Udemy - Time Series Analysis in Python 2020

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[GigaCourse.com] Udemy - Time Series Analysis in Python 2020 01 Introduction
  • 001 What does the course cover.en.srt (6.9 KB)
  • 001 What does the course cover.mp4 (47.3 MB)
02 Setting Up the Environment
  • 002 Setting up the environment - Do not skip please.en.srt (1.3 KB)
  • 002 Setting up the environment - Do not skip please.mp4 (6.0 MB)
  • 003 Why Python and Jupyter.en.srt (6.4 KB)
  • 003 Why Python and Jupyter.mp4 (25.2 MB)
  • 004 Installing Anaconda.en.srt (4.7 KB)
  • 004 Installing Anaconda.mp4 (26.6 MB)
  • 005 Jupyter Dashboard - Part 1.en.srt (3.3 KB)
  • 005 Jupyter Dashboard - Part 1.mp4 (9.8 MB)
  • 006 Jupyter Dashboard - Part 2.en.srt (6.6 KB)
  • 006 Jupyter Dashboard - Part 2.mp4 (20.0 MB)
  • 007 Installing the Necessary Packages.en.srt (1.9 KB)
  • 007 Installing the Necessary Packages.mp4 (7.8 MB)
  • 008 Installing Packages - Exercise.html (1.2 KB)
  • 009 Installing Packages - Exercise Solution.html (1.5 KB)
03 Introduction to Time Series in Python
  • 010 Introduction to Time-Series Data.en.srt (5.5 KB)
  • 010 Introduction to Time-Series Data.mp4 (47.2 MB)
  • 011 Notation for Time Series Data.en.srt (1.7 KB)
  • 011 Notation for Time Series Data.mp4 (12.2 MB)
  • 012 Peculiarities of Time Series Data.en.srt (3.8 KB)
  • 012 Peculiarities of Time Series Data.mp4 (26.8 MB)
  • 013 IndexE8.csv (290.7 KB)
  • 013 Loading the Data.en.srt (2.7 KB)
  • 013 Loading the Data.mp4 (10.2 MB)
  • 014 Examining the Data.en.srt (6.8 KB)
  • 014 Examining the Data.mp4 (39.8 MB)
  • 015 Plotting the Data.en.srt (6.1 KB)
  • 015 Plotting the Data.mp4 (21.2 MB)
  • 016 The QQ Plot.en.srt (3.4 KB)
  • 016 The QQ Plot.mp4 (16.3 MB)
  • external-assets-links.txt (0.3 KB)
04 Creating a Time Series Object in Python
  • 017 Transforming String inputs into DateTime Values.en.srt (6.1 KB)
  • 017 Transforming String inputs into DateTime Values.mp4 (27.9 MB)
  • 018 Using Date as an Index.en.srt (3.7 KB)
  • 018 Using Date as an Index.mp4 (16.6 MB)
  • 019 Setting the Frequency.en.srt (3.1 KB)
  • 019 Setting the Frequency.mp4 (13.4 MB)
  • 020 Filling Missing Values.en.srt (7.1 KB)
  • 020 Filling Missing Values.mp4 (30.0 MB)
  • 021 Adding and Removing Columns in a Data Frame.en.srt (4.4 KB)
  • 021 Adding and Removing Columns in a Data Frame.mp4 (16.3 MB)
  • 022 Splitting Up the Data.en.srt (5.2 KB)
  • 022 Splitting Up the Data.mp4 (21.0 MB)
  • 023 Appendix Updating the Dataset.html (8.7 KB)
  • 023 Section-4-Appendix-Updating-the-Dataset.pdf (235.5 KB)
  • external-assets-links.txt (0.5 KB)
05 Working with Time Series in Python
  • 024 Warning-Messages.pdf (151.5 KB)
  • 024 White Noise.en.srt (8.1 KB)
  • 024 White Noise.mp4 (46.4 MB)
  • 025 RandWalk.csv (163.9 KB)
  • 025 Random Walk.en.srt (6.4 KB)
  • 025 Random Walk.mp4 (32.4 MB)
  • 026 Stationarity.en.srt (3.1 KB)
  • 026 Stationarity.mp4 (21.6 MB)
  • 027 Determining Weak Form Stationarity.en.srt (7.5 KB)
  • 027 Determining Weak Form Stationarity.mp4 (33.8 MB)
  • 028 Seasonality.en.srt (6.3 KB)
  • 028 Seasonality.mp4 (34.2 MB)
  • 029 Correlation Between Past and Present Values.en.srt (2.2 KB)
  • 029 Correlation Between Past and Present Values.mp4 (14.1 MB)
  • 030 The Autocorrelation Function (ACF).en.srt (7.7 KB)
  • 030 The Autocorrelation Function (ACF).mp4 (30.7 MB)
  • 030 The-ACF.pdf (62.0 KB)
  • 031 The Partial Autocorrelation Function (PACF).en.srt (6.3 KB)
  • 031 The Partial Autocorrelation Function (PACF).mp4 (27.2 MB)
  • 031 The-PACF.pdf (63.6 KB)
  • external-assets-links.txt (0.4 KB)
06 Picking the Correct Model
  • 032 Picking the Correct Model.en.srt (3.3 KB)
  • 032 Picking the Correct Model.mp4 (23.0 MB)
07 Modeling Autoregression The AR Model
  • 033 The Autoregressive (AR) Model.en.srt (6.3 KB)
  • 033 The Autoregressive (AR) Model.mp4 (45.3 MB)
  • 034 Course-Notes-The-AR-Model.pdf (425.3 KB)
  • 034 Examining the ACF and PACF of Prices.en.srt (6.1 KB)
  • 034 Examining the ACF and PACF of Prices.mp4 (33.1 MB)
  • 035 Fitting an AR(1) Model for Index Prices.en.srt (5.9 KB)
  • 035 Fitting an AR(1) Model for Index Prices.mp4 (31.6 MB)
  • 036 Fitting Higher-Lag AR Models for Prices.en.srt (11.5 KB)
  • 036 Fitting Higher-Lag AR Models for Prices.mp4 (63.2 MB)
  • 037 Using Returns Instead of Prices.en.srt (7.5 KB)
  • 037 Using Returns Instead of Prices.mp4 (31.4 MB)
  • 038 Examining the ACF and PACF of Returns.en.srt (2.7 KB)
  • 038 Examining the ACF and PACF of Returns.mp4 (15.7 MB)
  • 039 Fitting an AR(1) Model for Index Returns.en.srt (3.1 KB)
  • 039 Fitting an AR(1) Model for Index Returns.mp4 (13.4 MB)
  • 040 Fitting Higher-Lag AR Models for Returns.en.srt (4.3 KB)
  • 040 Fitting Higher-Lag AR Models for Returns.mp4 (26.9 MB)
  • 041 Normalizing Values.en.srt (6.7 KB)
  • 041 Normalizing Values.mp4 (33.1 MB)
  • 042 Model Selection for Normalized Returns (AR).en.srt (3.1 KB)
  • 042 Model Selection for Normalized Returns (AR).mp4 (19.8 MB)
  • 043 Examining the AR Model Residuals.en.srt (7.0 KB)
  • 043 Examining the AR Model Residuals.mp4 (28.8 MB)
  • 044 Unexpected Shocks from Past Periods.en.srt (2.0 KB)
  • 044 Unexpected Shocks from Past Periods.mp4 (16.8 MB)
  • external-assets-links.txt (0.7 KB)
08 Adjusting to Shocks The MA Model
  • 045 8.1.1-MA-Inf-AR-1.pdf (169.2 KB)
  • 045 8.1.1.AR-Inf-MA-1.pdf (166.5 KB)
  • 045 The Moving Average (MA) Model.en.srt (6.5 KB)
  • 045 The Moving Average (MA) Model.mp4 (29.5 MB)
  • 046 Course-Notes-The-MA-Model.pdf (136.0 KB)
  • 046 Fitting an MA(1) Model for Returns.en.srt (4.9 KB)
  • 046 Fitting an MA(1) Model for Returns.mp4 (21.5 MB)

