝐫𝐬: ~22.7k
3. Data-Science-For-Beginners
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/d_zZBadF)
- 𝐒𝐭𝐚𝐫𝐬: ~14.5k
4. data-science-interviews
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dkN4RZjH)
- 𝐒𝐭𝐚𝐫𝐬: ~5.8k
5. Coding and ML System Design
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/gXFaaaQR)
- 𝐒𝐭𝐚𝐫𝐬: ~3.5k
6. Machine Learning Interviews from MAANG
- 𝐋𝐢𝐧𝐤: https://lnkd.in/gq_huuZD
- 𝐒𝐭𝐚𝐫𝐬: 8.1k
7. data-science-ipython-notebooks
- 𝐋𝐢𝐧𝐤: (https://lnkd.in/dPmQuPB9)
- 𝐒𝐭𝐚𝐫𝐬: ~27.2k
These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
👍5
7 best Telegram Channels to break into data analytics and data science:
1. Data Science & Machine Learning
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- 𝐋𝐢𝐧𝐤: (t.me/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/DataScienceInterviews)
- Subscribers: ~12.5k
These channels are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
1. Data Science & Machine Learning
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- 𝐋𝐢𝐧𝐤: (t.me/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- 𝐋𝐢𝐧𝐤: (https://news.1rj.ru/str/DataScienceInterviews)
- Subscribers: ~12.5k
These channels are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
👍4❤2
Oil bosses have big hopes for the AI boom
Data centres are fuelling demand for natural gas—for now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industry’s biggest annual gathering. This year’s focus, perhaps unsurprisingly, was the nexus of artificial intelligence (AI) and energy. On the eve of the jamboree Sultan Al Jaber, chief executive of ADNOC, the Emirati national oil giant, convened a private meeting of big tech and big energy bosses. A survey of some 400 energy, tech and finance bigwigs released in conjunction with the event concluded that AI is set to transform the energy business by boosting efficiency and cutting greenhouse-gas emissions.
Data centres are fuelling demand for natural gas—for now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industry’s biggest annual gathering. This year’s focus, perhaps unsurprisingly, was the nexus of artificial intelligence (AI) and energy. On the eve of the jamboree Sultan Al Jaber, chief executive of ADNOC, the Emirati national oil giant, convened a private meeting of big tech and big energy bosses. A survey of some 400 energy, tech and finance bigwigs released in conjunction with the event concluded that AI is set to transform the energy business by boosting efficiency and cutting greenhouse-gas emissions.
👍2
Decagon and OpenAI deliver high-performance, fully automated customer support at scale
Launched in 2023, Decagon(opens in a new window) has quickly become a key player in automating customer support for companies like Curology, BILT, Duolingo, Eventbrite, Notion, and Substack. OpenAI’s models are crucial in their ability to deliver fast, reliable responses—without human intervention.
From enterprises to tech-forward startups, Decagon helps businesses globally handle millions of support conversations without sacrificing quality or speed. The company uses a combination of OpenAI’s models—including GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-mini—to deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
Launched in 2023, Decagon(opens in a new window) has quickly become a key player in automating customer support for companies like Curology, BILT, Duolingo, Eventbrite, Notion, and Substack. OpenAI’s models are crucial in their ability to deliver fast, reliable responses—without human intervention.
From enterprises to tech-forward startups, Decagon helps businesses globally handle millions of support conversations without sacrificing quality or speed. The company uses a combination of OpenAI’s models—including GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-mini—to deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
❤1👍1
✅𝟓-𝐒𝐭𝐞𝐩 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐒𝐰𝐢𝐭𝐜𝐡 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐅𝐢𝐞𝐥𝐝✅
💁♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.
💁♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.
💁♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.
💁♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.
💁♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.
💁♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.
💁♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.
💁♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.
💁♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.
💁♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.
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Useful websites to practice and enhance your Data Analytics skills
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://news.1rj.ru/str/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://news.1rj.ru/str/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://news.1rj.ru/str/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://news.1rj.ru/str/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://news.1rj.ru/str/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://news.1rj.ru/str/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
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List of companies looking for data analyst freshers
👇👇
https://www.linkedin.com/posts/sqlspecialist_list-of-companies-looking-for-data-analyst-activity-7266347647593009152-0Iwj
👇👇
https://www.linkedin.com/posts/sqlspecialist_list-of-companies-looking-for-data-analyst-activity-7266347647593009152-0Iwj
10 Tools for SQL Developers 🛠📊 -
📄 SQL Server Management Studio (SSMS) - Manage and query SQL Server databases
🌐 phpMyAdmin - Web-based tool for MySQL database management
🔍 DBeaver - Universal database management tool
📊 Tableau - Data visualization and BI tool
⚙️ SQL Workbench/J - Cross-platform SQL query tool
🔐 pgAdmin - Management tool for PostgreSQL
🚀 Azure Data Studio - Lightweight and extensible data tool
📦 Toad for SQL - Database development and administration
📈 Datagrip - JetBrains SQL IDE for various databases
📂 HeidiSQL - Lightweight MySQL and MSSQL client
#SQLTools #DataAnalysis
📄 SQL Server Management Studio (SSMS) - Manage and query SQL Server databases
🌐 phpMyAdmin - Web-based tool for MySQL database management
🔍 DBeaver - Universal database management tool
📊 Tableau - Data visualization and BI tool
⚙️ SQL Workbench/J - Cross-platform SQL query tool
🔐 pgAdmin - Management tool for PostgreSQL
🚀 Azure Data Studio - Lightweight and extensible data tool
📦 Toad for SQL - Database development and administration
📈 Datagrip - JetBrains SQL IDE for various databases
📂 HeidiSQL - Lightweight MySQL and MSSQL client
#SQLTools #DataAnalysis
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