Official Python Docs
https://docs.python.org/3/
Tools:
http://docs.python-guide.org/en/latest/dev/virtualenvs/
http://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
http://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html
Python Resources Telegram Channel
https://t.me/PythoResourcesTP
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
https://docs.python.org/3/
Tools:
http://docs.python-guide.org/en/latest/dev/virtualenvs/
http://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
http://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html
Python Resources Telegram Channel
https://t.me/PythoResourcesTP
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
docs.python-guide.org
Pipenv & Virtual Environments โ The Hitchhiker's Guide to Python
๐1
There has never been a better time to become a data analyst.
Tackle the tools:
* Excel
* SQL
* PowerBI/Tableau
* Python/R
Sharpen these soft skills:
* Communication
* Storytelling
* Critical thinking
* Business acumen
And let your journey begin.
Learn Power BI in 2025: https://t.me/DataAnalysisResourcesTP/7
Tackle the tools:
* Excel
* SQL
* PowerBI/Tableau
* Python/R
Sharpen these soft skills:
* Communication
* Storytelling
* Critical thinking
* Business acumen
And let your journey begin.
Learn Power BI in 2025: https://t.me/DataAnalysisResourcesTP/7
Essential Python topics for data analysts ๐๐
Python Topics:
Python Resources - https://t.me/PythonResourcesTP
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/PythonResourcesTP
Hope it helps :)
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Python Topics:
Python Resources - https://t.me/PythonResourcesTP
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/PythonResourcesTP
Hope it helps :)
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐1
Learn Data Science in 2025
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก๏ธ
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join https://t.me/DataScienceResourcesTP for more
ENJOY LEARNING ๐๐
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก๏ธ
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join https://t.me/DataScienceResourcesTP for more
ENJOY LEARNING ๐๐
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐1
5 Game-Changing Habits to Master Your Data Science Journey
Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4
Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4
DEV Community
5 Game-Changing Habits to Master Your Data Science Journey
The journey to becoming a data scientist isnโt for the faint of heart. Itโs a demanding but rewarding...
