Data Science with Python at UDEMY Overview
Duration | 4 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Entry Requirements
- + 1 more item
Data Science with Python at UDEMY Highlights
- Get unlimited access to the course content
- Earn a Certificate from Udemy on successful course completion
- 4 articles + 1 downloadable resources
- Compatible on Mobile and TV
Data Science with Python at UDEMY Course details
- Learn how to use Python to create fascinating data visualizations
- Financial and Economic Data Application
- Data Visualization, Development Setup and Language Learning Bridge between Python and JS
- Heavyweight Scraping with Scrapy, Plotting and Visualization, and Data Aggregations and Group operations
- Data visualization is understanding the significance of data by placing it in a visual context. Patterns, trends that might go unnoticed in text-based data can be exposed and recognized easier with data visualization software. It basically involves presentation of data in a pictorial or graphical format. Through this training we are going to learn how to use Python to create fascinating data visualizations.
Data Science with Python at UDEMY Curriculum
Introduction
Segment - 03
Segment - 04-doing-data-science
Segment - 05-problem-definitions-and-collecting-data
Segment - 06-data-pipelines-preparation-cleaning-understanding
Segment - 07-model-building-validation-visualization-data-science-applications
Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools
Segment - 20-introduction-to-python-notebook
Segment - 21-git-and-repl
Segment - 22-introduction-ids-and-juypter-notebook
Segment - 23-lab-tutorials-learning-juypter-notebook
Segment - 24-python-loops-and-functions
Segment - 25-python-objects-introduction
Segment - 26-python-numpy
Segment - 27-arrays
Segment - 30-numpy-lab-tutorial
Segment 31 -review-session-python-for-data-science
Segment 32 - Why Pandas
Segment 33 - Data Series
Segment 34 - Series, Keys and Indices
Segment 35 - NumPy Array vs. Panda Series
Segment 36 - Dataframe
Segment 37 - Dataframe Operations
Segment 48 - Introduction to Scikit-Learn
Segment 49 - Scikit-Learn Uses and Applications
Segment 50 - Scikit-Learn vs. Other Tools
Segment 51 - Scikit-Learn Classes, Utils and Data Sets
Segment 51 - Setting Up Scikit-Learn
Segment 52 - Estimators and Algorithms
Segment 53 - Preprocessing and Feature Engineering
Segment 54 - Metrics
Segment 55 - Clustering
Quiz 1
Data Science with Python at UDEMY Entry Requirements
- Prior knowledge of basic Python will be useful but not necessary
- Not mentioned
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