Time Series Analysis and Forecasting using Python
Last updated 5/2024
Duration: 13h24m | .MP4 1280×720, 30 fps(r) | AAC, 44100 Hz, 2ch | 5.7 GB
Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN
What you’ll learn
Get a solid understanding of Time Series Analysis and Forecasting
Understand the business scenarios where Time Series Analysis is applicable
Building 5 different Time Series Forecasting Models in Python
Learn about Auto regression and Moving average Models
Learn about ARIMA and SARIMA models for forecasting
Use Pandas DataFrames to manipulate Time Series data and make statistical computations
Requirements
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description
You’re looking for a complete
course on Time Series Forecasting to
You’ve found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques.
This course
teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.
After completing this course
you will be able to
:
Implement time series forecasting and time series analysis models such as
AutoRegression, Moving Average, ARIMA, SARIMA
etc.
Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations
How will this course help you?
A
Verifiable Certificate of Completion
is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.
If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques.
Why should you choose this course?
We believe in
teaching by example
. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:
Theoretical concepts
and use cases of different forecasting models, time series forecasting and time series analysis
Step-by-step instructions
on implement time series forecasting models in Python
Downloadable Code files
containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques
Class notes and assignments
to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques
The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques.
.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques.
We are also the creators of some of the most popular online courses – with over 170,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are
class notes
attached for you to follow along. You can also take
quizzes
to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques.
Each section contains a
practice assignment
for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.
What is covered in this course?
Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can
use forecasting models to
See patterns in time series data
Make forecasts based on models
Let me give you a brief overview of the course
Section 1 – Introduction
In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.
Section 2 – Python basics
This section will help you set up the python and Jupyter environment on your system and it’ll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Section 3 – Basics of Time Series Data
In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.
Section 4 – Pre-processing Time Series Data
Section 5 –
Getting Data Ready for Regression Model
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
outlier treatment and missing value imputation.
Section 6 – Forecasting using Regression Model
Section 7 – Theoretical Concepts
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Section 8 – Creating Regression and Classification ANN model in Python
I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.
Go ahead and click the enroll button, and I’ll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques!
Cheers
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Machine Learning journey
Statisticians needing more practical experience
Anyone curious to master Time Series Analysis using Python in short span of time
