Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:
โข Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
โข Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
โข Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.
But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.
No matter where your path leads, the key is to start now.
โข Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
โข Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
โข Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.
But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.
No matter where your path leads, the key is to start now.
๐4
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.
๐6โค2
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.
Share this channel with your real friends: https://t.iss.one/datasciencefun
Like if you want me to continue this series ๐โค๏ธ
ENJOY LEARNING ๐๐
- 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://t.iss.one/datasciencefun
Like if you want me to continue this series ๐โค๏ธ
ENJOY LEARNING ๐๐
๐8
Important Topics to become a data scientist
[Advanced Level]
๐๐
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 Description
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 ๐๐
[Advanced Level]
๐๐
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 Description
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 ๐๐
๐8โค3
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 Silverโs forecasting predicted state outcomes, while Obamaโs 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 โdo-not-disturbโ voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecastingโs 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 Silverโs forecasting predicted state outcomes, while Obamaโs 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 โdo-not-disturbโ voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecastingโs broad predictions.
๐1
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 โ 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 titled "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 โ 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 titled "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
๐1
7 best GitHub repositories to break into data analytics and data science:
1. 100-Days-Of-ML-Code
- ๐๐ข๐ง๐ค: (https://lnkd.in/dcftdA57)
- ๐๐ญ๐๐ซ๐ฌ: ~42k
2. awesome-datascience
- ๐๐ข๐ง๐ค: (https://lnkd.in/dcFYYwx9)
- ๐๐ญ๐๐ซ๐ฌ: ~22.7k
3. Data-Science-For-Beginners
- ๐๐ข๐ง๐ค: (https://lnkd.in/d_zZBadF)
- ๐๐ญ๐๐ซ๐ฌ: ~14.5k
4. data-science-interviews
- ๐๐ข๐ง๐ค: (https://lnkd.in/dkN4RZjH)
- ๐๐ญ๐๐ซ๐ฌ: ~5.8k
5. Coding and ML System Design
- ๐๐ข๐ง๐ค: (https://lnkd.in/gXFaaaQR)
- ๐๐ญ๐๐ซ๐ฌ: ~3.5k
6. Machine Learning Interviews from MAANG
- ๐๐ข๐ง๐ค: https://lnkd.in/gq_huuZD
- ๐๐ญ๐๐ซ๐ฌ: 8.1k
7. data-science-ipython-notebooks
- ๐๐ข๐ง๐ค: (https://lnkd.in/dPmQuPB9)
- ๐๐ญ๐๐ซ๐ฌ: ~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
- ๐๐ข๐ง๐ค: (https://lnkd.in/dcftdA57)
- ๐๐ญ๐๐ซ๐ฌ: ~42k
2. awesome-datascience
- ๐๐ข๐ง๐ค: (https://lnkd.in/dcFYYwx9)
- ๐๐ญ๐๐ซ๐ฌ: ~22.7k
3. Data-Science-For-Beginners
- ๐๐ข๐ง๐ค: (https://lnkd.in/d_zZBadF)
- ๐๐ญ๐๐ซ๐ฌ: ~14.5k
4. data-science-interviews
- ๐๐ข๐ง๐ค: (https://lnkd.in/dkN4RZjH)
- ๐๐ญ๐๐ซ๐ฌ: ~5.8k
5. Coding and ML System Design
- ๐๐ข๐ง๐ค: (https://lnkd.in/gXFaaaQR)
- ๐๐ญ๐๐ซ๐ฌ: ~3.5k
6. Machine Learning Interviews from MAANG
- ๐๐ข๐ง๐ค: https://lnkd.in/gq_huuZD
- ๐๐ญ๐๐ซ๐ฌ: 8.1k
7. data-science-ipython-notebooks
- ๐๐ข๐ง๐ค: (https://lnkd.in/dPmQuPB9)
- ๐๐ญ๐๐ซ๐ฌ: ~27.2k
These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
๐5
7 best Telegram Channels to break into data analytics and data science:
1. Data Science & Machine Learning
- ๐๐ข๐ง๐ค: (https://t.iss.one/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- ๐๐ข๐ง๐ค: (https://t.iss.one/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- ๐๐ข๐ง๐ค: (https://t.iss.one/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- ๐๐ข๐ง๐ค: (t.iss.one/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- ๐๐ข๐ง๐ค: (https://t.iss.one/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- ๐๐ข๐ง๐ค: (https://t.iss.one/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- ๐๐ข๐ง๐ค: (https://t.iss.one/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
- ๐๐ข๐ง๐ค: (https://t.iss.one/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- ๐๐ข๐ง๐ค: (https://t.iss.one/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- ๐๐ข๐ง๐ค: (https://t.iss.one/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- ๐๐ข๐ง๐ค: (t.iss.one/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- ๐๐ข๐ง๐ค: (https://t.iss.one/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- ๐๐ข๐ง๐ค: (https://t.iss.one/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- ๐๐ข๐ง๐ค: (https://t.iss.one/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.
๐4โค2
Oil bosses have big hopes for the AI boom
Data centres are fuelling demand for natural gasโfor now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industryโs biggest annual gathering. This yearโs 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 gasโfor now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industryโs biggest annual gathering. This yearโs 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.
๐2
Decagon and OpenAI deliver high-performance, fully automated customer support at scale
Launched in 2023, Decagonโ (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. OpenAIโs models are crucial in their ability to deliver fast, reliable responsesโwithout 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 OpenAIโs modelsโincluding GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-miniโto deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
Launched in 2023, Decagonโ (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. OpenAIโs models are crucial in their ability to deliver fast, reliable responsesโwithout 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 OpenAIโs modelsโincluding GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-miniโto deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
โค1๐1
โ
๐-๐๐ญ๐๐ฉ ๐๐จ๐๐๐ฆ๐๐ฉ ๐ญ๐จ ๐๐ฐ๐ข๐ญ๐๐ก ๐ข๐ง๐ญ๐จ ๐ญ๐ก๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐
๐ข๐๐ฅ๐โ
๐โโ๏ธ๐๐ฎ๐ข๐ฅ๐ ๐๐๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Focus on core skillsโExcel, SQL, Power BI, and Python.
๐โโ๏ธ๐๐๐ง๐๐ฌ-๐๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: 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.
๐โโ๏ธ๐ ๐ข๐ง๐ ๐ ๐๐๐ง๐ญ๐จ๐ซ: Connect with someone experienced in data analytics for guidance(like me ๐ ). They can provide valuable insights, feedback, and keep you on track.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiterโs eye.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.
๐โโ๏ธ๐๐ฎ๐ข๐ฅ๐ ๐๐๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Focus on core skillsโExcel, SQL, Power BI, and Python.
๐โโ๏ธ๐๐๐ง๐๐ฌ-๐๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: 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.
๐โโ๏ธ๐ ๐ข๐ง๐ ๐ ๐๐๐ง๐ญ๐จ๐ซ: Connect with someone experienced in data analytics for guidance(like me ๐ ). They can provide valuable insights, feedback, and keep you on track.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiterโs eye.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ: 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
๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/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://t.iss.one/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 ๐๐
๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/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://t.iss.one/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 ๐๐
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.
๐3