Lynda - Python for Data Science Essential Training
Lynda - Python for Data Science Essential Training
Size: 828 MB | Duration: 6h 32m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Beginner | Language: English
Size: 828 MB | Duration: 6h 32m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Beginner | Language: English
By using Python to glean value from your raw data, you can simplify the often complex journey from data to value. In this practical, hands-on course, learn how to use Python for data preparation, data munging, data visualization, and predictive analytics. Instructor Lillian Pierson, P. E. covers the essential Python methods for preparing, cleaning, reformatting, and visualizing your data for use in analytics and data science. She helps to provide you with a working understanding of machine learning, as well as outlier analysis, cluster analysis, and network analysis. Plus, Lillian explains how to create web-based data visualizations with Plot. ly, and how to use Python to scrape the web and capture your own data sets.
* Getting started with Jupyter Notebooks
* Visualizing data: basic charts, time series, and statistical plots
* Preparing for analysis: treating missing values and data transformation
* Data analysis basics: arithmetic, summary statistics, and correlation analysis
* Outlier analysis: univariate, multivariate, and linear projection methods
* Introduction to machine learning
* Basic machine learning methods: linear and logistic regression, Naïve Bayes
* Reducing dataset dimensionality with PCA
* Clustering and classification: k-means, hierarchical, and k-NN
* Simulating a social network with NetworkX
* Creating Plot.ly charts
* Scraping the web with Beautiful Soup
* Visualizing data: basic charts, time series, and statistical plots
* Preparing for analysis: treating missing values and data transformation
* Data analysis basics: arithmetic, summary statistics, and correlation analysis
* Outlier analysis: univariate, multivariate, and linear projection methods
* Introduction to machine learning
* Basic machine learning methods: linear and logistic regression, Naïve Bayes
* Reducing dataset dimensionality with PCA
* Clustering and classification: k-means, hierarchical, and k-NN
* Simulating a social network with NetworkX
* Creating Plot.ly charts
* Scraping the web with Beautiful Soup
Lynda - Python for Data Science Essential Training
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