๐ SQL Project Ideas for Beginners
1. Employee Database: Create a database to manage employee records. Implement tables for employees, departments, and salaries, and practice complex queries to retrieve specific data.
2. Library Management System: Design a database to track books, authors, and borrowers. Write queries to find available books, late returns, and popular authors.
3. E-commerce Analytics: Set up a database for an online store. Analyze sales data to find best-selling products, customer purchase patterns, and inventory levels using JOIN and GROUP BY clauses.
4. Movie Database: Create a database to manage movies, actors, and genres. Write queries to find movies by specific actors, genres, or release years.
5. Social Media Analysis: Build a database to analyze user interactions (likes, comments, shares) on a social media platform. Use aggregate functions to derive insights from user activity.
6. Student Enrollment System: Create a database to manage student information, courses, and enrollments. Write queries to find students enrolled in specific courses or average grades per course.
7. Sales Performance Dashboard: Design a database to store sales data. Use SQL queries to create reports on monthly sales trends, regional performance, and top sales representatives.
8. Weather Data Analysis: Set up a database to store historical weather data. Write queries to analyze trends in temperature, rainfall, and other metrics over time.
9. Healthcare Database: Create a database to manage patient records, treatments, and doctors. Write queries to find patients with specific conditions or treatment histories.
10. Survey Analysis: Design a database to store survey results. Use SQL queries to analyze responses and derive insights based on demographics or question categories.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
ENJOY LEARNING ๐๐
1. Employee Database: Create a database to manage employee records. Implement tables for employees, departments, and salaries, and practice complex queries to retrieve specific data.
2. Library Management System: Design a database to track books, authors, and borrowers. Write queries to find available books, late returns, and popular authors.
3. E-commerce Analytics: Set up a database for an online store. Analyze sales data to find best-selling products, customer purchase patterns, and inventory levels using JOIN and GROUP BY clauses.
4. Movie Database: Create a database to manage movies, actors, and genres. Write queries to find movies by specific actors, genres, or release years.
5. Social Media Analysis: Build a database to analyze user interactions (likes, comments, shares) on a social media platform. Use aggregate functions to derive insights from user activity.
6. Student Enrollment System: Create a database to manage student information, courses, and enrollments. Write queries to find students enrolled in specific courses or average grades per course.
7. Sales Performance Dashboard: Design a database to store sales data. Use SQL queries to create reports on monthly sales trends, regional performance, and top sales representatives.
8. Weather Data Analysis: Set up a database to store historical weather data. Write queries to analyze trends in temperature, rainfall, and other metrics over time.
9. Healthcare Database: Create a database to manage patient records, treatments, and doctors. Write queries to find patients with specific conditions or treatment histories.
10. Survey Analysis: Design a database to store survey results. Use SQL queries to analyze responses and derive insights based on demographics or question categories.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
ENJOY LEARNING ๐๐
๐13โค1๐1
AI/ML (Daily Schedule) ๐จ๐ปโ๐ป
Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study
Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest
Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing
Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest
Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study
Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest
Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing
Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest
Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐34โค10๐ฅ5
Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:
๐ 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.
๐ 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
๐ 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.
๐ 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.
๐ 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.
๐ 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐ 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.
๐ 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
๐ 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.
๐ 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.
๐ 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.
๐ 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐10
10 commonly asked data science interview questions along with their answers
1๏ธโฃ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2๏ธโฃ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3๏ธโฃ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4๏ธโฃ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5๏ธโฃ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6๏ธโฃ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7๏ธโฃ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8๏ธโฃ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9๏ธโฃ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
๐ What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1๏ธโฃ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2๏ธโฃ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3๏ธโฃ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4๏ธโฃ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5๏ธโฃ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6๏ธโฃ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7๏ธโฃ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8๏ธโฃ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9๏ธโฃ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
๐ What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐14โค4
Here are some essential machine learning algorithms that every data scientist should know:
* Linear Regression: This is a supervised learning algorithm that is used for continuous target variables. It finds a linear relationship between a dependent variable (y) and one or more independent variables (X). It's widely used for tasks like predicting house prices or stock prices.
