Advanced Data Science Concepts ๐
1๏ธโฃ Feature Engineering & Selection
Handling Missing Values โ Imputation techniques (mean, median, KNN).
Encoding Categorical Variables โ One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization โ StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction โ PCA, t-SNE, UMAP, LDA.
2๏ธโฃ Machine Learning Optimization
Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization.
Model Validation โ Cross-validation, Bootstrapping.
Class Imbalance Handling โ SMOTE, Oversampling, Undersampling.
Ensemble Learning โ Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3๏ธโฃ Deep Learning & Neural Networks
Neural Network Architectures โ CNNs, RNNs, Transformers.
Activation Functions โ ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms โ SGD, Adam, RMSprop.
Transfer Learning โ Pre-trained models like BERT, GPT, ResNet.
4๏ธโฃ Time Series Analysis
Forecasting Models โ ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series โ Lag features, Rolling statistics.
Anomaly Detection โ Isolation Forest, Autoencoders.
5๏ธโฃ NLP (Natural Language Processing)
Text Preprocessing โ Tokenization, Stemming, Lemmatization.
Word Embeddings โ Word2Vec, GloVe, FastText.
Sequence Models โ LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis โ TF-IDF, Attention Mechanism.
6๏ธโฃ Computer Vision
Image Processing โ OpenCV, PIL.
Object Detection โ YOLO, Faster R-CNN, SSD.
Image Segmentation โ U-Net, Mask R-CNN.
7๏ธโฃ Reinforcement Learning
Markov Decision Process (MDP) โ Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) โ Policy improvement techniques.
Multi-Agent RL โ Competitive and cooperative learning.
8๏ธโฃ MLOps & Model Deployment
Model Monitoring & Versioning โ MLflow, DVC.
Cloud ML Services โ AWS SageMaker, GCP AI Platform.
API Deployment โ Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic โค๏ธ
Data Science & Machine Learning Resources: https://t.iss.one/datasciencefun
Hope this helps you ๐
1๏ธโฃ Feature Engineering & Selection
Handling Missing Values โ Imputation techniques (mean, median, KNN).
Encoding Categorical Variables โ One-Hot Encoding, Label Encoding, Target Encoding.
Scaling & Normalization โ StandardScaler, MinMaxScaler, RobustScaler.
Dimensionality Reduction โ PCA, t-SNE, UMAP, LDA.
2๏ธโฃ Machine Learning Optimization
Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization.
Model Validation โ Cross-validation, Bootstrapping.
Class Imbalance Handling โ SMOTE, Oversampling, Undersampling.
Ensemble Learning โ Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.
3๏ธโฃ Deep Learning & Neural Networks
Neural Network Architectures โ CNNs, RNNs, Transformers.
Activation Functions โ ReLU, Sigmoid, Tanh, Softmax.
Optimization Algorithms โ SGD, Adam, RMSprop.
Transfer Learning โ Pre-trained models like BERT, GPT, ResNet.
4๏ธโฃ Time Series Analysis
Forecasting Models โ ARIMA, SARIMA, Prophet.
Feature Engineering for Time Series โ Lag features, Rolling statistics.
Anomaly Detection โ Isolation Forest, Autoencoders.
5๏ธโฃ NLP (Natural Language Processing)
Text Preprocessing โ Tokenization, Stemming, Lemmatization.
Word Embeddings โ Word2Vec, GloVe, FastText.
Sequence Models โ LSTMs, Transformers, BERT.
Text Classification & Sentiment Analysis โ TF-IDF, Attention Mechanism.
6๏ธโฃ Computer Vision
Image Processing โ OpenCV, PIL.
Object Detection โ YOLO, Faster R-CNN, SSD.
Image Segmentation โ U-Net, Mask R-CNN.
7๏ธโฃ Reinforcement Learning
Markov Decision Process (MDP) โ Reward-based learning.
