4.22 (601 reviews)

$29.99

Regular PriceWhat you will 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

Topics

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**

Screenshots

Content

Introduction

Introduction

Time Series - Basics

Time Series Forecasting - Use cases

Course Resources

Forecasting model creation - Steps

Forecasting model creation - Steps 1 (Goal)

Time Series - Basic Notations

Setting up Python and Python Crash Course

Installing Python and Anaconda

Course resources

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Time Series - Data Loading

Data Loading in Python

Time Series - Visualization

Time Series - Visualization Basics

Time Series - Visualization in Python

Time Series - Feature Engineering

Time Series - Feature Engineering Basics

Time Series - Feature Engineering in Python

Time Series - Resampling

Time Series - Upsampling and Downsampling

Time Series - Upsampling and Downsampling in Python

Time Series - Transformation

Time Series - Power Transformation

Moving Average

Exponential Smoothing

Time Series - Important Concepts

White Noise

Random Walk

Decomposing Time Series in Python

Differencing

Differencing in Python

Time Series - Test Train Split

Test Train Split in Python

Time Series - Naive (Persistence) model

Naive (Persistence) model in Python

Time Series - Auto Regression Model

Auto Regression Model - Basics

Auto Regression Model creation in Python

Auto Regression with Walk Forward validation in Python

Time Series - Moving Average model

Moving Average model -Basics

Moving Average model in Python

Time Series - ARIMA model

ACF and PACF

ARIMA model - Basics

ARIMA model in Python

ARIMA model with Walk Forward Validation in Python

Time Series - SARIMA model

SARIMA model in Python

Linear Regression - Data Preprocessing

Additional Course Resources

Gathering Business Knowledge

Data Exploration

The Dataset and the Data Dictionary

Importing Data in Python

Univariate analysis and EDD

EDD in Python

Outlier Treatment

Outlier Treatment in Python

Missing Value Imputation

Missing Value Imputation in Python

Seasonality in Data

Bi-variate analysis and Variable transformation

Variable transformation and deletion in Python

Non-usable variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Correlation Analysis

Correlation Analysis in Python

Linear Regression - Model Creation

The Problem Statement

Basic Equations and Ordinary Least Squares (OLS) method

Assessing accuracy of predicted coefficients

Assessing Model Accuracy: RSE and R squared

Simple Linear Regression in Python

Multiple Linear Regression

The F - statistic

Interpreting results of Categorical variables

Multiple Linear Regression in Python

Test-train split

Bias Variance trade-off

Test train split in Python

Introduction to ANN

Introduction to Neural Networks and Course flow

Single Cells - Perceptron and Sigmoid Neuron

Perceptron

Activation Functions

Python - Creating Perceptron model

Neural Networks - Stacking cells to create network

Basic Terminologies

Gradient Descent

Back Propagation

Important concepts: Common Interview questions

Some Important Concepts

Standard Model Parameters

Hyperparameters

Tensorflow and Keras

Keras and Tensorflow

Installing Tensorflow and Keras

Python - Dataset for classification problem

Dataset for classification

Normalization and Test-Train split

Python - Building and training the Model

Different ways to create ANN using Keras

Building the Neural Network using Keras

Compiling and Training the Neural Network model

Evaluating performance and Predicting using Keras

Python - Solving a Regression problem using ANN

Building Neural Network for Regression Problem

Reviews

A

Andrea6 November 2020

Awful English, moreover all the regression and deep learning part is not related to time series! To avoid

A

Abdul14 October 2020

I learned new things in this course that i could apply to real world problems and deeply understand the concept, requirements and working of Time Series Models.

M

Minnie18 August 2020

The course started strong however by the time I got to lecture 41, there were a ton of mistakes in the lecture. Also, some of their work is outdated as the code provided by them doesn't work for ARIMA. The course is well organized but with so many mistakes, it's hard to focus.

K

Kshitij17 August 2020

Good practical implementation based course with all of the necessities of python covered along with one of the most difficult topics to understand

A

Aaswad9 July 2020

Results of the regression model created using ANN were not nicely explained, the Time series part was very good.

S

Shubham29 June 2020

This course is really Awesome, very well explained the Time Series concept along with fundamental logic and concept of Data Science calculation and neural network. I highly recommend this course to everyone. Really thanks for such a valuable course. thanks Udemy and team.

M

Mario24 June 2020

The course is great, and it briefly touches on the mathematical background of the used algorithms which I believe is essential. It would be better to add more quizzes to make sure learners fully understood the concepts.

A

Anand12 June 2020

I am not getting any new knowledge so far. Yes, this course gave me direction of thinking when you start on any problem.

D

Dosses10 June 2020

up to this stage, the introduction and the objectives of the course have been introduced along with some conceptual facts necessary for the analysis. The presentation refers to the Amtrak dataset which is not available with the coursee materials.

P

Piotr7 June 2020

Great course! Lecturers have a lot of knowledge and skillfully transfer it. The scope of the course is broader than the topic indicates. My expectations were exceeded.

S

Sanket30 May 2020

In starting of the course. I cant say anything but i like te introduction in simple way application of time series forcasting is expalin

L

Luis20 May 2020

I have enjoyed this course as I get to review time series analysis (and I was expecting to see ANN apply into time series analysis). Also, it helped to introduce ANN with Keras over Tensorflow for classification or regression problems. Many thanks

E

Emmanuel27 April 2020

Esperaba se utilizaran pronosticos con las redes neuronales y no la repeticion de otro curso que ya habia tomado de su parte.

I

Iskandar27 April 2020

The Indian accent of this guy is almost impossible to understand. The closed caption and subtitles are frequently incorrect. The material is good and the guy has the knowledge but he should not be giving a course in English language, he should be teaching in Urdu or Bengali.

K

Kevin16 April 2020

It was a good overview of AR, ARMA, etc and regression was taught beautifully, although I had a hard time with the accents at a few points. Why 3 stars? I was hoping on seeing how to apply a real-time series of data to the model, for example, as would be presented by a series of sensors. In other words the actual keras/TF portion was based on training standard canned datasets, not on a time-series (which is what I need.) Further, once the model is set up, how do I use it to find anomalies in incoming data streams (when actual inputs are different than the predicted)?

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