| 001. Course Overview - PLEASE DO NOT SKIP THIS LECTURE.mp4 | 9.8 MB | ||
| 001.1 UDEMY_TSA_FINAL.zip | 10.9 MB | ||
| 002. Course Overview Check.html | 102.4 B | ||
| 003. Course Curriculum Overview.mp4 | 6 MB | ||
| 004. FAQ - Frequently Asked Questions.html | 5.1 KB | ||
| 004.1 UDEMY_TSA_FINAL.zip | 10.9 MB | ||
| 005. Installing Anaconda Python Distribution and Jupyter.mp4 | 26.7 MB | ||
| 005.1 TSA_COURSE_NOTEBOOKS.zip | 11.7 MB | ||
| 005.2 The .yml file..html | 102.4 B | ||
| 006. NumPy Section Overview.mp4 | 1.1 MB | ||
| 007. NumPy Arrays - Part One.mp4 | 17.3 MB | ||
| 008. NumPy Arrays - Part Two.mp4 | 13.5 MB | ||
| 009. NumPy Indexing and Selection.mp4 | 17.2 MB | ||
| 010. NumPy Operations.mp4 | 10.4 MB | ||
| 011. NumPy Exercises.mp4 | 4.5 MB | ||
| 012. NumPy Exercise Solutions.mp4 | 15.3 MB | ||
| 013. Introduction to Pandas.mp4 | 1.7 MB | ||
| 014. Series.mp4 | 13.9 MB | ||
| 015. DataFrames - Part One.mp4 | 23.7 MB | ||
| 016. DataFrames - Part Two.mp4 | 20.6 MB | ||
| 017. Missing Data with Pandas.mp4 | 12.1 MB | ||
| 018. Group By Operations.mp4 | 8.6 MB | ||
| 019. Common Operations.mp4 | 13.9 MB | ||
| 020. Data Input and Output.mp4 | 20.8 MB | ||
| 021. Pandas Exercises.mp4 | 7.8 MB | ||
| 022. Pandas Exercises Solutions.mp4 | 26.9 MB | ||
| 023. Overview of Capabilities of Data Visualization with Pandas.mp4 | 2.4 MB | ||
| 024. Visualizing Data with Pandas.mp4 | 32.6 MB | ||
| 025. Customizing Plots created with Pandas.mp4 | 16.5 MB | ||
| 026. Pandas Data Visualization Exercise.mp4 | 6.9 MB | ||
| 027. Pandas Data Visualization Exercise Solutions.mp4 | 14.6 MB | ||
| 028. Overview of Time Series with Pandas.mp4 | 1.8 MB | ||
| 029. DateTime Index.mp4 | 14.4 MB | ||
| 030. DateTime Index Part Two.mp4 | 19.9 MB | ||
| 031. Time Resampling.mp4 | 22.4 MB | ||
| 032. Time Shifting.mp4 | 9.2 MB | ||
| 033. Rolling and Expanding.mp4 | 15.3 MB | ||
| 034. Visualizing Time Series Data.mp4 | 20.6 MB | ||
| 035. Visualizing Time Series Data - Part Two.mp4 | 23.4 MB | ||
| 036. Time Series Exercises - Set One.mp4 | 3.6 MB | ||
| 037. Time Series Exercises - Set One - Solutions.mp4 | 9.2 MB | ||
| 038. Time Series with Pandas Project Exercise - Set Two.mp4 | 11.4 MB | ||
| 039. Time Series with Pandas Project Exercise - Set Two - Solutions.mp4 | 27.4 MB | ||
| 040. Introduction to Time Series Analysis with Statsmodels.mp4 | 2 MB | ||
| 041. Introduction to Statsmodels Library.mp4 | 21.5 MB | ||
| 042. ETS Decomposition.mp4 | 17.7 MB | ||
| 043. EWMA - Theory.mp4 | 6.6 MB | ||
| 044. EWMA - Exponentially Weighted Moving Average.mp4 | 26.2 MB | ||
| 045. Holt - Winters Methods Theory.mp4 | 14 MB | ||
| 046. Holt - Winters Methods Code Along - Part One.mp4 | 18.2 MB | ||
| 047. Holt - Winters Methods Code Along - Part Two.mp4 | 26.6 MB | ||
| 048. Statsmodels Time Series Exercises.mp4 | 7 MB | ||
| 049. Statsmodels Time Series Exercise Solutions.mp4 | 12.8 MB | ||