Description

Udemy - Time Series Analysis in Python 2020



Description

How does a commercial bank forecast the expected performance of their loan portfolio?

Or how does an investment manager estimate a stock portfolio’s risk?

Which are the quantitative methods used to predict real-estate properties?

If there is some time dependency, then you know it - the answer is: time series analysis.

This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.

In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:

· Easy to understand

· Comprehensive

· Practical

· To the point

· Packed with plenty of exercises and resources

But we know that may not be enough.

We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…

Welcome to Time Series Analysis in Python!

The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.

We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.

Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.

With these tools we will master the most widely used models out there:

· AR (autoregressive model)

· MA (moving-average model)

· ARMA (autoregressive-moving-average model)

· ARIMA (autoregressive integrated moving average model)

· ARIMAX (autoregressive integrated moving average model with exogenous variables)

. SARIA (seasonal autoregressive moving average model)

. SARIMA (seasonal autoregressive integrated moving average model)

. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)

· ARCH (autoregressive conditional heteroscedasticity model)

· GARCH (generalized autoregressive conditional heteroscedasticity model)

. VARMA (vector autoregressive moving average model)

We know that time series is one of those topics that always leaves some doubts.

Until now.

This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.

What you get?

· Active Q&A support

· Supplementary materials – notebook files, course notes, quiz questions, exercises

· All the knowledge to get a job with time series analysis

· A community of data science enthusiasts

· A certificate of completion

· Access to future updates

· Solve real-life business cases that will get you the job

We are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and start mastering time series in Python today.

Created by 365 Careers
Last updated 1/2020
English
English [Auto-generated]



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Udemy - Time Series Analysis in Python 2020

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Download torrent
2.9 GB
seeders:6
leechers:4
Udemy - Time Series Analysis in Python 2020

Torrent hash: 190D0B585D4D9748AF3921CB42B8A03A91EB8012