* Logistic Regression: This is another supervised learning algorithm that is used for binary classification problems. It predicts the probability of an event happening based on independent variables. It's commonly used for tasks like spam email detection or credit card fraud detection.
* Decision Tree: This is a supervised learning algorithm that uses a tree-like model to classify data. It breaks down a decision into a series of smaller and simpler decisions. Decision trees are easily interpretable, making them a good choice for understanding how a model makes predictions.
* Support Vector Machine (SVM): This is a supervised learning algorithm that can be used for both classification and regression tasks. It finds a hyperplane that best separates the data points into different categories. SVMs are known for their good performance on high-dimensional data.
* K-Nearest Neighbors (KNN): This is a supervised learning algorithm that classifies data points based on the labels of their nearest neighbors. The number of neighbors (k) is a parameter that can be tuned to improve the performance of the algorithm. KNN is a simple and easy-to-understand algorithm, but it can be computationally expensive for large datasets.
* Random Forest: This is a supervised learning algorithm that is an ensemble of decision trees. Random forests are often more accurate and robust than single decision trees. They are also less prone to overfitting.
* Naive Bayes: This is a supervised learning algorithm that is based on Bayes' theorem. It assumes that the features are independent of each other, which is often not the case in real-world data. However, Naive Bayes can be a good choice for tasks where the features are indeed independent or when the computational cost is a major concern.
* K-Means Clustering: This is an unsupervised learning algorithm that is used to group data points into k clusters. The k clusters are chosen to minimize the within-cluster sum of squares (WCSS). K-means clustering is a simple and efficient algorithm, but it is sensitive to the initialization of the cluster centers.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
* Linear Regression: This is a supervised learning algorithm that is used for continuous target variables. It finds a linear relationship between a dependent variable (y) and one or more independent variables (X). It's widely used for tasks like predicting house prices or stock prices.
* Logistic Regression: This is another supervised learning algorithm that is used for binary classification problems. It predicts the probability of an event happening based on independent variables. It's commonly used for tasks like spam email detection or credit card fraud detection.
* Decision Tree: This is a supervised learning algorithm that uses a tree-like model to classify data. It breaks down a decision into a series of smaller and simpler decisions. Decision trees are easily interpretable, making them a good choice for understanding how a model makes predictions.
* Support Vector Machine (SVM): This is a supervised learning algorithm that can be used for both classification and regression tasks. It finds a hyperplane that best separates the data points into different categories. SVMs are known for their good performance on high-dimensional data.
* K-Nearest Neighbors (KNN): This is a supervised learning algorithm that classifies data points based on the labels of their nearest neighbors. The number of neighbors (k) is a parameter that can be tuned to improve the performance of the algorithm. KNN is a simple and easy-to-understand algorithm, but it can be computationally expensive for large datasets.
* Random Forest: This is a supervised learning algorithm that is an ensemble of decision trees. Random forests are often more accurate and robust than single decision trees. They are also less prone to overfitting.
* Naive Bayes: This is a supervised learning algorithm that is based on Bayes' theorem. It assumes that the features are independent of each other, which is often not the case in real-world data. However, Naive Bayes can be a good choice for tasks where the features are indeed independent or when the computational cost is a major concern.