| 050. Introduction to General Forecasting Section.mp4 | 5.3 MB | ||
| 051. Introduction to Forecasting Models Part One.mp4 | 22.1 MB | ||
| 052. Evaluating Forecast Predictions.mp4 | 12.9 MB | ||
| 053. Introduction to Forecasting Models Part Two.mp4 | 20.3 MB | ||
| 054. ACF and PACF Theory.mp4 | 13.8 MB | ||
| 055. ACF and PACF Code Along.mp4 | 27.3 MB | ||
| 056. ARIMA Overview.mp4 | 20 MB | ||
| 057. Autoregression - AR - Overview.mp4 | 8.3 MB | ||
| 058. Autoregression - AR with Statsmodels.mp4 | 41.8 MB | ||
| 059. Descriptive Statistics and Tests - Part One.mp4 | 12 MB | ||
| 060. Descriptive Statistics and Tests - Part Two.mp4 | 49 MB | ||
| 061. Descriptive Statistics and Tests - Part Three.mp4 | 12.4 MB | ||
| 062. ARIMA Theory Overview.mp4 | 8.5 MB | ||
| 063. Choosing ARIMA Orders - Part One.mp4 | 9.7 MB | ||
| 064. Choosing ARIMA Orders - Part Two.mp4 | 31.8 MB | ||
| 065. ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One.mp4 | 21.8 MB | ||
| 066. ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two.mp4 | 64.6 MB | ||
| 067. SARIMA - Seasonal Autoregressive Integrated Moving Average.mp4 | 35 MB | ||
| 068. SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE.mp4 | 10.8 MB | ||
| 069. SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO.mp4 | 43 MB | ||
| 070. SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3.mp4 | 36.7 MB | ||
| 071. Vector AutoRegression - VAR.mp4 | 8.3 MB | ||
| 072. VAR - Code Along.mp4 | 31.2 MB | ||
| 073. VAR - Code Along - Part Two.mp4 | 37.8 MB | ||
| 074. Vector AutoRegression Moving Average - VARMA.mp4 | 4 MB | ||
| 075. Vector AutoRegression Moving Average - VARMA - Code Along.mp4 | 29.2 MB | ||
| 076. Forecasting Exercises.mp4 | 6.1 MB | ||
| 077. Forecasting Exercises - Solutions.mp4 | 19.4 MB | ||
| 078. Introduction to Deep Learning Section.mp4 | 6.4 MB | ||
| 079. Perceptron Model.mp4 | 6.8 MB | ||
| 080. Introduction to Neural Networks.mp4 | 8.9 MB | ||
| 081. Keras Basics.mp4 | 29.1 MB | ||
| 082. Recurrent Neural Network Overview.mp4 | 10.3 MB | ||
| 083. LSTMS and GRU.mp4 | 13.8 MB | ||
| 084. Keras and RNN Project - Part One.mp4 | 21.1 MB | ||
| 085. Keras and RNN Project - Part Two.mp4 | 20.6 MB | ||
| 086. Keras and RNN Project - Part Three.mp4 | 46.7 MB | ||
| 087. Keras and RNN Exercise.mp4 | 9.8 MB | ||
| 088. Keras and RNN Exercise Solutions.mp4 | 26.2 MB | ||
| 089. Overview of Facebook's Prophet Library.mp4 | 4.8 MB | ||
| 090. Facebook's Prophet Library.mp4 | 34.8 MB | ||
| 091. Facebook Prophet Evaluation.mp4 | 35.6 MB | ||
| 092. Facebook Prophet Trend.mp4 | 7.9 MB | ||
| 093. Facebook Prophet Seasonality.mp4 | 12.2 MB | ||
| Readme.txt | 921.6 B | ||
| [GigaCourse.com].url | 0 B | ||
| ▲ 99 total files | |||
Udemy - Python for Time Series Data Analysis
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.
For more Udemy Courses: https://gigacourse.com
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