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Python for Data Science Essential Training
Torrent info
Torrent Files List
004 Exercise files.mp4 (Size: 820.04 MB) (Files: 47)
004 Exercise files.mp4
003 Getting started with Jupyter.mp4
031 Intro to network analysis.mp4
Ex_Files_Python_Data_Science_EssT.zip
026 Multivariate analysis for outlier detection.mp4
002 What you should know.mp4
022 Introduction to machine learning.mp4
009 Group and aggregate data.mp4
014 Create visualizations from time series data.mp4
023 Explanatory factor analysis.mp4
034 Generate stats on nodes and inspect graphs.mp4
007 Remove duplicates.mp4
046 Next steps.mp4
043 Explore NavigatableString objects.mp4
045 Web scrape in practice.mp4
027 A linear projection method for multivariate data.mp4
018 Summarize categorical data.mp4
021 Transform dataset distributions.mp4
017 Generate summary statistics.mp4
025 Extreme value analysis using univariate methods.mp4
011 Define plot elements.mp4
033 Simulate a social network.mp4
041 Create Plotly point maps.mp4
030 Instance-based learning with k-Nearest Neighbor.mp4
035 Linear regression model.mp4
024 Principal component analysis (PCA).mp4
020 Non-parametric methods.mp4
016 Use NumPy arithmetic.mp4
001 Welcome.mp4
040 Create Plotly choropleth maps.mp4
044 Parse data.mp4
039 Create statistical charts.mp4
012 Format plots.mp4
015 Construct histograms, box plots, and scatter plots.mp4
010 Create standard line, bar, and pie plots.mp4
037 Naive Bayes classifiers.mp4
036 Logistic regression model.mp4
028 K-means method.mp4
008 Concatenate and transform data.mp4
029 Hierarchical methods.mp4
042 Introduction to Beautiful Soup.mp4
019 Parametric methods.mp4
013 Create labels and annotations.mp4
032 Work with graph objects.mp4
005 Filter and select data.mp4
038 Create basic charts.mp4
006 Treat missing values.mp4
Torrent description
Lynda - Python for Data Science Essential Training
Author - Lillian Pierson, P.E.
Released: 4/10/2017
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.
Topics include:
# 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
Lynda, Python, Data, Science, Essential, Training
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