Learning Python for Data Analysis and Visualization [2015, ENG] torrent

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Learning Python for Data Analysis and Visualization

Год выпуска: 2015
Производитель: Udemy
Сайт производителя: https://www.udemy.com/learning-python-for-data-analysis-and-visualization/
Автор: Jose Portilla
Продолжительность: 19:27:11
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!

You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

You will also get lifetime access to over 90 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!

By the end of this course you will:

- Have an understanding of how to program in Python.

- Know how to create and manipulate arrays using numpy and Python.

- Know how to use pandas to create and analyze data sets.

- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.

- Have an amazing portfolio of example python data analysis projects!

- Have an understanding of Machine Learning and SciKit Learn!

With 90+ lectures and over 16 hours of information and more than 80 example python code notebooks, you will be excellently prepared for a future in data science!
[spoiler="Содержание"]
Section 1: Intro to Course and Python
Lecture 1 Course Intro Preview 04:17
Get a basic overview of what you will learn in this course.

Lecture 2 Resources for Learning Python 01:38
Get links to free resources online to interactively learn Python! Check out the attached text file.

Check out the Appendix A videos for a quick crash course in python by me, or check out the links in the attached file!

Section 2: Setup
Lecture 3 Installation Setup and Overview 14:43
Lecture 4 iPython Notebook Overview 05:42
Lecture 5 Course Environment Overview 02:36
Overview of the remaining lectures will be structured. Using iPython Notebooks and links to nbviewer.

Section 3: Learning Numpy
Lecture 6 Intro to numpy
Text
Take a quick glance at the links in the text and then move on to the next lecture for the video lessons!

Lecture 7 Creating arrays 07:27
Learn to create arrays with numpy and Python.

Lecture 8 Using arrays and scalars 04:41
Learn how to perform operations on multiple arrays and scalars!

Lecture 9 Indexing Arrays 14:19
Learn how to index arrays with numpy.

Lecture 10 Array Transposition 04:07
Learn several universal array functions in numpy.

Lecture 11 Universal Array Function 06:04
Learn how to transpose arrays with numpy.

Lecture 12 Array Processing 21:48
Learn different methods of processing arrays.

Lecture 13 Array Input and Output 07:59
Learn how to import and export your arrays.

Section 4: Intro to Pandas
Lecture 14 Series 13:58
Learn about the Series data structure in pandas.

Lecture 15 DataFrames 17:46
Learn about the DataFrame structure in pandas.

Important Note: If copying directly from Wikipedia does not work, paste the data into a word processor or NotePad Editor and then copy it from there and then run pd.read_clipboard()

Lecture 16 Index objects 04:59
Learn how to index Series and DataFrames in pandas.

Lecture 17 Reindex 15:54
Learn how to reindex in pandas.

Lecture 18 Drop Entry 05:41
Learn how to drop data entries in pandas.

Lecture 19 Selecting Entries 10:22
Learn how to select particular entries in a pandas data structure.

Lecture 20 Data Alignment 10:14
Learn how to align your data in Python.

Lecture 21 Rank and Sort 05:38
Learn how to rank and sort data entries.

Lecture 22 Summary Statistics 22:35
Learn how to quickly get summary statistics in pandas.

Lecture 23 Missing Data 11:37
Learn different ways of dealing with missing data in pandas.

Lecture 24 Index Hierarchy 13:32
Learn how to create hierarchical indexes in pandas.

Section 5: Working with Data: Part 1
Lecture 25 Reading and Writing Text Files 10:03
Learn how to import and export text files with pandas.

Lecture 26 JSON with Python 04:12
Learn how to import and export JSON files with pandas.

Lecture 27 HTML with Python 04:36
Learn how to import HTML files with pandas.

NOTE: Install the following before this lecture, using either conda install or pip install:

pip install beautifulsoup4
pip install lxml

Lecture 28 Microsoft Excel files with Python 03:51
Learn how to import and export MS Excel files with pandas.

Section 6: Working with Data: Part 2
Lecture 29 Merge 20:31
Learn the basics of merging data sets.

Lecture 30 Merge on Index 12:36
Learn how to merge using an index.

Lecture 31 Concatenate 09:19
Learn how to concatenate arrays,matrices, and DataFrames.

Lecture 32 Combining DataFrames 10:20
Learn how to combine DataFrames in pandas.

Lecture 33 Reshaping 07:51
Learn how to reshape data sets.

Lecture 34 Pivoting 05:31
Learn how to create Pivot tables with Python.

Lecture 35 Duplicates in DataFrames 05:54
Learn how to take care of duplicate data entries.

Lecture 36 Mapping 04:12
Learn how to use mapping with pandas.

Lecture 37 Replace 03:15
Learn how to replace data in pandas.

Lecture 38 Rename Index 05:55
Learn how to rename indexes in pandas.

Lecture 39 Binning 06:16
Learn how to use bins with pandas.

Lecture 40 Outliers 06:52
Learn how to find outliers in your data with pandas.

Lecture 41 Permutation 05:21
Learn how to use permutation with numpy and pandas.

Section 7: Working with Data: Part 3
Lecture 42 GroupBy on DataFrames 17:41
Learn how to use advanced groupby techniques.

