Time Series Analysis and Forecasting using Python
Created by Abhishek And Pukhraj, Last Updated 15-Apr-2020, Language:English
Time Series Analysis and Forecasting using Python
Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN
Created by Abhishek And Pukhraj, Last Updated 15-Apr-2020, Language:English
What Will I Get ?
- 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.
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
Course Content
-
Introduction
1 Lectures 00:02:10-
Introduction
Preview00:02:10
-
-
Time Series - Basics
5 Lectures 00:20:16-
Time Series Forecasting - Use cases
Preview00:02:25 -
Course Resources
-
Forecasting model creation - Steps
Preview00:02:46 -
Forecasting model creation - Steps 1 (Goal)
00:06:03 -
Time Series - Basic Notations
00:09:02
-
-
Setting up Python and Python Crash Course
9 Lectures 01:37:58-
Installing Python and Anaconda
00:03:04 -
Opening Jupyter Notebook
00:09:06 -
Introduction to Jupyter
00:13:26 -
Arithmetic operators in Python: Python Basics
Preview00:04:28 -
Strings in Python: Python Basics
00:19:07 -
Lists, Tuples and Directories: Python Basics
00:18:41 -
Working with Numpy Library of Python
00:11:54 -
Working with Pandas Library of Python
00:09:15 -
Working with Seaborn Library of Python
00:08:57
-
-
Time Series - Data Loading
1 Lectures 00:17:51-
Data Loading in Python
00:17:51
-
-
Time Series - Visualization
2 Lectures 00:36:38-
Time Series - Visualization Basics
00:09:28 -
Time Series - Visualization in Python
00:27:10
-
-
Time Series - Feature Engineering
2 Lectures 00:29:04-
Time Series - Feature Engineering Basics
00:11:03 -
Time Series - Feature Engineering in Python
00:18:01
-
-
Time Series - Resampling
2 Lectures 00:21:02-
Time Series - Upsampling and Downsampling
00:04:17 -
Time Series - Upsampling and Downsampling in Python
00:16:45
-
-
Time Series - Transformation
3 Lectures 00:11:51-
Time Series - Power Transformation
00:02:32 -
Moving Average
00:07:12 -
Exponential Smoothing
00:02:07
-
-
Time Series - Important Concepts
5 Lectures 00:37:56-
White Noise
00:02:29 -
Random Walk
Preview00:04:23 -
Differencing
00:06:16 -
Decomposing Time Series in Python
00:09:41 -
Differencing in Python
00:15:07
-
-
Time Series - Test Train Split
1 Lectures 00:11:28-
Test Train Split in Python
00:11:28
-
-
Time Series - Naive (Persistence) model
1 Lectures 00:07:54-
Naive (Persistence) model in Python
00:07:54
-
-
Time Series - Auto Regression Model
3 Lectures 00:21:11-
Auto Regression Model - Basics
00:03:29 -
Auto Regression Model creation in Python
00:09:22 -
Auto Regression with Walk Forward validation in Python
00:08:20
-
-
Time Series - Moving Average model
2 Lectures 00:13:31-
Moving Average model -Basics
00:04:33 -
Moving Average model in Python
00:08:58
-
-
Time Series - ARIMA model
4 Lectures 00:31:29-
ACF and PACF
00:08:07 -
ARIMA model - Basics
00:04:43 -
ARIMA model in Python
00:13:15 -
ARIMA model with Walk Forward Validation in Python
00:05:24
-
-
Time Series - SARIMA model
2 Lectures 00:18:06-
SARIMA model
00:07:26 -
SARIMA model in Python
00:10:40
-
-
Stationary time Series
1 Lectures 00:01:42-
Stationary time Series
00:01:42
-
-
Linear Regression - Data Preprocessing
20 Lectures 02:07:09-
Introduction
00:02:20 -
Additional Course Resources
-
Gathering Business Knowledge
00:03:26 -
Data Exploration
00:03:19 -
The Dataset and the Data Dictionary
00:07:31 -
Importing Data in Python
00:06:03 -
Univariate analysis and EDD
00:03:33 -
EDD in Python
00:12:11 -
Outlier Treatment
00:04:15 -
Outlier Treatment in Python
00:14:18 -
Missing Value Imputation
00:03:36 -
Missing Value Imputation in Python
00:04:57 -
Seasonality in Data
00:03:34 -
Bi-variate analysis and Variable transformation
00:16:14 -
Variable transformation and deletion in Python
00:09:21 -
Non-usable variables
00:04:44 -
Dummy variable creation: Handling qualitative data
00:04:50 -
Dummy variable creation in Python
00:05:45 -
Correlation Analysis
00:10:05 -
Correlation Analysis in Python
00:07:07
-
-
Linear Regression - Model Creation
12 Lectures 01:44:11-
The Problem Statement
00:01:25 -
Basic Equations and Ordinary Least Squares (OLS) method
00:08:13 -
Assessing accuracy of predicted coefficients
00:14:40 -
Assessing Model Accuracy: RSE and R squared
00:07:19 -
Simple Linear Regression in Python
00:14:06 -
Multiple Linear Regression
00:04:57 -
The F - statistic
00:08:22 -
Interpreting results of Categorical variables
00:05:04 -
Multiple Linear Regression in Python
00:14:13 -
Test-train split
00:09:32 -
Bias Variance trade-off
00:06:01 -
Test train split in Python
00:10:19
-
-
Introduction to ANN
1 Lectures 00:04:38-
Introduction to Neural Networks and Course flow
00:04:38
-
-
Single Cells - Perceptron and Sigmoid Neuron
3 Lectures 00:31:27-
Perceptron
00:09:47 -
Activation Functions
00:07:30 -
Python - Creating Perceptron model
00:14:10
-
-
Neural Networks - Stacking cells to create network
3 Lectures 00:44:31-
Basic Terminologies
00:09:47 -
Gradient Descent
00:12:17 -
Back Propagation
00:22:27
-
-
Important concepts: Common Interview questions
1 Lectures 00:12:44-
Some Important Concepts
00:12:44
-
-
Standard Model Parameters
1 Lectures 00:08:19-
Hyperparameters
00:08:19
-
-
Tensorflow and Keras
2 Lectures 00:07:08-
Keras and Tensorflow
00:03:04 -
Installing Tensorflow and Keras
00:04:04
-
-
Python - Dataset for classification problem
2 Lectures 00:13:18-
Dataset for classification
00:07:19 -
Normalization and Test-Train split
00:05:59
-
-
Python - Building and training the Model
4 Lectures 00:34:17-
Different ways to create ANN using Keras
00:01:58 -
Building the Neural Network using Keras
00:12:24 -
Compiling and Training the Neural Network model
00:10:34 -
Evaluating performance and Predicting using Keras
00:09:21
-
-
Python - Solving a Regression problem using ANN
1 Lectures 00:22:10-
Building Neural Network for Regression Problem
00:22:10
-

Abhishek And Pukhraj
Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.