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 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

## 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

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