Python for Data Science Essential Training

Python for Data Science Essential Training

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 6h 32m | 820 MB

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 Lilliаn Piеrson, 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, Lilliаn explains how to create web-based data visualizations with, 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 charts
  • Scraping the web with Beautiful Soup
Table of Contents

1 Welcome
2 What you should know
3 Getting started with Jupyter
4 Exercise files
5 Filter and select data
6 Treat missing values
7 Remove duplicates
8 Concatenate and transform data
9 Group and aggregate data
10 Create standard line, bar, and pie plots
11 Define plot elements
12 Format plots
13 Create labels and annotations
14 Create visualizations from time series data
15 Construct histograms, box plots, and scatter plots
16 Use NumPy arithmetic
17 Generate summary statistics
18 Summarize categorical data
19 Parametric methods
20 Non-parametric methods
21 Transform dataset distributions
22 Introduction to machine learning
23 Explanatory factor analysis
24 Principal component analysis (PCA)
25 Extreme value analysis using univariate methods
26 Multivariate analysis for outlier detection
27 A linear projection method for multivariate data
28 K-means method
29 Hierarchical methods
30 Instance-based learning with k-Nearest Neighbor
31 Intro to network analysis
32 Work with graph objects
33 Simulate a social network
34 Generate stats on nodes and inspect graphs
35 Linear regression model
36 Logistic regression model
37 Naive Bayes classifiers
38 Create basic charts
39 Create statistical charts
40 Create Plotly choropleth maps
41 Create Plotly point maps
42 Introduction to Beautiful Soup
43 Explore NavigatableString objects
44 Parse data
45 Web scrape in practice
46 Next steps