Q-Learning & Deep Q-Networks (DQN) โ Policy improvement techniques.
Multi-Agent RL โ Competitive and cooperative learning.
8๏ธโฃ MLOps & Model Deployment
Model Monitoring & Versioning โ MLflow, DVC.
Cloud ML Services โ AWS SageMaker, GCP AI Platform.
API Deployment โ Flask, FastAPI, TensorFlow Serving.
Like if you want detailed explanation on each topic โค๏ธ
Data Science & Machine Learning Resources: https://t.iss.one/datasciencefun
Hope this helps you ๐
โค3
ETL vs ELT โ Explained Using Apple Juice analogy! ๐๐ง
We often hear about ETL and ELT in the data world โ but how do they actually apply in tools like Excel and Power BI?
Letโs break it down with a simple and relatable analogy ๐
โ ETL (Extract โ Transform โ Load)
๐ง First you make the juice, then you deliver it
โก๏ธ Apples โ Juice โ Truck
๐น In Power BI / Excel:
You clean and transform the data in Power Query
Then load the final data into your report or sheet
๐ก Thatโs ETL โ transformation happens before loading
โ ELT (Extract โ Load โ Transform)
๐ First you deliver the apples, and make juice later
โก๏ธ Apples โ Truck โ Juice
๐น In Power BI / Excel:
You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
๐ก Thatโs ELT โ transformation happens after loading
We often hear about ETL and ELT in the data world โ but how do they actually apply in tools like Excel and Power BI?
Letโs break it down with a simple and relatable analogy ๐
โ ETL (Extract โ Transform โ Load)
๐ง First you make the juice, then you deliver it
โก๏ธ Apples โ Juice โ Truck
๐น In Power BI / Excel:
You clean and transform the data in Power Query
Then load the final data into your report or sheet
๐ก Thatโs ETL โ transformation happens before loading
โ ELT (Extract โ Load โ Transform)
๐ First you deliver the apples, and make juice later
โก๏ธ Apples โ Truck โ Juice
๐น In Power BI / Excel:
You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
๐ก Thatโs ELT โ transformation happens after loading
โค2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
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7 Baby Steps to Learn Excel
1. Understand the Basics: Start by getting familiar with Excel's interface, including workbooks, worksheets, cells, rows, and columns. Learn basic operations like entering and editing data, formatting cells, and using basic formulas (e.g., SUM, AVERAGE, COUNT).
2. Master Essential Functions: Excel's power lies in its functions. Focus on learning frequently used ones like:
Mathematical: SUM, AVERAGE, ROUND
Text: CONCATENATE, LEFT, RIGHT, LEN
Logical: IF, AND, OR
Lookup: VLOOKUP, HLOOKUP, INDEX, MATCH
3. Work with Data: Learn how to organize, sort, and filter data effectively. Practice creating and formatting tables to handle structured data, and explore data validation to restrict input values.
4. Visualize with Charts: Understand how to create charts like bar, line, and pie charts to represent data visually. Learn the importance of choosing the right chart type and practice customizing them for clarity and impact.
5. Explore Pivot Tables: Pivot tables are essential for summarizing large datasets. Learn how to create pivot tables, use slicers for dynamic filtering, and analyze data using fields like Rows, Columns, Values, and Filters.
6. Use Advanced Features: Dive into advanced features like conditional formatting, macros, and Excel's built-in tools for data analysis (e.g., Goal Seek, Solver, and Data Analysis ToolPak). Learn how to work with Array Formulas and explore the power of XLOOKUP (in newer versions).
7. Engage with Excel Communities: Join Excel communities on forums like Redditโs r/Excel, or Microsoftโs Excel Community. Participate in challenges on platforms like ExcelJet, LeetCode, or Kaggle to improve your problem-solving skills and get insights from experts.
Additional Tips:
- Regularly practice on real-world datasets.
- Learn keyboard shortcuts to speed up your work.