* K-Means Clustering: This is an unsupervised learning algorithm that is used to group data points into k clusters. The k clusters are chosen to minimize the within-cluster sum of squares (WCSS). K-means clustering is a simple and efficient algorithm, but it is sensitive to the initialization of the cluster centers.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐17
One day or Day one. You decide.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
๐31๐ฅ11
Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
| | |
| |
| |
|
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
| | |
| |
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
| |
|
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
| |
|
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
| |
|
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | -- 5. Object-Oriented Programming| | |
| |
-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | -- iii. Dplyr (R)| |
|
-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| -- iii. ggplot2 (R)|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | -- 2. Polynomial Regression| | |
| |
-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | -- 5. Random Forest| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
-- 3. Hierarchical Clustering
| | |
| | -- ii. Dimensionality Reduction| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | -- iii. Model Selection| |
|
-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| -- iv. PyTorch (Python)|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | -- iii. Image Segmentation| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | -- ii. Language Modeling| |
|
-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| -- iii. Data Augmentation|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | -- iii. MLlib| |
|
-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| -- iv. Couchbase|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| -- c. Effective Communication|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
-- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
๐21โค6๐ค2
Top free Data Science resources
@datasciencefun
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. ML Crash Course by Google
https://developers.google.com/machine-learning/crash-course/
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 โ Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf
9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx
10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE
Share the channel link with friends
http://t.me/datasciencefun
@datasciencefun
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. ML Crash Course by Google
https://developers.google.com/machine-learning/crash-course/
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 โ Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf
9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx
10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE
Share the channel link with friends
http://t.me/datasciencefun
๐13โค5๐3
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
๐17โค2
๐โ๏ธHere are Data Analytics-related questions along with their answers:
1.Question: What is the purpose of exploratory data analysis (EDA)?
Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.
2. Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.
3.Question: Explain the concept of normalization in the context of data preprocessing.
Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.
4. Question: What is the purpose of a correlation coefficient in statistics?
Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.
5. Question: What is the role of a decision tree in machine learning?
Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.
6. Question: Define precision and recall in the context of classification models.
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.
7. Question: What is the purpose of cross-validation in machine learning?
Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.
8. Question: Explain the concept of a data warehouse.
Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.
9. Question: What is the difference between structured and unstructured data?
Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).
10. Question: What is clustering in machine learning?
Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.
1.Question: What is the purpose of exploratory data analysis (EDA)?
Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.
2. Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.
3.Question: Explain the concept of normalization in the context of data preprocessing.
Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.
4. Question: What is the purpose of a correlation coefficient in statistics?
Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.
5. Question: What is the role of a decision tree in machine learning?
Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.
6. Question: Define precision and recall in the context of classification models.
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.
7. Question: What is the purpose of cross-validation in machine learning?
Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.
8. Question: Explain the concept of a data warehouse.
Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.
9. Question: What is the difference between structured and unstructured data?
Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).
10. Question: What is clustering in machine learning?
Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.
๐20โค2๐1
๐ Data science Free Courses
1๏ธโฃ Python for Everybody Course : A great course for beginners to learn Python.
2๏ธโฃ Data analysis with Python course : This course introduces you to data analysis techniques with Python.
3๏ธโฃ Databases & SQL course : You will learn how to manage databases with SQL.
4๏ธโฃ Intro to Inferential Statistics course : This course teaches you how to make predictions by learning statistics.
5๏ธโฃ ML Zoomcamp course : a practical and practical course for learning machine learning.
1๏ธโฃ Python for Everybody Course : A great course for beginners to learn Python.
2๏ธโฃ Data analysis with Python course : This course introduces you to data analysis techniques with Python.
3๏ธโฃ Databases & SQL course : You will learn how to manage databases with SQL.
4๏ธโฃ Intro to Inferential Statistics course : This course teaches you how to make predictions by learning statistics.
5๏ธโฃ ML Zoomcamp course : a practical and practical course for learning machine learning.
