The perfect quick reference to key Scikit-learn (Python's primary machine learning library) concepts and code , right at your fingertips.
This fast-paced class is intended for practicing data scientists, data analysts, and business intelligence experts interested in using Python for their day-to-day work. The primary focus is on learning to use Python tools for data science, data analysis, and machine learning efficiently and effectively.
Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.
Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).
Acquiring Data with Python
Cleansing Data with Python
Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.
Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.
At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.
At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.
The course assumes a working knowledge of key data science topics (statistics, machine learning, and general data analytic methods). Programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions. Experience with Python is helpful (but not required).
Yes, a class completion certificate is provided for the Python for Data Science class.
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Discounts are available for 3+ attendees, and corporate training options are also available.
A 20% discount is available for academics currently at a degree-granting institution.
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