๐ฆTop 10 Data Science Tools๐ฆ
Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.
๐ฐWhat is Data Science ?
Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .
๐ฝTop Data Science Tools that are normally utilized :
1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .
2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.
3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.
4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.
5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.
6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.
7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.
8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.
9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.
10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.
Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.
๐ฐWhat is Data Science ?
Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .
๐ฝTop Data Science Tools that are normally utilized :
1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .
2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.
3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.
4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.
5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.
6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.
7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.
8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.
9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.
10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.
โค2
Forwarded from Artificial Intelligence
๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
Data Analytics :- https://pdlink.in/3Fq7E4p
Data Science :- https://pdlink.in/4iSWjaP
SQL :- https://pdlink.in/3EyjUPt
Python :- https://pdlink.in/4c7hGDL
Web Dev :- https://bit.ly/4ffFnJZ
AI :- https://pdlink.in/4d0SrTG
Enroll For FREE & Get Certified ๐
Data Analytics :- https://pdlink.in/3Fq7E4p
Data Science :- https://pdlink.in/4iSWjaP
SQL :- https://pdlink.in/3EyjUPt
Python :- https://pdlink.in/4c7hGDL
Web Dev :- https://bit.ly/4ffFnJZ
AI :- https://pdlink.in/4d0SrTG
Enroll For FREE & Get Certified ๐
โค1๐ฅ1
Theoretical Questions for Coding Interviews on Basic Data Structures
1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.
2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.
3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.
4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).
5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).
6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.
7. What is the difference between an array and a linked list?
Array: Fixed size, elements stored in contiguous memory.
Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.
8. What is the time complexity for accessing an element in an array vs. a linked list?
Array: O(1) for direct access by index.
Linked List: O(n) for access, as you must traverse the list from the start to find an element.
9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?
Array:
Insertion/Deletion at the end: O(1).
Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.
Linked List:
Insertion/Deletion at the beginning: O(1).
Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.
10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).
Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.
2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.
3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.
4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).
5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).
6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.
7. What is the difference between an array and a linked list?
Array: Fixed size, elements stored in contiguous memory.
Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.
8. What is the time complexity for accessing an element in an array vs. a linked list?
Array: O(1) for direct access by index.
Linked List: O(n) for access, as you must traverse the list from the start to find an element.
9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?
Array:
Insertion/Deletion at the end: O(1).
Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.
Linked List:
Insertion/Deletion at the beginning: O(1).
Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.
10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).
Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
๐2โค1
Forwarded from Generative AI
๐ฐ ๐๐ฟ๐ฒ๐ฒ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐๐ผ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐๐ฎ๐ถ๐น๐ (๐ก๐ผ ๐ฆ๐ถ๐ด๐ป๐๐ฝ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
๐ Want to Sharpen Your Data Analytics Skills for FREE?๐ซ
If youโre learning data analytics and want to build real skills, theory alone wonโt cut it. You need hands-on practiceโand the best part? You can do it daily, for free!๐ฏ
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
๐ Want to Sharpen Your Data Analytics Skills for FREE?๐ซ
If youโre learning data analytics and want to build real skills, theory alone wonโt cut it. You need hands-on practiceโand the best part? You can do it daily, for free!๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44WK6ie
Enjoy Learning โ ๏ธ
If I had to start learning data analyst all over again, I'd follow this:
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โค1
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ง๐ผ๐ฝ ๐ ๐ก๐๐ ๐ข๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
Infosys :- https://pdlink.in/4jsHZXf
IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified ๐
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
Infosys :- https://pdlink.in/4jsHZXf
IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified ๐
Machine Learning โ Essential Concepts ๐
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค3
๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified๐๏ธ
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified๐๏ธ
๐1
Forwarded from Artificial Intelligence
๐๐ฅ๐๐ ๐ง๐๐ง๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ๐
Gain Real-World Data Analytics Experience with TATA โ 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!
๐๐ข๐ง๐ค๐:-
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Enroll For FREE & Get Certified๐๏ธ
Gain Real-World Data Analytics Experience with TATA โ 100% Free!
This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FyjDgp
Enroll For FREE & Get Certified๐๏ธ