๐8โค4
FREE Resources to learn Statistics
๐๐
Khan academy:
https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube:
https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin :
https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel:
https://www.youtube.com/user/joshstarmer
Free Statistics Books
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
๐๐
Khan academy:
https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube:
https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin :
https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel:
https://www.youtube.com/user/joshstarmer
Free Statistics Books
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
๐19
Data Science Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python
| | |-- R
| | |-- SQL
|
|-- Data Collection and Cleaning
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Data Transformation
| | |-- Data Normalization
|
|-- Data Analysis
| |-- Exploratory Data Analysis (EDA)
| | |-- Descriptive Statistics
| | |-- Data Visualization
| | |-- Hypothesis Testing
| |
| |-- Data Wrangling
| | |-- Pandas
| | |-- NumPy
| | |-- dplyr (R)
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Reinforcement Learning
| | |-- Q-Learning
| | |-- Policy Gradient Methods
| |
| |-- Model Evaluation
| | |-- Cross-Validation
| | |-- Performance Metrics
| | |-- Hyperparameter Tuning
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Feedforward Networks
| | |-- Backpropagation
| |
| |-- Advanced Architectures
| | |-- Convolutional Neural Networks (CNN)
| | |-- Recurrent Neural Networks (RNN)
| | |-- Transformers
| |
| |-- Tools and Frameworks
| | |-- TensorFlow
| | |-- PyTorch
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| | |-- Tokenization
| | |-- Stop Words Removal
| | |-- Stemming and Lemmatization
| |
| |-- NLP Techniques
| | |-- Word Embeddings
| | |-- Sentiment Analysis
| | |-- Named Entity Recognition (NER)
|
|-- Data Visualization
| |-- Basic Plotting
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2 (R)
| |
| |-- Interactive Visualization
| | |-- Plotly
| | |-- Bokeh
| | |-- Dash
|
|-- Big Data
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Spark
| |
| |-- NoSQL Databases
| |-- MongoDB
| |-- Cassandra
|
|-- Cloud Computing
| |-- Cloud Platforms
| | |-- AWS
| | |-- Google Cloud
| | |-- Azure
| |
| |-- Data Services
| |-- Data Storage (S3, Google Cloud Storage)
| |-- Data Pipelines (Dataflow, AWS Data Pipeline)
|
|-- Model Deployment
| |-- Serving Models
| | |-- Flask/Django
| | |-- FastAPI
| |
| |-- Model Monitoring
| |-- Performance Tracking
| |-- A/B Testing
|
|-- Domain Knowledge
| |-- Industry-Specific Applications
| | |-- Finance
| | |-- Healthcare
| | |-- Retail
|
|-- Ethical and Responsible AI
| |-- Bias and Fairness
| |-- Privacy and Security
| |-- Interpretability and Explainability
|
|-- Communication and Storytelling
| |-- Reporting
| |-- Dashboarding
| |-- Presentation Skills
|
|-- Advanced Topics
| |-- Time Series Analysis
| |-- Anomaly Detection
| |-- Graph Analytics
| |-- *PH4N745M*
โ-- Comments
|-- # Single-line comment (Python)
โ-- /* Multi-line comment (Python/R) */
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python
| | |-- R
| | |-- SQL
|
|-- Data Collection and Cleaning
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Data Transformation
| | |-- Data Normalization
|
|-- Data Analysis
| |-- Exploratory Data Analysis (EDA)
| | |-- Descriptive Statistics
| | |-- Data Visualization
| | |-- Hypothesis Testing
| |
| |-- Data Wrangling
| | |-- Pandas
| | |-- NumPy
| | |-- dplyr (R)
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Reinforcement Learning
| | |-- Q-Learning
| | |-- Policy Gradient Methods
| |
| |-- Model Evaluation
| | |-- Cross-Validation
| | |-- Performance Metrics
| | |-- Hyperparameter Tuning
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Feedforward Networks
| | |-- Backpropagation
| |
| |-- Advanced Architectures
| | |-- Convolutional Neural Networks (CNN)
| | |-- Recurrent Neural Networks (RNN)
| | |-- Transformers
| |
| |-- Tools and Frameworks
| | |-- TensorFlow
| | |-- PyTorch
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| | |-- Tokenization
| | |-- Stop Words Removal
| | |-- Stemming and Lemmatization
| |
| |-- NLP Techniques
| | |-- Word Embeddings
| | |-- Sentiment Analysis
| | |-- Named Entity Recognition (NER)
|
|-- Data Visualization
| |-- Basic Plotting
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2 (R)
| |
| |-- Interactive Visualization
| | |-- Plotly
| | |-- Bokeh
| | |-- Dash
|
|-- Big Data
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Spark
| |
