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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 drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?

You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python.

After completing this course you will be able to:

Implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.

Implement multivariate forecasting models based on Linear regression and Neural Networks.

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

How this course will help you?

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

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.

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

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

Downloadable Code files containing data and solutions used in each lecture

Class notes and assignments to revise and practice the concepts

The practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.

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

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. Each section contains a practice assignment for you to practically implement your learning.

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 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

Section 2 - Python basics

This section gets you started with Python.

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

Section 4 - Pre-processing Time Series Data

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models

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.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do 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 linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. 

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.

Section 7 - Theoretical Concepts

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

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

In this part you will learn how to create ANN models in Python.

We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, 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 in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

I am pretty confident that the course will give you the necessary knowledge and skills to immediately see practical benefits in your work place.

Go ahead and click 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 Time Series Analysis using Python in short span of time

Homepage:

https://www.udemy.com/course/machine-learning-time-series-forecasting-in-python/

Part 1

Part 2

Part 3

Part 4

Part 5