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Time Series Analysis with Python and R

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Time Series Analysis with Python and R, available at $19.99, has an average rating of 4.75, with 43 lectures, 2 quizzes, based on 6 reviews, and has 41 subscribers.

You will learn about Learn the basic statistical concepts and techniques used in time series analysis. Learn some basic statements to do a time series analysis using Python. Learn some basic statements to do a time series analysis using R. Explain in your own terms, how to perform a time series analysis. Identify the type of model to apply in a time series. This course is ideal for individuals who are Students who wish to acquire or improve their skills in data analysis through time series techniques. or Python developers who want to improve their skills using time series techniques. or Data Analysts. or Beginning python and r developers interested in data science. or Professionals in areas such as marketing, finance, retail, budget, production stock, and so forth. It is particularly useful for Students who wish to acquire or improve their skills in data analysis through time series techniques. or Python developers who want to improve their skills using time series techniques. or Data Analysts. or Beginning python and r developers interested in data science. or Professionals in areas such as marketing, finance, retail, budget, production stock, and so forth.

Enroll now: Time Series Analysis with Python and R

Summary

Title: Time Series Analysis with Python and R

Price: $19.99

Average Rating: 4.75

Number of Lectures: 43

Number of Quizzes: 2

Number of Published Lectures: 43

Number of Published Quizzes: 2

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn the basic statistical concepts and techniques used in time series analysis.
  • Learn some basic statements to do a time series analysis using Python.
  • Learn some basic statements to do a time series analysis using R.
  • Explain in your own terms, how to perform a time series analysis.
  • Identify the type of model to apply in a time series.

Who Should Attend

  • Students who wish to acquire or improve their skills in data analysis through time series techniques.
  • Python developers who want to improve their skills using time series techniques.
  • Data Analysts.
  • Beginning python and r developers interested in data science.
  • Professionals in areas such as marketing, finance, retail, budget, production stock, and so forth.

Target Audiences

  • Students who wish to acquire or improve their skills in data analysis through time series techniques.
  • Python developers who want to improve their skills using time series techniques.
  • Data Analysts.
  • Beginning python and r developers interested in data science.
  • Professionals in areas such as marketing, finance, retail, budget, production stock, and so forth.

There are several reasons why it is desirable to study a time series.

In general, we can say that, the study of a time series has as main objectives:

  • Describe

  • Predict

  • Explain

  • Control

One of the most important reasons for studying time series is for the purpose of making forecasts about the analyzed time series.

The reason that forecasting is so important is that prediction of future events is critical input into many types of planning and decision-making processes, with application to areas such Marketing, Finance Risk Management, Economics, Industrial Process Control, Demography, and so forth.

Despite the wide range of problem situations that require forecasts, there are only two broad types of forecasting techniques. These are Qualitative methods and Quantitative methods.

Qualitative forecasting techniques are often subjective in nature and require judgment on the part of experts.

Quantitative forecasting techniques make formal use of historical data and a forecasting model. The model formally summarizes patterns in the data and expresses a statistical relationship between previous (Tn-1), and current values (Tn), of the variable.

In other words, the forecasting model is used to extrapolate past and current behavior into the future. That’s what we’ll be learning in this course.

Regardless of your objective, this course is oriented to provide you with the basic foundations and knowledge, as well as a practical application, in the study of time series.

Students will find valuable resources, in addition to the video lessons, it has a large number of laboratories, which will allow you to apply in a practical way the concepts described in each lecture.

The labs are written in two of the most important languages in data science. These are python and r.

Course Curriculum

Chapter 1: Environment Preparation

Lecture 1: Install Anaconda Individual Edition

Lecture 2: First Anaconda Execution

Lecture 3: Install RStudio Free Edition

Lecture 4: First R lab execution

Chapter 2: Time Series – Concepts

Lecture 1: Basic Concepts

Lecture 2: Time Series Components

Lecture 3: Time Series Components Labs

Lecture 4: Time Series Decomposition Analysis

Lecture 5: Time Series Decomposition Labs

Chapter 3: Data Wrangling

Lecture 1: Loading Data

Lecture 2: Loading Data Labs

Lecture 3: Summary Data Part 1

Lecture 4: Summary Data Part Lab 1

Lecture 5: Summary Data – Part 2

Lecture 6: Summary Data Part Lab 2

Chapter 4: Differencing

Lecture 1: Differencing and Random Walk

Lecture 2: Differencing and Random Walk Lab

Lecture 3: Order Differencing

Lecture 4: Order Differencing Labs

Chapter 5: Time Series Models bases

Lecture 1: Autoregressive Model

Lecture 2: Moving Average Model

Lecture 3: Moving Average Model Lab

Lecture 4: BackShift Operator

Lecture 5: Difference Operator

Lecture 6: Auto Correlation Function

Lecture 7: Partial Autocorrelation Function

Lecture 8: Partial Autocorrelation Function Labs

Chapter 6: Time Series Models

Lecture 1: ARMA Model

Lecture 2: ARMA Model Labs

Lecture 3: ARIMA Model

Lecture 4: ARIMA Model Lab

Lecture 5: Dickey Fuller Test

Lecture 6: Dickey Fuller Test Lab

Lecture 7: Ljung-Box Q-statistics

Lecture 8: Model Basic Steps

Lecture 9: SARIMA Model

Lecture 10: SARIMA Model Labs

Chapter 7: Forecasting

Lecture 1: Forecast

Lecture 2: Simple Exponential Smoothing – Part 1

Lecture 3: Simple Exponential Smoothing – Part 2

Lecture 4: Simple Exponential Smoothing Labs

Lecture 5: Holt's Exponential Smoothing

Lecture 6: Holt's Exponential Smoothing Labs

Instructors

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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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