Lecture 43 GroupBy on Dict and Series 13:21
Learn how to use the groupby method on Dictionaries and Series.

Lecture 44 Aggregation Preview 12:42
Learn about Data Aggregation with Python and pandas.

Lecture 45 Splitting Applying and Combining 10:02
Learn about the powerful Split-Apply-Combine technique and how to use it in pandas.

Lecture 46 Cross Tabulation 05:06
Learn about cross-tabulation in pandas, a special case of pivot table!

Section 8: Data Visualization
Lecture 47 Installing Seaborn 01:44
Quick overview on installing seaborn. Use "conda install seaborn" or "pip install seaborn".

Lecture 48 Histograms 09:19
Learn how to create histograms using seaborn and python.

Lecture 49 Kernel Density Estimate Plots 25:58
Learn how to create kernel Density Estimation Plots with seaborn.

Lecture 50 Combining Plot Styles 06:14
Learn how to combine histograms, KDE , and rug plots onto a single figure.

Lecture 51 Box and Violin Plots 08:52
Learn how to create box and violin plots with seaborn.

Lecture 52 Regression Plots 18:39
Learn how to create regression plots in seaborn.

Lecture 53 Heatmaps and Clustered Matrices 16:49
Learn how to create heatmaps with seaborn.

Section 9: Example Projects.
Lecture 54 Data Projects Preview 03:02
Quick Preview for those interested in enrolling in the course!

Lecture 55 Intro to Data Projects 04:34
Get an introduction to Github, Kaggle, and great public data sets!

Lecture 56 Titanic Project - Part 1 17:06
Learn how to analyze the Titanic Kaggle Problem with Python, pandas, and seaborn!

Lecture 57 Titanic Project - Part 2 16:08
Lecture 58 Titanic Project - Part 3 15:49
Lecture 59 Titanic Project - Part 4 02:05
Lecture 60 Intro to Data Project - Stock Market Analysis 03:13
Lecture 61 Data Project - Stock Market Analysis Part 1 11:19
Lecture 62 Data Project - Stock Market Analysis Part 2 18:06
Lecture 63 Data Project - Stock Market Analysis Part 3 10:24
Lecture 64 Data Project - Stock Market Analysis Part 4 06:56
Lecture 65 Data Project - Stock Market Analysis Part 5 27:40
Lecture 66 Data Project - Intro to Election Analysis 02:20
Please Note: The second presidential debate was Oct 16 and not Oct 11. Oct 11 was the date of the Vice Presidential Debate!

Lecture 67 Data Project - Election Analysis Part 1 18:00
Lecture 68 Data Project - Election Analysis Part 2 20:34
Lecture 69 Data Project - Election Analysis Part 3 15:04
Lecture 70 Data Project - Election Analysis Part 4 26:02

Section 10: Machine Learning
Lecture 71 Introduction to Machine Learning with SciKit Learn 12:51
Learn about the Pydata Ecosystem and SciKit Learn and what Machine Learning is all about!

Lecture 72 Linear Regression Part 1 17:40
Learn about the Math behind Linear Regression then implement it with SciKit Learn!

Lecture 73 Linear Regression Part 2 18:21
Lecture 74 Linear Regression Part 3 18:45
Lecture 75 Linear Regression Part 4 22:08
Lecture 76 Logistic Regression Part 1 14:18
Lecture 77 Logistic Regression Part 2 14:25
Lecture 78 Logistic Regression Part 3 12:20
Lecture 79 Logistic Regression Part 4 22:22
Lecture 80 Multi Class Classification Part 1 - Logistic Regression 18:33
Lecture 81 Multi Class Classification Part 2 - k Nearest Neighbor 23:05
Lecture 82 Support Vector Machines Part 1 12:52
Lecture 83 Support Vector Machines - Part 2 29:07
Lecture 84 Naive Bayes Part 1 10:03
Lecture 85 Naive Bayes Part 2 12:26
Lecture 86 Upcoming topics! Text

Section 11: Appendix A: Python Overview
Lecture 87 Intro to Resources for Learning Python 05:07
Even more great and free resources to learn Python!

Lecture 88 Python Overview Part 1 18:52
Lecture 89 Python Overview Part 2 12:18
Lecture 90 Python Overview Part 3 10:13

Section 12: Appendix B: Statistics Overview
Lecture 91 Intro to Appendix B 02:44
Lecture 92 Discrete Uniform Distribution 06:11
Lecture 93 Continuous Uniform Distribution 07:03
Lecture 94 Binomial Distribution 12:35
Lecture 95 Poisson Distribution 10:55
Lecture 96 Normal Distribution 06:24
Lecture 97 Sampling Techniques 04:54
Lecture 98 T-Distribution 05:09
Lecture 99 Hypothesis Testing and Confidence Intervals 20:08
Lecture 100 Chi Square Test and Distribution 02:53
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Файлы примеров: отсутствуют
Формат видео: MP4
Видео: AVC, 1280x720 (16:9), 29.970 fps, Zencoder Video Encoding System ~296 Kbps avg, 0.011 bit
Аудио: 48.0 KHz, AAC LC, 2 ch, ~76.0 Kbps
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