Time Series Analysis in Python

Time Series Analysis in Python, available at $89.99, has an average rating of 4.57, with 98 lectures, 59 quizzes, based on 2626 reviews, and has 17638 subscribers.

You will learn about Differentiate between time series data and cross-sectional data. Understand the fundamental assumptions of time series data and how to take advantage of them. Transforming a data set into a time-series. Start coding in Python and learn how to use it for statistical analysis. Carry out time-series analysis in Python and interpreting the results, based on the data in question. Examine the crucial differences between related series like prices and returns. Comprehend the need to normalize data when comparing different time series. Encounter special types of time series like White Noise and Random Walks. Learn about "autocorrelation" and how to account for it. Learn about accounting for "unexpected shocks" via moving averages. Discuss model selection in time series and the role residuals play in it. Comprehend stationarity and how to test for its existence. Acknowledge the notion of integration and understand when, why and how to properly use it. Realize the importance of volatility and how we can measure it. Forecast the future based on patterns observed in the past. This course is ideal for individuals who are Aspiring data scientists. or Programming beginners. or People interested in quantitative finance. or Programmers who want to specialize in finance. or Finance graduates and professionals who need to better apply their knowledge in Python. It is particularly useful for Aspiring data scientists. or Programming beginners. or People interested in quantitative finance. or Programmers who want to specialize in finance. or Finance graduates and professionals who need to better apply their knowledge in Python.

Enroll now: Time Series Analysis in Python

Summary

Title: Time Series Analysis in Python

Price: $89.99

Average Rating: 4.57

Number of Lectures: 98

Number of Quizzes: 59

Number of Published Lectures: 97

Number of Published Quizzes: 59

Number of Curriculum Items: 157

Number of Published Curriculum Objects: 156

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Differentiate between time series data and cross-sectional data.
Understand the fundamental assumptions of time series data and how to take advantage of them.
Transforming a data set into a time-series.
Start coding in Python and learn how to use it for statistical analysis.
Carry out time-series analysis in Python and interpreting the results, based on the data in question.
Examine the crucial differences between related series like prices and returns.
Comprehend the need to normalize data when comparing different time series.
Encounter special types of time series like White Noise and Random Walks.
Learn about "autocorrelation" and how to account for it.
Learn about accounting for "unexpected shocks" via moving averages.
Discuss model selection in time series and the role residuals play in it.
Comprehend stationarity and how to test for its existence.
Acknowledge the notion of integration and understand when, why and how to properly use it.
Realize the importance of volatility and how we can measure it.
Forecast the future based on patterns observed in the past.

Who Should Attend

Aspiring data scientists.
Programming beginners.
People interested in quantitative finance.
Programmers who want to specialize in finance.
Finance graduates and professionals who need to better apply their knowledge in Python.

Target Audiences

Aspiring data scientists.
Programming beginners.
People interested in quantitative finance.
Programmers who want to specialize in finance.
Finance graduates and professionals who need to better apply their knowledge in Python.

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 timelessbut 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What does the course cover?

Lecture 2: Download Additional Resources

Chapter 2: Setting Up the Environment

Lecture 1: Setting up the environment – Do not skip, please!

Lecture 2: Why Python and Jupyter?

Lecture 3: Installing Anaconda

Lecture 4: Jupyter Dashboard – Part 1

Lecture 5: Jupyter Dashboard – Part 2

Lecture 6: Installing the Necessary Packages

Lecture 7: Installing Packages – Exercise

Lecture 8: Installing Packages – Exercise Solution

Chapter 3: Introduction to Time Series in Python

Lecture 1: Introduction to Time-Series Data

Lecture 2: Notation for Time Series Data

Lecture 3: Peculiarities of Time Series Data

Lecture 4: Loading the Data

Lecture 5: Examining the Data

Lecture 6: Plotting the Data

Lecture 7: The QQ Plot

Chapter 4: Creating a Time Series Object in Python

Lecture 1: Transforming String inputs into DateTime Values

Lecture 2: Using Date as an Index

Lecture 3: Setting the Frequency

Lecture 4: Filling Missing Values

Lecture 5: Adding and Removing Columns in a Data Frame

Lecture 6: Splitting Up the Data

Lecture 7: Appendix: Updating the Dataset

Chapter 5: Working with Time Series in Python

Lecture 1: White Noise

Lecture 2: Random Walk

Lecture 3: Stationarity

Lecture 4: Determining Weak Form Stationarity

Lecture 5: Seasonality

Lecture 6: Correlation Between Past and Present Values

Lecture 7: The Autocorrelation Function (ACF)

Lecture 8: The Partial Autocorrelation Function (PACF)

Chapter 6: Picking the Correct Model

Lecture 1: Picking the Correct Model

Chapter 7: Modeling Autoregression: The AR Model

Lecture 1: The Autoregressive (AR) Model

Lecture 2: Examining the ACF and PACF of Prices

Lecture 3: Fitting an AR(1) Model for Index Prices

Lecture 4: Fitting Higher-Lag AR Models for Prices

Lecture 5: Using Returns Instead of Prices

Lecture 6: Examining the ACF and PACF of Returns

Lecture 7: Fitting an AR(1) Model for Index Returns

Lecture 8: Fitting Higher-Lag AR Models for Returns

Lecture 9: Normalizing Values

Lecture 10: Model Selection for Normalized Returns (AR)

Lecture 11: Examining the AR Model Residuals

Lecture 12: Unexpected Shocks from Past Periods

Chapter 8: Adjusting to Shocks: The MA Model

Lecture 1: The Moving Average (MA) Model

Lecture 2: Fitting an MA(1) Model for Returns

Lecture 3: Fitting Higher-Lag MA Models for Returns

Lecture 4: Examining the MA Model Residuals for Returns

Lecture 5: Model Selection for Normalized Returns (MA)

Lecture 6: Fitting an MA(1) Model for Prices

Lecture 7: Past Values and Past Errors

Chapter 9: Past Values and Past Errors: The ARMA Model

Instructors

365 Careers
Creating opportunities for Data Science and Finance students

Rating Distribution

1 stars: 44 votes
2 stars: 45 votes
3 stars: 232 votes
4 stars: 855 votes
5 stars: 1450 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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