Complete Data Science Road Mapπ₯
with resourcesπ
1.Math and Statistics:
β’ Linear Algebra
β’ Calculus
β’ Probability
β’ Statistics
2.Languages:
β’ Python (
β’ NumPy,
β’ Pandas,
β’ Matplotlib,
β’ Seaborn )
β’ R
3. Data skills:
β’ Data Cleaning
β’ Exploratory Data Analysis
β’ Feature Engineering
4. Data Visualization:
β’ Matplotlib
β’ Seaborn
β’ Plotly
β’ Tableau
5.Machine Learning Basics:
β’ Supervised Learning
β’ Unsupervised Learning
β’ Regression
β’ Classification
β’ Clustering
6. ML Libraries:
β’ Scikit-Learn
β’ TensorFlow
β’ Keras
β’ PyTorch
7.Model Evaluation and Validation:
β’ Cross-Validation
β’ Hyperparameter Tuning
β’ Evaluation Metrics
8.Big Data Technologies:
β’ Apache Hadoop
β’ Apache Spark
9.Database:
β’ SQL Basics
β’ MySQL
β’ PostgreSQL
10.Deep Learning:
β’ Neural Networks
β’ CNN
β’ RNN
β’ Transfer Learning
11.Natural Language Processing (NLP):
β’ Tokenization
β’ Named Entity Recognition (NER)
β’ Sentiment Analysis
12.Time Series Analysis:
β’ Time Series Components
β’ Seasonal Decomposition
β’ Forecasting Methods
13.Model Deployment:
β’ Flask (for Python)
β’ Django (for Python)
β’ Docker
14.Version Control:
β’ Git
β’ GitHub
15. Cloud Platforms:
β’ AWS
β’ Azure
β’ GCP
16. Data Ethics and Privacy:
β’ Ethical Considerations
β’ Privacy Protection
17.Communication and Reporting:
β’ Data Storytelling
β’ Reporting Tools e.g.
- Jupyter Notebooks
- R Markdown
18.Continuous Learning:
β’ Stay Updated with Industry Trends
β’ Participate in Online Communities
β’ Join online Conferences
------------------- END --------------------
Some good resources to learn Data Science
Books:
β’ Python for Data Analysis
- by Wes McKinney
β’ Hands-On Machine Learning
- by AurΓ©lien GΓ©ron
β’ The Art of Data Science
- by Roger D. Peng and Elizabeth M.
β’ Data Science from Scratch
-by Joel Grus
Blogs:
β’ Towards Data Science
β’ KDnuggets
β’ R-bloggers
β’ Flowingdata
β’ Analytics Vidhya
YouTube Channel
β― Python β Corey Schafer
β― SQL β Joey Blue
β― Excel β ExcelIsFun
β― PowerBI β Guy in a Cube
β― Tableau β Tableau Tim
β― Mathematics β 3Blue1Brown
β― Statistics β statquest
β― Data Analyst β AlexTheAnalyst
β― ML, DL β sentdex
Podcasts:
β’ Data Science at Home
β’ Talking Machines
β’ O'Reilly Data Science Podcast
β’ Linear Digressions
β’ DataFramed
Community and Forums:
Stack Overflow
Reddit - r/datascience:
Documentation and Guides:
1.Scikit-Learn Documentation:
Official documentation for the Scikit-Learn library.
2.Pandas Documentation: Official documentation for the Pandas library.
with resourcesπ
1.Math and Statistics:
β’ Linear Algebra
β’ Calculus
β’ Probability
β’ Statistics
2.Languages:
β’ Python (
β’ NumPy,
β’ Pandas,
β’ Matplotlib,
β’ Seaborn )
β’ R
3. Data skills:
β’ Data Cleaning
β’ Exploratory Data Analysis
β’ Feature Engineering
4. Data Visualization:
β’ Matplotlib
β’ Seaborn
β’ Plotly
β’ Tableau
5.Machine Learning Basics:
β’ Supervised Learning
β’ Unsupervised Learning
β’ Regression
β’ Classification
β’ Clustering
6. ML Libraries:
β’ Scikit-Learn
β’ TensorFlow
β’ Keras
β’ PyTorch
7.Model Evaluation and Validation:
β’ Cross-Validation
β’ Hyperparameter Tuning
β’ Evaluation Metrics
8.Big Data Technologies:
β’ Apache Hadoop
β’ Apache Spark
9.Database:
β’ SQL Basics
β’ MySQL
β’ PostgreSQL
10.Deep Learning:
β’ Neural Networks
β’ CNN
β’ RNN
β’ Transfer Learning
11.Natural Language Processing (NLP):
β’ Tokenization
β’ Named Entity Recognition (NER)
β’ Sentiment Analysis
12.Time Series Analysis:
β’ Time Series Components
β’ Seasonal Decomposition
β’ Forecasting Methods
13.Model Deployment:
β’ Flask (for Python)
β’ Django (for Python)
β’ Docker
14.Version Control:
β’ Git
β’ GitHub
15. Cloud Platforms:
β’ AWS
β’ Azure
β’ GCP
16. Data Ethics and Privacy:
β’ Ethical Considerations
β’ Privacy Protection
17.Communication and Reporting:
β’ Data Storytelling
β’ Reporting Tools e.g.
