If you want to become a Data Scientist, you NEED to have product sense.
10 interview questions to test your product sense 👇
1. Netflix believes that viewers who watch foreign language content are more likely to remain subscribed. How would you prove or disprove this hypothesis?
2. LinkedIn believes that users who regularly update their skills get more job offers. How would you go about investigating this?
3. Snapchat is considering ways to capture an older demographic. As a Data Scientist, how would you advice your team on this?
4. Spotify leadership is wondering if they should divest from any product lines. How would you go about making a recommendation to the leadership team?
5. YouTube believes that creators who produce Shorts get better distribution on their Longs. How would you prove or disprove this hypothesis?
6. What are some suggestions you have for improving the Airbnb app? How would you go about testing this?
7. Instagram wants to develop features to help travelers. What are some ideas you have to help achieve this goal?
8. Amazon Web Services (AWS) leadership is wondering if they should discontinue any of their cloud services. How would you go about making a recommendation to the leadership team?
9. Salesforce is considering ways to better serve small businesses. As a Data Scientist, how would you advise your team on this?
10. Asana is a B2B business, and they’re considering ways to increase user adoption of their product.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
10 interview questions to test your product sense 👇
1. Netflix believes that viewers who watch foreign language content are more likely to remain subscribed. How would you prove or disprove this hypothesis?
2. LinkedIn believes that users who regularly update their skills get more job offers. How would you go about investigating this?
3. Snapchat is considering ways to capture an older demographic. As a Data Scientist, how would you advice your team on this?
4. Spotify leadership is wondering if they should divest from any product lines. How would you go about making a recommendation to the leadership team?
5. YouTube believes that creators who produce Shorts get better distribution on their Longs. How would you prove or disprove this hypothesis?
6. What are some suggestions you have for improving the Airbnb app? How would you go about testing this?
7. Instagram wants to develop features to help travelers. What are some ideas you have to help achieve this goal?
8. Amazon Web Services (AWS) leadership is wondering if they should discontinue any of their cloud services. How would you go about making a recommendation to the leadership team?
9. Salesforce is considering ways to better serve small businesses. As a Data Scientist, how would you advise your team on this?
10. Asana is a B2B business, and they’re considering ways to increase user adoption of their product.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍21❤2🥰1
Pandas vs. Polars: Which one should you use for your next data project?
Here’s a comparison to help you to choose the right tool:
1. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Great for small to medium-sized datasets but can slow down with larger data due to its row-based memory layout.
𝗣𝗼𝗹𝗮𝗿𝘀: Optimized for speed with a columnar memory layout, making it much faster for large datasets and complex operations.
2. 𝗘𝗮𝘀𝗲 𝗼𝗳 𝗨𝘀𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Highly intuitive and widely adopted, making it easy to find resources, tutorials, and community support.
𝗣𝗼𝗹𝗮𝗿𝘀: Newer and less intuitive for those used to Pandas, but it's catching up quickly with comprehensive documentation and growing community support.
3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆:
𝗣𝗮𝗻𝗱𝗮𝘀: Can be memory-intensive, especially with large DataFrames. Requires careful management to avoid memory issues.
𝗣𝗼𝗹𝗮𝗿𝘀: Designed for efficient memory usage, handling larger datasets better without requiring extensive optimization.
4. 𝗔𝗣𝗜 𝗮𝗻𝗱 𝗦𝘆𝗻𝘁𝗮𝘅:
𝗣𝗮𝗻𝗱𝗮𝘀: Large and mature API with extensive functionality for data manipulation and analysis.
𝗣𝗼𝗹𝗮𝗿𝘀: Offers a similar API to Pandas but focuses on a more modern and efficient approach. Some differences in syntax may require a learning curve.
5. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺:
𝗣𝗮𝗻𝗱𝗮𝘀: Lacks built-in parallelism, requiring additional libraries like Dask for parallel processing.
𝗣𝗼𝗹𝗮𝗿𝘀: Built-in parallelism out of the box, leveraging multi-threading to speed up computations.
Choose Pandas for its simplicity and compatibility with existing projects. Go for Polars when performance and efficiency with large datasets are important.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
Here’s a comparison to help you to choose the right tool:
1. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Great for small to medium-sized datasets but can slow down with larger data due to its row-based memory layout.
𝗣𝗼𝗹𝗮𝗿𝘀: Optimized for speed with a columnar memory layout, making it much faster for large datasets and complex operations.
2. 𝗘𝗮𝘀𝗲 𝗼𝗳 𝗨𝘀𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Highly intuitive and widely adopted, making it easy to find resources, tutorials, and community support.
𝗣𝗼𝗹𝗮𝗿𝘀: Newer and less intuitive for those used to Pandas, but it's catching up quickly with comprehensive documentation and growing community support.
