Time Series Analysis and Forecasting using Python

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Description : 

You're looking for an entire course on statistic Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and lots of other parts of the business., right?

You've found the proper statistic Analysis and Forecasting course. This course teaches you everything you would like to understand about different forecasting models and the way to implement these models in Python.

After completing this course you'll be able to:

Implement statistic forecasting models like AutoRegression, Moving Average, ARIMA, SARIMA etc.

Implement multivariate forecasting models supported rectilinear regression and Neural Networks.

Confidently practice, discuss and understand different Forecasting models employed by organizations

How this course will help you?

A Verifiable Certificate of Completion is presented to all or any students who undertake this Marketing Analytics: Forecasting Models with Excel course.

If you're a business manager or an executive, or a student who wants to find out and apply forecasting models in world problems of business, this course will offer you a solid base by teaching you the foremost popular forecasting models and the way to implement it.

Why do you have to choose this course?

We believe teaching by example. This course is not any exception. Every Section’s primary focus is to show you the concepts through how-to examples. Each section has the subsequent components:

Theoretical concepts and use cases of various forecasting models

Step-by-step instructions on implement forecasting models in Python

Downloadable Code files containing data and solutions utilized in each lecture

Class notes and assignments to revise and practice the concepts

The practical classes where we create the model for every of those strategies are some things which differentiates this course from the other course available online.

What makes us qualified to show you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics consulting company , we've helped businesses solve their business problem using Analytics and that we have used our experience to incorporate the sensible aspects of selling and data analytics during this course

We also are the creators of a number of the foremost popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:

This is excellent , i really like the very fact the all explanation given are often understood by a layman - Joshua

Thank you Author for this excellent course. you're the simplest and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and that we are committed thereto . If you've got any questions on the course content, practice sheet or anything associated with any topic, you'll always post an issue within the course or send us an immediate message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. you'll also take quizzes to see your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered during this course?

Understanding how future sales will change is one among the key information needed by manager to require data driven decisions. during this course, we'll explore how one can use forecasting models to

See patterns in statistic data

Make forecasts supported models

Let me offer you a quick overview of the course

Section 1 - Introduction

In this section we'll study the course structure



Section 2 - Python basics

This section gets you started with Python.

This section will assist you found out the python and Jupyter environment on your system and it will teach

you how to perform some basic operations in Python. we'll understand the importance of various libraries like Numpy, Pandas & Seaborn.



Section 3 - Basics of your time Series Data

In this section, we'll discuss about the fundamentals of your time series data, application of your time series forecasting and therefore the standard process followed to create a forecasting model



Section 4 - Pre-processing statistic Data

In this section, you'll find out how to see statistic , perform feature engineering, do re-sampling of knowledge , and various other tools to research and prepare the info for models



Section 5 - Getting Data Ready for Regression Model

In this section you'll learn what actions you would like to require a step by step to urge the info then prepare it for the analysis these steps are vital .

We start with understanding the importance of business knowledge then we'll see the way to do data exploration. We find out how to try to to uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.



Section 6 - Forecasting using Regression Model

This section starts with simple rectilinear regression then covers multiple rectilinear regression .We have covered the essential theory behind each concept without getting too mathematical about it in order that you understand where the concept is coming from and the way it's important. But albeit you do not know it , it'll be okay as long as you find out how to run and interpret the result as taught within the practical lectures.

We also check out the way to quantify models accuracy, what's the meaning of F statistic, how categorical variables within the independent variables dataset are interpreted within the results.



Section 7 - Theoretical Concepts

This part will offer you a solid understanding of concepts involved in Neural Networks.

In this section you'll study the only cells or Perceptrons and the way Perceptrons are stacked to make a specification . Once architecture is about , we understand the Gradient descent algorithm to seek out the minima of a function and find out how this is often wont to optimize our network model.



Section 8 - Creating Regression and Classification ANN model in Python

In this part you'll find out how to make ANN models in Python.

We will start this section by creating an ANN model using Sequential API to unravel a classification problem. We find out how to define specification , configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem during which we attempt to predict house prices during a location. we'll also cover the way to create complex ANN architectures using functional API. Lastly we find out how to save lots of and restore models.

I am pretty confident that the course will offer you the required knowledge and skills to right away see practical benefits in your work place.

Go ahead and click on the enroll button, and I'll see you in lesson 1

Cheers

Start-Tech Academy

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 statistic Analysis using Python briefly span of your time