- Jupyter Notebooks
- R Markdown
18.Continuous Learning:
β’ Stay Updated with Industry Trends
β’ Participate in Online Communities
β’ Join online Conferences
------------------- END --------------------
Some good resources to learn Data Science
Books:
β’ Python for Data Analysis
- by Wes McKinney
β’ Hands-On Machine Learning
- by AurΓ©lien GΓ©ron
β’ The Art of Data Science
- by Roger D. Peng and Elizabeth M.
β’ Data Science from Scratch
-by Joel Grus
Blogs:
β’ Towards Data Science
β’ KDnuggets
β’ R-bloggers
β’ Flowingdata
β’ Analytics Vidhya
YouTube Channel
β― Python β Corey Schafer
β― SQL β Joey Blue
β― Excel β ExcelIsFun
β― PowerBI β Guy in a Cube
β― Tableau β Tableau Tim
β― Mathematics β 3Blue1Brown
β― Statistics β statquest
β― Data Analyst β AlexTheAnalyst
β― ML, DL β sentdex
Podcasts:
β’ Data Science at Home
β’ Talking Machines
β’ O'Reilly Data Science Podcast
β’ Linear Digressions
β’ DataFramed
Community and Forums:
Stack Overflow
Reddit - r/datascience:
Documentation and Guides:
1.Scikit-Learn Documentation:
Official documentation for the Scikit-Learn library.
2.Pandas Documentation: Official documentation for the Pandas library.
π1
PYTHON PROGRAMS pdf
Answer is 1. Because:
1οΈβ£.inner() function is defined inside outer(), but it has its own local variable x with the value of 2. Note that this inner x is different from the outer x.
2οΈβ£.inner() function is called inside outer(), but its return value (which is 2) is not stored or used in any way.
3οΈβ£.Finally, outer() returns the value of its own x, which is 1.
4οΈβ£.The print(outer()) statement calls outer() and prints the returned value, which is 1.
1οΈβ£.inner() function is defined inside outer(), but it has its own local variable x with the value of 2. Note that this inner x is different from the outer x.
2οΈβ£.inner() function is called inside outer(), but its return value (which is 2) is not stored or used in any way.
3οΈβ£.Finally, outer() returns the value of its own x, which is 1.
4οΈβ£.The print(outer()) statement calls outer() and prints the returned value, which is 1.
Answer is C.)
Simply, because there's no code inside the try block that raises an exception, the except block is not executed, and the code continues to print "3" after executing the try block.
Simply, because there's no code inside the try block that raises an exception, the except block is not executed, and the code continues to print "3" after executing the try block.
π1
As a web developer today, most of your works are related to APIs.
But most of developers don't actually understand what it is. π
When we talk about API, we are usually referring to Web API. π
Web API (Application Programmatic Interface) can provide:
1. Resources, e.g. bus arrival time, restaurant ratings...
2. Services or microservices, e.g. converting coordinates into place names, creating QR codes...
With Web APIs, developers can then easily incorporate these building blocks into other web applications.
π©βπ»There's some features of different Web API, for example:
1. Public vs. private
- Public APIs are available to everyone, but may subject to licenses
- Private APIs are only available to internal developers
2. Free vs. premium
- Premium APIs may charge on-demand
- That's why there's also this term βAPI Economyβ!
There's actually API marketplace and directory site, like
- RapidAPI
- ProgrammableWeb
Here's some example of public API that you can explore:
1. Google (developers.google.com/apis-explorer)
2. Facebook (developers.facebook.com/docs/apis-and-β¦)
3. Data portals of many cities (dataportals.org/search)And you also have to understand what is 'API endpoints'.
API usually provides endpoints as a URI to provide services.
And it needs to be static, without affecting applications built upon β¨And that's why there's a standard for writing API called REST.
REST stands for 'REpresentational State Transfer'.
It's an architectural style of writing APIs.
But most of developers don't actually understand what it is. π
When we talk about API, we are usually referring to Web API. π
Web API (Application Programmatic Interface) can provide:
1. Resources, e.g. bus arrival time, restaurant ratings...
2. Services or microservices, e.g. converting coordinates into place names, creating QR codes...
With Web APIs, developers can then easily incorporate these building blocks into other web applications.
π©βπ»There's some features of different Web API, for example:
1. Public vs. private
- Public APIs are available to everyone, but may subject to licenses
- Private APIs are only available to internal developers
2. Free vs. premium
- Premium APIs may charge on-demand
- That's why there's also this term βAPI Economyβ!
There's actually API marketplace and directory site, like
- RapidAPI
- ProgrammableWeb
Here's some example of public API that you can explore:
1. Google (developers.google.com/apis-explorer)
2. Facebook (developers.facebook.com/docs/apis-and-β¦)
3. Data portals of many cities (dataportals.org/search)And you also have to understand what is 'API endpoints'.
API usually provides endpoints as a URI to provide services.
And it needs to be static, without affecting applications built upon β¨And that's why there's a standard for writing API called REST.
REST stands for 'REpresentational State Transfer'.
It's an architectural style of writing APIs.
Google for Developers
Google APIs Explorer | Google for Developers
The Google APIs Explorer is is a tool that helps you explore various Google APIs interactively.
Answer is 8 64
πThe code defines a Square class with a method getArea that calculates the area of a square using the side attribute.
πAn instance of the Square class is created which is obj, It has side = 8,
πits area is calculated by calling getarea.
πThe code defines a Square class with a method getArea that calculates the area of a square using the side attribute.
πAn instance of the Square class is created which is obj, It has side = 8,
πits area is calculated by calling getarea.