Python Libraries for Data Science Training Courses
Offering a wide range of tools and functionalities for various tasks in data analysis, machine learning, and artificial intelligence
- Experienced instructors with industry expertise
- Access to official guides and materials
- Covers all exam topics thoroughly
- Flexible study at your own pace
- Support for exam service
- Anytime access to study resources
What you'll learn
The Python libraries course will develop yourunderstanding on how to perform numericalcomputation,data analysis and data visualization usingNumPy, Pandas, and Matplotlib libraries. Once youlearn about the Python libraries for data science, nextyou will explore scikit-learn for model building,Beautiful Soup for web scraping, and TensorFlow.
Training Course Outline
Python Libraries for Data Science
1.Introduction
2.Top 5 Python Libraries for Data Science
3.NumPy
4.Pandas
5.Matplotlib
6.Scikit Learn
7.TensorFlow
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
NumPy, Pandas, and Matplotlib are indeed fundamental for numerical computation, data manipulation, and visualization. Scikit-learn is excellent for building machine learning models, Beautiful Soup is handy for web scraping tasks, and TensorFlow is a powerful library for deep learning applications.
Pre-requisites
The course has no specific prerequisites.
Python Datascience PDF Free Download | SPOTO
Python Datascience PDF Free Download | SPOTO
Cours name: python File Type: PDF
Python Libraries for Data Science FAQs
What are the top 20 Python packages?
Among the most popular Python packages are NumPy, Pandas, Matplotlib, TensorFlow, PyTorch, Scikit-learn, Requests, Keras, Seaborn, Plotly, NLTK, Beautiful Soup, Pygame, Gensim, spaCy, SciPy, Theano, PyBrain, Bokeh, and Hebel. These libraries are celebrated across the Python ecosystem for their versatility and effectiveness in fields like data science, machine learning, web scraping, game development, and beyond.
Is NumPy a data science library?
Yes, NumPy is a cornerstone library for data science in Python. This widely adopted open-source tool excels in scientific computing, offering robust support for multidimensional arrays and large matrices. Its rich set of mathematical functions enables rapid calculations, making it a vital asset for data processing and analysis in data science projects.
Is Python alone enough for data science?
Python by itself is not enough to master data science, though it serves as an excellent starting point. To meet industry demands and keep pace with evolving technology, proficiency in additional areas is essential. These include machine learning techniques, statistical analysis, data visualization, data wrangling, web scraping, and numerical computing. Leveraging Python’s ecosystem, including libraries like Pandas and Scikit-learn, is also critical for success.
What is PANDAS used for?
Pandas is a powerful open-source Python library tailored for data manipulation and analysis. It provides specialized data structures, such as DataFrames and Series, which streamline the handling of tabular data. With its user-friendly API, Pandas simplifies complex tasks like data cleaning, restructuring, and exploration, making it an indispensable tool for data scientists and analysts.