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[ DevCourseWeb.com ] Python for Data Science by Starweaver
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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 59 lectures (9h 5m) | Size: 2.73 GB
An introductory to intermediate level program in Python, and how to apply it in data science
What you'll learn:
Explain machine learning and its technologies
Discuss and apply Python fundamentals
Understand the NumPy package
Use data analysis using Pandas and data visualization
Implement supervised (regression and classification) & unsupervised (clustering) machine learning
Use various analysis and visualization tools associated with Python, such as Matplotlib, Seaborn etc.
Describe the behavior of data in Python models
Understand how to use the various Python libraries to manipulate data, like Numpy, Pandas and Scikit-Learn
Use Python libraries and work on data manipulation, data preparation and data explorations
Requirements
Basic Python knowledge is assumed
Some software development experience (including languages, databases...)
Description
This Python for Data Science course is an introduction to Python and how to apply it in data science. The course contains ~60 lectures and 7.5 hours of content taught by Praba Santanakrishnan, a highly experienced data scientist from Microsoft.
Staring with some fundamentals about "what is data science," and "who is a data scientist," the program rapidly move into the specific challenges of data science. This includes the challenges of problem definitions and collecting data, to data pipelines, data preparation, data cleaning and related subjects. Data science methodologies, data analytics tools and open source tools are all covered. Model building validation, visualization and various data science applications are also covered. Discussion of the types of machine learning are covered, including supervised and unsupervised machine learning, as well as methodologies and clustering. NumPy, Pandas, Python Notebook, Git, REPL, IDS and Jupyter Notebook are also covered. Arrays, advanced arrays, and matrices are discussed in some detail to ensure you understand what it is all about and how these tools are implemented.
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