| |-- NoSQL Databases
| |-- MongoDB
| |-- Cassandra
|
|-- Cloud Computing
| |-- Cloud Platforms
| | |-- AWS
| | |-- Google Cloud
| | |-- Azure
| |
| |-- Data Services
| |-- Data Storage (S3, Google Cloud Storage)
| |-- Data Pipelines (Dataflow, AWS Data Pipeline)
|
|-- Model Deployment
| |-- Serving Models
| | |-- Flask/Django
| | |-- FastAPI
| |
| |-- Model Monitoring
| |-- Performance Tracking
| |-- A/B Testing
|
|-- Domain Knowledge
| |-- Industry-Specific Applications
| | |-- Finance
| | |-- Healthcare
| | |-- Retail
|
|-- Ethical and Responsible AI
| |-- Bias and Fairness
| |-- Privacy and Security
| |-- Interpretability and Explainability
|
|-- Communication and Storytelling
| |-- Reporting
| |-- Dashboarding
| |-- Presentation Skills
|
|-- Advanced Topics
| |-- Time Series Analysis
| |-- Anomaly Detection
| |-- Graph Analytics
| |-- *PH4N745M*
โ-- Comments
|-- # Single-line comment (Python)
โ-- /* Multi-line comment (Python/R) */
๐25โค10
Myths About Data Science:
โ Data Science is Just Coding
Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones
โ Data Science is a Solo Job
I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts
โ Data Science is All About Big Data
Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. Itโs about the quality of the data and the questions youโre asking, not just the quantity.
โ You Need to Be a Math Genius
Many data science problems can be solved with basic statistical methods and simple logistic regression. Itโs more about applying the right techniques rather than knowing advanced math theories.
โ Data Science is All About Algorithms
Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but itโs not just about complex models. Sometimes simple models can provide the best results. Logistic regression!
โ Data Science is Just Coding
Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones
โ Data Science is a Solo Job
I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts
โ Data Science is All About Big Data
Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. Itโs about the quality of the data and the questions youโre asking, not just the quantity.
โ You Need to Be a Math Genius
Many data science problems can be solved with basic statistical methods and simple logistic regression. Itโs more about applying the right techniques rather than knowing advanced math theories.
โ Data Science is All About Algorithms
Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but itโs not just about complex models. Sometimes simple models can provide the best results. Logistic regression!
๐26
20 essential Python libraries for data science:
๐น pandas: Data manipulation and analysis. Essential for handling DataFrames.
๐น numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
๐น scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
๐น matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
๐น seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
๐น scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
๐น statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
๐น tensorflow: Deep learning. End-to-end open-source platform for machine learning.
๐น keras: High-level neural networks API. Simplifies building and training deep learning models.
๐น pytorch: Deep learning. A flexible and easy-to-use deep learning library.
๐น mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
๐น pydantic: Data validation. Provides data validation and settings management using Python type annotations.
๐น xgboost: Gradient boosting. An optimized distributed gradient boosting library.
๐น lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
๐น pandas: Data manipulation and analysis. Essential for handling DataFrames.
๐น numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
๐น scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
๐น matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
๐น seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
๐น scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
๐น statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
๐น tensorflow: Deep learning. End-to-end open-source platform for machine learning.
๐น keras: High-level neural networks API. Simplifies building and training deep learning models.
๐น pytorch: Deep learning. A flexible and easy-to-use deep learning library.
๐น mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
๐น pydantic: Data validation. Provides data validation and settings management using Python type annotations.
๐น xgboost: Gradient boosting. An optimized distributed gradient boosting library.
๐น lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
๐16๐ฅ5โค2