Python for Time Series Data Analysis

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Jose Portilla

15:17:35

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1 - Introduction

1 - Course Overview Check.html

1 - Course Overview PLEASE DO NOT SKIP THIS LECTURE.mp4

06:47

1 - UDEMY-TSA-FINAL.zip

2 - Course Curriculum Overview.mp4

04:08

3 - FAQ Frequently Asked Questions.html

3 - UDEMY-TSA-FINAL.zip

2 - Course Set Up and Install

4 - Installing Anaconda Python Distribution and Jupyter.mp4

15:54

4 - UDEMY-TSA-FINAL.zip

4 - the yml file.zip

3 - NumPy

5 - NumPy Section Overview.mp4

00:44

6 - NumPy Arrays Part One.mp4

10:45

7 - NumPy Arrays Part Two.mp4

08:10

8 - NumPy Indexing and Selection.mp4

12:16

9 - NumPy Operations.mp4

06:46

10 - NumPy Exercises.mp4

01:18

11 - NumPy Exercise Solutions.mp4

07:05

4 - Pandas Overview

12 - Introduction to Pandas.mp4

01:11

13 - Series.mp4

10:01

14 - DataFrames Part One.mp4

13:24

15 - DataFrames Part Two.mp4

11:09

16 - Missing Data with Pandas.mp4

08:26

17 - Group By Operations.mp4

05:44

18 - Common Operations.mp4

09:21

19 - Data Input and Output.mp4

10:19

20 - Pandas Exercises.mp4

03:07

21 - Pandas Exercises Solutions.mp4

13:57

5 - Data Visualization with Pandas

22 - Overview of Capabilities of Data Visualization with Pandas.mp4

01:41

23 - Visualizing Data with Pandas.mp4

19:24

24 - Customizing Plots created with Pandas.mp4

10:00

25 - Pandas Data Visualization Exercise.mp4

03:30

26 - Pandas Data Visualization Exercise Solutions.mp4

07:32

6 - Time Series with Pandas

27 - Overview of Time Series with Pandas.mp4

01:10

28 - DateTime Index.mp4

10:20

29 - DateTime Index Part Two.mp4

11:49

30 - Time Resampling.mp4

12:10

31 - Time Shifting.mp4

05:37

32 - Rolling and Expanding.mp4

09:39

33 - Visualizing Time Series Data.mp4

11:14

34 - Visualizing Time Series Data Part Two.mp4

13:09

35 - Time Series Exercises Set One.mp4

01:25

36 - Time Series Exercises Set One Solutions.mp4

04:39

37 - Time Series with Pandas Project Exercise Set Two.mp4

04:48

38 - Time Series with Pandas Project Exercise Set Two Solutions.mp4

15:20

7 - Time Series Analysis with Statsmodels

39 - Introduction to Time Series Analysis with Statsmodels.mp4

01:21

40 - Introduction to Statsmodels Library.mp4

13:19

41 - ETS Decomposition.mp4

10:27

42 - EWMA Theory.mp4

04:34

43 - EWMA Exponentially Weighted Moving Average.mp4

14:07

44 - Holt Winters Methods Theory.mp4

09:44

45 - Holt Winters Methods Code Along Part One.mp4

10:32

46 - Holt Winters Methods Code Along Part Two.mp4

15:46

47 - Statsmodels Time Series Exercises.mp4

02:44

48 - Statsmodels Time Series Exercise Solutions.mp4

06:20

8 - General Forecasting Models

49 - Introduction to General Forecasting Section.mp4

03:42

50 - Introduction to Forecasting Models Part One.mp4

13:21

51 - Evaluating Forecast Predictions.mp4

09:03

52 - Introduction to Forecasting Models Part Two.mp4

11:20

53 - ACF and PACF Theory.mp4

10:16

54 - ACF and PACF Code Along.mp4

16:54

55 - ARIMA Overview.mp4

13:52

56 - Autoregression AR Overview.mp4

05:58

57 - Autoregression AR with Statsmodels.mp4

26:43

58 - Descriptive Statistics and Tests Part One.mp4

08:27

59 - Descriptive Statistics and Tests Part Two.mp4

20:46

60 - Descriptive Statistics and Tests Part Three.mp4

07:29

61 - ARIMA Theory Overview.mp4

06:14

62 - Choosing ARIMA Orders Part One.mp4

06:38

63 - Choosing ARIMA Orders Part Two.mp4

14:00

64 - ARMA and ARIMA AutoRegressive Integrated Moving Average Part One.mp4

12:32

65 - ARMA and ARIMA AutoRegressive Integrated Moving Average Part Two.mp4

26:53

66 - SARIMA Seasonal Autoregressive Integrated Moving Average.mp4

17:50

67 - SARIMAX Seasonal Autoregressive Integrated Moving Average Exogenous PART ONE.mp4

07:30

68 - SARIMAX Seasonal Autoregressive Integrated Moving Average Exogenous PART TWO.mp4

22:09

69 - SARIMAX Seasonal Autoregressive Integrated Moving Average Exogenous PART 3.mp4

20:39

70 - Vector AutoRegression VAR.mp4

05:58

71 - VAR Code Along.mp4

18:44

72 - VAR Code Along Part Two.mp4

15:50

73 - Vector AutoRegression Moving Average VARMA.mp4

02:57

74 - Vector AutoRegression Moving Average VARMA Code Along.mp4

09:26

75 - Forecasting Exercises.mp4

02:09

76 - Forecasting Exercises Solutions.mp4

09:02

9 - Deep Learning for Time Series Forecasting

2 - Quick Check on MultiVariate Time Series Notebook and Data.html

77 - Introduction to Deep Learning Section.mp4

04:30

78 - Perceptron Model.mp4

05:13

79 - Introduction to Neural Networks.mp4

06:35

80 - Keras Basics.mp4

15:26

81 - Recurrent Neural Network Overview.mp4

07:47

82 - LSTMS and GRU.mp4

10:11

83 - Keras and RNN Project Part One.mp4

12:10

84 - Keras and RNN Project Part Two.mp4

11:10

85 - Keras and RNN Project Part Three.mp4

25:19

86 - Keras and RNN Exercise.mp4

03:59

87 - Keras and RNN Exercise Solutions.mp4

13:22

88 - BONUS Multivariate Time Series with RNN.html

88 - MultiVariate-RNN-with-TensorFlow-and-Keras-master.zip

89 - BONUS Multivariate Time Series with RNN.mp4

16:12

10 - Facebooks Prophet Library

90 - Overview of Facebooks Prophet Library.mp4

03:21

91 - Facebooks Prophet Library.mp4

16:37

92 - Facebook Prophet Evaluation.mp4

16:14

93 - Facebook Prophet Trend.mp4

04:38

94 - Facebook Prophet Seasonality.mp4

05:36

11 - BONUS SECTION THANK YOU

95 - BONUS LECTURE.html

Description

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!

What You'll Learn?

Pandas for Data Manipulation
NumPy and Python for Numerical Processing
Pandas for Data Visualization
How to Work with Time Series Data with Pandas
Use Statsmodels to Analyze Time Series Data
Use Facebook's Prophet Library for forecasting
Understand advanced ARIMA models for Forecasting

Who is this for?

Python Developers interested in learning how to forecast time series data

More details

Description
Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!
This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.
We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.
Then we'll begin to learn about the statsmodels library and its powerful built in TimeÂ Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.
Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.
Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.
This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.
So what are you waiting for! Learn how to work with your time series data and forecast the future!
We'll see you inside the course!
Who this course is for:
Python Developers interested in learning how to forecast time series data

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Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.

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