3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆:
𝗣𝗮𝗻𝗱𝗮𝘀: Can be memory-intensive, especially with large DataFrames. Requires careful management to avoid memory issues.
𝗣𝗼𝗹𝗮𝗿𝘀: Designed for efficient memory usage, handling larger datasets better without requiring extensive optimization.
4. 𝗔𝗣𝗜 𝗮𝗻𝗱 𝗦𝘆𝗻𝘁𝗮𝘅:
𝗣𝗮𝗻𝗱𝗮𝘀: Large and mature API with extensive functionality for data manipulation and analysis.
𝗣𝗼𝗹𝗮𝗿𝘀: Offers a similar API to Pandas but focuses on a more modern and efficient approach. Some differences in syntax may require a learning curve.
5. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺:
𝗣𝗮𝗻𝗱𝗮𝘀: Lacks built-in parallelism, requiring additional libraries like Dask for parallel processing.
𝗣𝗼𝗹𝗮𝗿𝘀: Built-in parallelism out of the box, leveraging multi-threading to speed up computations.
Choose Pandas for its simplicity and compatibility with existing projects. Go for Polars when performance and efficiency with large datasets are important.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍23❤3
Salary of a Data Scientist can go up to ₹98 Lakhs in India
You can get this job easily
Just say ‘Bell Curve’ instead of ‘Ghanta’ when talking to people 😂
You can get this job easily
Just say ‘Bell Curve’ instead of ‘Ghanta’ when talking to people 😂
🤣40👍8👎2🥰2
Entry-level AI/ML Jobs nowadays
- 3+ years of deploying GPT models without touching the keyboard.
- 5+ years of experience using TensorFlow, scikit-learn, etc.
- 4+ years of Python/Java experience.
- Graduate from a reputable university (TOP TIER UNIVERSITY) with a minimum GPA of 3.99/4.00.
- Expertise in Database System Management, Frontend Development, and System Integration.
- Proficiency in Python and one or more programming languages such as Java, Javanoscript, or GoLang is a plus
- 4+ years with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Expertise in using Al development frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face Transformers
- Must be a certified Kubernetes administrator.
- Ability to write production-ready code in less than 24 hours.
- Proven track record of solving world hunger with AI.
- Must have telepathic debugging skills.
- Willing to work weekends, holidays, and during full moons.
Oh, and the most important requirement: must be resilient in handling sudden revisions from the boss
- 3+ years of deploying GPT models without touching the keyboard.
- 5+ years of experience using TensorFlow, scikit-learn, etc.
- 4+ years of Python/Java experience.
- Graduate from a reputable university (TOP TIER UNIVERSITY) with a minimum GPA of 3.99/4.00.
- Expertise in Database System Management, Frontend Development, and System Integration.
- Proficiency in Python and one or more programming languages such as Java, Javanoscript, or GoLang is a plus
- 4+ years with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Expertise in using Al development frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face Transformers
- Must be a certified Kubernetes administrator.
- Ability to write production-ready code in less than 24 hours.
- Proven track record of solving world hunger with AI.
- Must have telepathic debugging skills.
- Willing to work weekends, holidays, and during full moons.
Oh, and the most important requirement: must be resilient in handling sudden revisions from the boss
🤣49👍9❤4👎4👌2
There’s no single powerful machine learning algorithm that works well on any problem.
Yes, algorithms like XGBoost can help you in Kaggle Competitions to build more accurate models.
But the real world is different. Choose algorithms based on your data characteristics, the assumptions of algorithms, and the problem type.
Yes, algorithms like XGBoost can help you in Kaggle Competitions to build more accurate models.
But the real world is different. Choose algorithms based on your data characteristics, the assumptions of algorithms, and the problem type.
❤9👍4🥰1
Complete Machine Learning Roadmap
👇👇
1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R
4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation
5. Exploratory Data Analysis (EDA)
- Data Visualization
- Denoscriptive Statistics
6. Supervised Learning
- Regression
- Classification
- Model Evaluation
7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)
9. Ensemble Learning
- Random Forest
- Gradient Boosting
10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning
13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn
14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes
15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations
16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)
17. Real-world Projects and Case Studies
18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals
📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://news.1rj.ru/str/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)
📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners
📚 Join @free4unow_backup for more free resources.
ENJOY LEARNING! 👍👍
👇👇
1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R
4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation
5. Exploratory Data Analysis (EDA)
- Data Visualization
- Denoscriptive Statistics
6. Supervised Learning
- Regression
- Classification
- Model Evaluation
7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)
9. Ensemble Learning
- Random Forest
- Gradient Boosting
10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning
13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn
14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes
15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations
16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)
17. Real-world Projects and Case Studies
18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals
📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://news.1rj.ru/str/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)
📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners
📚 Join @free4unow_backup for more free resources.
