Artificial Intelligence for Robotics.epub
24 MB
Artificial Intelligence for Robotics
Francis X. Govers, 2018
Francis X. Govers, 2018
Ultimate ChatGPT Handbook for Enterprises.pdf
18.3 MB
Ultimate ChatGPT Handbook for Enterprises
Harald Gunia, 2024
Harald Gunia, 2024
π5
Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more πβ€οΈ
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more πβ€οΈ
π26β€12
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:
β’ Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
β’ Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
β’ 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.
β’ Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.
β’ Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.
β’ 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.
π4
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.
π6β€2
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 πβ€οΈ
ENJOY LEARNING ππ
- 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 πβ€οΈ
ENJOY LEARNING ππ
π8
Important Topics to become a data scientist
[Advanced Level]
ππ
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 ππ
[Advanced Level]
ππ
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 ππ
π8β€3
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 Silverβs forecasting predicted state outcomes, while Obamaβs 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 βdo-not-disturbβ voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecastingβs 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 Silverβs forecasting predicted state outcomes, while Obamaβs 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 βdo-not-disturbβ voters and improving ad spending effectiveness by 18%.
Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecastingβs broad predictions.
π1
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 β 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 β 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
π1
7 best GitHub repositories to break into data analytics and data science:
1. 100-Days-Of-ML-Code
- ππ’π§π€: (https://lnkd.in/dcftdA57)
- ππππ«π¬: ~42k
2. awesome-datascience
- ππ’π§π€: (https://lnkd.in/dcFYYwx9)
- ππππ«π¬: ~22.7k
3. Data-Science-For-Beginners
- ππ’π§π€: (https://lnkd.in/d_zZBadF)
- ππππ«π¬: ~14.5k
4. data-science-interviews
- ππ’π§π€: (https://lnkd.in/dkN4RZjH)
- ππππ«π¬: ~5.8k
5. Coding and ML System Design
- ππ’π§π€: (https://lnkd.in/gXFaaaQR)
- ππππ«π¬: ~3.5k
6. Machine Learning Interviews from MAANG
- ππ’π§π€: https://lnkd.in/gq_huuZD
- ππππ«π¬: 8.1k
7. data-science-ipython-notebooks
- ππ’π§π€: (https://lnkd.in/dPmQuPB9)
- ππππ«π¬: ~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
- ππ’π§π€: (https://lnkd.in/dcftdA57)
- ππππ«π¬: ~42k
2. awesome-datascience
- ππ’π§π€: (https://lnkd.in/dcFYYwx9)
- ππππ«π¬: ~22.7k
3. Data-Science-For-Beginners
- ππ’π§π€: (https://lnkd.in/d_zZBadF)
- ππππ«π¬: ~14.5k
4. data-science-interviews
- ππ’π§π€: (https://lnkd.in/dkN4RZjH)
- ππππ«π¬: ~5.8k
5. Coding and ML System Design
- ππ’π§π€: (https://lnkd.in/gXFaaaQR)
- ππππ«π¬: ~3.5k
6. Machine Learning Interviews from MAANG
- ππ’π§π€: https://lnkd.in/gq_huuZD
- ππππ«π¬: 8.1k
7. data-science-ipython-notebooks
- ππ’π§π€: (https://lnkd.in/dPmQuPB9)
- ππππ«π¬: ~27.2k
These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science.
π5
7 best Telegram Channels to break into data analytics and data science:
1. Data Science & Machine Learning
- ππ’π§π€: (https://t.iss.one/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- ππ’π§π€: (https://t.iss.one/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- ππ’π§π€: (https://t.iss.one/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- ππ’π§π€: (t.iss.one/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- ππ’π§π€: (https://t.iss.one/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- ππ’π§π€: (https://t.iss.one/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- ππ’π§π€: (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
- ππ’π§π€: (https://t.iss.one/datasciencefun)
- Subscribers: ~48k
2. Python for Data Analysts
- ππ’π§π€: (https://t.iss.one/pythonanalyst)
- Subscribers: ~34.8k
3. SQL For Data Analytics
- ππ’π§π€: (https://t.iss.one/sqlanalyst)
- Subscribers: ~58.9k
4. Power BI & Tableau
- ππ’π§π€: (t.iss.one/PowerBI_analyst)
- Subscribers: ~36.1k
5. Artificial Intelligence
- ππ’π§π€: (https://t.iss.one/machinelearning_deeplearning)
- Subscribers: ~28.7k
6. Coding Interviews
- ππ’π§π€: (https://t.iss.one/crackingthecodinginterview)
- Subscribers: 38.6k
7. Data Science Interviews
- ππ’π§π€: (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.
π4β€2
Oil bosses have big hopes for the AI boom
Data centres are fuelling demand for natural gasβfor now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industryβs biggest annual gathering. This yearβs 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 gasβfor now
This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industryβs biggest annual gathering. This yearβs 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.
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Decagon and OpenAI deliver high-performance, fully automated customer support at scale
Launched in 2023, Decagonβ (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. OpenAIβs models are crucial in their ability to deliver fast, reliable responsesβwithout 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 OpenAIβs modelsβincluding GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-miniβto deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
Launched in 2023, Decagonβ (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. OpenAIβs models are crucial in their ability to deliver fast, reliable responsesβwithout 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 OpenAIβs modelsβincluding GPT-3.5, 4, 4o, 4 Turbo, and OpenAI o1-miniβto deliver agentic bots that go beyond response generation and service the entire customer lifecycle.
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