- Explore Microsoft Excel's official documentation and free online tutorials for deeper insights.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Understand the Basics: Start by getting familiar with Excel's interface, including workbooks, worksheets, cells, rows, and columns. Learn basic operations like entering and editing data, formatting cells, and using basic formulas (e.g., SUM, AVERAGE, COUNT).
2. Master Essential Functions: Excel's power lies in its functions. Focus on learning frequently used ones like:
Mathematical: SUM, AVERAGE, ROUND
Text: CONCATENATE, LEFT, RIGHT, LEN
Logical: IF, AND, OR
Lookup: VLOOKUP, HLOOKUP, INDEX, MATCH
3. Work with Data: Learn how to organize, sort, and filter data effectively. Practice creating and formatting tables to handle structured data, and explore data validation to restrict input values.
4. Visualize with Charts: Understand how to create charts like bar, line, and pie charts to represent data visually. Learn the importance of choosing the right chart type and practice customizing them for clarity and impact.
5. Explore Pivot Tables: Pivot tables are essential for summarizing large datasets. Learn how to create pivot tables, use slicers for dynamic filtering, and analyze data using fields like Rows, Columns, Values, and Filters.
6. Use Advanced Features: Dive into advanced features like conditional formatting, macros, and Excel's built-in tools for data analysis (e.g., Goal Seek, Solver, and Data Analysis ToolPak). Learn how to work with Array Formulas and explore the power of XLOOKUP (in newer versions).
7. Engage with Excel Communities: Join Excel communities on forums like Redditโs r/Excel, or Microsoftโs Excel Community. Participate in challenges on platforms like ExcelJet, LeetCode, or Kaggle to improve your problem-solving skills and get insights from experts.
Additional Tips:
- Regularly practice on real-world datasets.
- Learn keyboard shortcuts to speed up your work.
- Explore Microsoft Excel's official documentation and free online tutorials for deeper insights.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
๐ง๐ผ๐ฝ ๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐ด๐ด๐น๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ถ๐๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐๐บ๐ฝ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
Want to break into Data Science but not sure where to start?๐
These free Kaggle micro-courses are the perfect launchpad โ beginner-friendly, self-paced, and yes, they come with certifications!๐จโ๐๐
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Want to break into Data Science but not sure where to start?๐
These free Kaggle micro-courses are the perfect launchpad โ beginner-friendly, self-paced, and yes, they come with certifications!๐จโ๐๐
๐๐ข๐ง๐ค๐:-
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No subscription. No hidden fees. Just pure learning from a trusted platformโ ๏ธ
10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. โ Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. โ Know basics of descriptive statistics
6. โ With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. โ Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. โ WORK on atleast 2 end to end projects and create a portfolio of it
9. โ Prepare an ATS friendly resume & start applying
10. โ Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback ๐๐
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. โ Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. โ Know basics of descriptive statistics
6. โ With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. โ Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. โ WORK on atleast 2 end to end projects and create a portfolio of it
9. โ Prepare an ATS friendly resume & start applying
10. โ Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback ๐๐
โค1
Forwarded from Python Projects & Resources
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โค1
Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
โค1
Forwarded from Python Projects & Resources
๐ฑ ๐๐ฅ๐๐ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐๐ฎ๐๐ฎ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ & ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐ป๐ฒ๐๐
Want to break into Data Analytics or Data Scienceโbut donโt know where to begin?๐
Harvard University offers 5 completely free online courses that will build your foundation in Python, statistics, machine learning, and data visualization โ no prior experience or degree required!๐จโ๐๐ซ
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Want to break into Data Analytics or Data Scienceโbut donโt know where to begin?๐
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Some essential concepts every data scientist should understand:
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.
### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).
### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.
### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.
### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).
### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.
### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).
### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.
### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.
### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.
### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.
### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.
### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.
### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.
### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
โค3
Forwarded from Artificial Intelligence
๐ฑ ๐๐ฅ๐๐ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐ ๐ฏ๐ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ, ๐๐๐ , ๐จ๐ฑ๐ฎ๐ฐ๐ถ๐๐ & ๐ ๐ผ๐ฟ๐ฒ๐
Looking to learn Python from scratchโwithout spending a rupee? ๐ป
Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion๐ฅ๐จโ๐
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Kickstart your careerโ ๏ธ
Looking to learn Python from scratchโwithout spending a rupee? ๐ป
Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion๐ฅ๐จโ๐
๐๐ข๐ง๐ค๐:-
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Kickstart your careerโ ๏ธ
Essential Programming Languages to Learn Data Science ๐๐
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ๐๐
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Join @free4unow_backup for more free resources.
ENJOY LEARNING๐๐
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ๐๐
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Join @free4unow_backup for more free resources.
ENJOY LEARNING๐๐
โค3
๐ฐ ๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ & ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐ช๐ถ๐น๐น ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐จ๐ฝ๐ด๐ฟ๐ฎ๐ฑ๐ฒ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
I failed my first data interview โ and hereโs why:โฌ๏ธ
โ No structured learning
โ No real projects
โ Just random YouTube tutorials and half-read blogs
If this sounds like you, donโt repeat my mistakeโจ๏ธ
Recruiters want proof of skills, not just buzzwords๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ka1ZOl
All The Best ๐
I failed my first data interview โ and hereโs why:โฌ๏ธ
โ No structured learning
โ No real projects
โ Just random YouTube tutorials and half-read blogs
If this sounds like you, donโt repeat my mistakeโจ๏ธ
Recruiters want proof of skills, not just buzzwords๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ka1ZOl
All The Best ๐
โค2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING ๐๐
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING ๐๐
โค1
Forwarded from Artificial Intelligence
๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฆ๐ค๐ ๐๐ฎ๐ป ๐๐ฒ ๐๐๐ป! ๐ฐ ๐๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ฃ๐น๐ฎ๐๐ณ๐ผ๐ฟ๐บ๐ ๐ง๐ต๐ฎ๐ ๐๐ฒ๐ฒ๐น ๐๐ถ๐ธ๐ฒ ๐ฎ ๐๐ฎ๐บ๐ฒ๐
Think SQL is all about dry syntax and boring tutorials? Think again.๐ค
These 4 gamified SQL websites turn learning into an adventure โ from solving murder mysteries to exploring virtual islands, youโll write real SQL queries while cracking clues and completing missions๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4nh6PMv
These platforms make SQL interactive, practical, and funโ ๏ธ
Think SQL is all about dry syntax and boring tutorials? Think again.๐ค
These 4 gamified SQL websites turn learning into an adventure โ from solving murder mysteries to exploring virtual islands, youโll write real SQL queries while cracking clues and completing missions๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4nh6PMv
These platforms make SQL interactive, practical, and funโ ๏ธ
โค2
This is how data analytics teams work!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the clientโs business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, itโs availableโcollaboration is key!
End of the day:
1) Data analytics teams arenโt just about crunching numbersโtheyโre about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. Itโll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the clientโs business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, itโs availableโcollaboration is key!
End of the day:
1) Data analytics teams arenโt just about crunching numbersโtheyโre about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. Itโll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
๐๐ฅ๐๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐บ๐ฝ๐ฟ๐ผ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐ฆ๐ธ๐ถ๐น๐น๐๐ฒ๐ ๐
โ Artificial Intelligence โ Master AI & Machine Learning
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โ Artificial Intelligence โ Master AI & Machine Learning
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โ Cloud Computing โ Learn AWS, Azure&cloud infrastructure โ
โ Web 3.0 โ Explore the future of the Internet &Apps ๐
๐๐ข๐ง๐ค ๐:-
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Enroll For FREE & Get Certified ๐
Practice projects to consider:
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
โค2
Forwarded from Python Projects & Resources
๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ข๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified ๐
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified ๐