ENJOY LEARNING! 👍👍
👍19❤8
Data Scientist Vs. Data Analyst Vs. Data Engineer
What’s the difference between the data roles?
The data role family is more than just one role that does it all.
Here are the key differences.
Data Scientist
- Focuses on deriving insights and creating predictive models.
- Strong background in math, statistics, and machine learning.
- Analyzing complex datasets to identify patterns, trends, and insights.
- Developing predictive models and machine learning algorithms.
- Communicating findings to stakeholders through reports and visualizations.
- Working with data engineers and analysts to implement data-driven solutions.
- Uses tools like Python, R, SQL, Tableau, and others
Data analyst
- Focuses more on interpreting and visualizing data rather than creating predictive models.
- Often works closely with business teams to provide actionable insights.
- Collecting, processing, and performing statistical analyses on large data sets.
- Creating data visualizations and dashboards to communicate insights.
- Conducting ad-hoc analyses and generating reports for business decision-making.
- Ensuring data quality and accuracy.
- Uses tools like Excel, SQL, BI Tools, SAS
Data Engineer
- Focuses on the infrastructure and tools needed to store, process, and retrieve data.
- Designing, building, and maintaining data pipelines and architectures.
- Ensuring data is accessible, reliable, and efficient to process.
- Integrating data from various sources and formats.
- Optimizing database performance and data storage solutions.
- Uses languages like Python, Java, Scala, as well as SQL and NOSQL, ETL tools, data warehouse tools and others
Like if you need similar content 😄👍
Hope this helps you 😊
What’s the difference between the data roles?
The data role family is more than just one role that does it all.
Here are the key differences.
Data Scientist
- Focuses on deriving insights and creating predictive models.
- Strong background in math, statistics, and machine learning.
- Analyzing complex datasets to identify patterns, trends, and insights.
- Developing predictive models and machine learning algorithms.
- Communicating findings to stakeholders through reports and visualizations.
- Working with data engineers and analysts to implement data-driven solutions.
- Uses tools like Python, R, SQL, Tableau, and others
Data analyst
- Focuses more on interpreting and visualizing data rather than creating predictive models.
- Often works closely with business teams to provide actionable insights.
- Collecting, processing, and performing statistical analyses on large data sets.
- Creating data visualizations and dashboards to communicate insights.
- Conducting ad-hoc analyses and generating reports for business decision-making.
- Ensuring data quality and accuracy.
- Uses tools like Excel, SQL, BI Tools, SAS
Data Engineer
- Focuses on the infrastructure and tools needed to store, process, and retrieve data.
- Designing, building, and maintaining data pipelines and architectures.
- Ensuring data is accessible, reliable, and efficient to process.
- Integrating data from various sources and formats.
- Optimizing database performance and data storage solutions.
- Uses languages like Python, Java, Scala, as well as SQL and NOSQL, ETL tools, data warehouse tools and others
Like if you need similar content 😄👍
Hope this helps you 😊
👍25❤5
ML Engineer vs AI Engineer
ML Engineer / MLOps
-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools
AI Engineer / Developer
- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face
ML Engineer / MLOps
-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools
AI Engineer / Developer
- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face
👍10❤4
TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
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🔺 Free Machine learning Courses
1️⃣ Intro to ML course : an introductory and self-paced course to start machine learning.
2️⃣ ML for Everybody course : A simple approach to learning machine learning concepts.
3️⃣ ML in Python course : focus on machine learning with Python and Scikit-Learn.
4️⃣ ML Crash Course : A quick but comprehensive introduction to machine learning.
5️⃣ CS229 : ML : An advanced course for those who want to deepen their knowledge
1️⃣ Intro to ML course : an introductory and self-paced course to start machine learning.
2️⃣ ML for Everybody course : A simple approach to learning machine learning concepts.
3️⃣ ML in Python course : focus on machine learning with Python and Scikit-Learn.
4️⃣ ML Crash Course : A quick but comprehensive introduction to machine learning.
5️⃣ CS229 : ML : An advanced course for those who want to deepen their knowledge
❤10👍5
No one tells you to train Machine Learning models in Data Science interviews.
Problems in Data Science interviews are focused on:
1. SQL for Querying Data
2. Python/R for Data Manipulation
3. Scenario Based Problems to test your way of approaching problems
Problems in Data Science interviews are focused on:
1. SQL for Querying Data
2. Python/R for Data Manipulation
3. Scenario Based Problems to test your way of approaching problems
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10 Best Practices for Data Science
The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.
And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.
Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons
The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.
And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.
Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons
❤15👍7
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...
👍26❤7
Every data scientist should know🙌🤩
👍36❤5🥰1