Interested to Buy Any Domain ? << Click Here >> for more details...
What are all important modules in python reuired for a data science ?
- 1 Answers
- 1199 Views
- I also Faced
- E-Mail Answers
Answer / praveen
Here's a comprehensive list of essential Python modules for data science:
*Core Modules:*
1. NumPy (np) - Numerical computations
2. Pandas (pd) - Data manipulation and analysis
3. Matplotlib (plt) - Data visualization
4. Scikit-learn (sklearn) - Machine learning
5. SciPy - Scientific computing
*Data Manipulation and Analysis:*
1. Pandas-datareader (web data retrieval)
2. Openpyxl (Excel file handling)
3. CSV, JSON, and XML (data import/export)
*Data Visualization:*
1. Seaborn (visualization based on Matplotlib)
2. Plotly (interactive visualizations)
3. Bokeh (interactive visualizations)
4. Geopandas (geospatial data visualization)
*Machine Learning and Deep Learning:*
1. TensorFlow (tf) - Deep learning
2. Keras - Deep learning
3. PyTorch - Deep learning
4. Scikit-learn (sklearn) - Machine learning
5. LightGBM - Gradient boosting
6. XGBoost - Gradient boosting
*Statistical Analysis:*
1. Statsmodels - Statistical modeling
2. PyMC3 - Bayesian modeling
3. Scipy.stats - Statistical functions
*Data Preprocessing and Feature Engineering:*
1. Scikit-image (image processing)
2. NLTK (natural language processing)
3. SpaCy (natural language processing)
4. Gensim (topic modeling)
*Big Data and Distributed Computing:*
1. Apache Spark - Big data processing
2. Dask - Parallel computing
3. Joblib - Parallel computing
*Other Essential Modules:*
1. IPython - Interactive shell
2. Jupyter Notebook - Interactive coding environment
3. PyCharm, VSCode, or Spyder - IDEs
4. Git - Version control
*Domain-Specific Modules:*
1. Bioinformatics: Biopython, Scikit-bio
2. Finance: Pandas-datareader, Zipline
3. Geospatial: Geopandas, Folium
4. Natural Language Processing: NLTK, SpaCy
5. Computer Vision: OpenCV, Scikit-image
*Tips:*
1. Install modules using pip or conda.
2. Keep your modules up-to-date.
3. Explore documentation and tutorials for each module.
4. Practice using modules on real-world projects.
*Resources:*
1. Python Data Science Handbook (book)
2. DataCamp (online courses)
3. Kaggle (competitions and tutorials)
4. GitHub (open-source projects)
Mastering these modules will provide a solid foundation for data science tasks in Python.
| Is This Answer Correct ? | 0 Yes | 0 No |
What is python and python scripting?
What are mixins in python?
Write a script to connect to MySql database using Python?
What is indexing? Explain with an example
What is the difference between encapsulation and abstraction in python?
Is there any way to kill a thread in python?
What is the python keyword "with" used for?
What are negative indexes and why are they used?
How will you share global variables across modules?
How to prevent blocking in content() method of socket?
How will you get the length of the string?
How are arguments passed - by reference or by value?
Python 
