100 Days of Data Science Roadmap
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https://www.linkedin.com/posts/sql-analysts_100-days-of-data-science-roadmap-2024-activity-7199265302499483648-FDq3
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https://www.linkedin.com/posts/sql-analysts_100-days-of-data-science-roadmap-2024-activity-7199265302499483648-FDq3
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How to enter into Data Science
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
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Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
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10 commonly asked data science interview questions along with their answers
1️⃣ 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.
2️⃣ 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.
3️⃣ 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.
4️⃣ 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.
5️⃣ 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.
6️⃣ 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.
7️⃣ 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.
8️⃣ 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.
9️⃣ 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.
🔟 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://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1️⃣ 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.
2️⃣ 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.
3️⃣ 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.
4️⃣ 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.
5️⃣ 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.
6️⃣ 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.
7️⃣ 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.
8️⃣ 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.
9️⃣ 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.
🔟 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://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence
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What is PCA
PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:
🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.
🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.
🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.
🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.
🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.
🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.
🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.
Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.
PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:
🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.
🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.
🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.
🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.
🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.
🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.
🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.
Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.
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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.
Let's talk about it 🧵
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization
The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.
Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.
This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.
In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).
More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.
Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.
https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing
Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.
https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk
Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.
Enable gradient descent to converge faster
Let's talk about it 🧵
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization
The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.
Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.
This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.
In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).
More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.
Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.
https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing
Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.
https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk
Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.
Enable gradient descent to converge faster
Google
DS - Feature Scaling.ipynb
Colaboratory notebook
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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react 👍❤️ if you guys want me to share more of this content...
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react 👍❤️ if you guys want me to share more of this content...
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Stop wasting your 20s partying, dating, and being in a comfort zone.
Here are 15 habits that will transform your life in 90 days:
👇👇
Health Fitness Tips
Here are 15 habits that will transform your life in 90 days:
👇👇
Health Fitness Tips
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If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
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✅ Best Telegram channels to get free coding & data science resources
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✅ Free Courses with Certificate:
https://news.1rj.ru/str/free4unow_backup
https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
✅ Free Courses with Certificate:
https://news.1rj.ru/str/free4unow_backup
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How to get started with data science
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data.
Happy learning 😄😄
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data.
Happy learning 😄😄
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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:
👉 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.
👉 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.
👉 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.
👉 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.
👉 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.
👉 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 👍👍
👉 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.
👉 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.
👉 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.
👉 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.
👉 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.
👉 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 👍👍
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