Forecasting Models Course (4 Courses, 14+ Projects)

Objectives

The fundamental objective of this program is to guide the individuals who are ready to learn the forecasting models from the beginning. This training program is completely based on the different forecasting methods like statistical tools in Microsoft Excel, machine learning – statistics essentials, statistics for data science using python, statistics essentials for analytics, and many more other modules.

Course Highlights

This training program includes eight main modules of training which are outlined in such a way that all the necessary information are highlighted:

Statistical Tools in Microsoft Excel is the first module of the training program where you will learn about how different statistical tools of Ms. Excel are used in forecasting data. It will be explained through a video tutorial of 1 hour long in which brief detailing of these tools will be done.

Machine Learning – Statistics Essentials will be next about how to use descriptive statistical methods to transform raw observations into information that you can understand and share through a video tutorial of almost 8 hours.

Statistics for Data Science using Python is the third module about how python can be used in data science for statistics. It will be explained briefly in the session.

Statistics Essentials for Analytics is next to where the importance of the self-made data is designed in such a manner that it is easy for a future to get a solid foundation on the concepts. The complete mechanism is explained in detail in terms of Statistical data.

Employee Attrition Prediction is in which under stable circumstances, wherein a set pattern can be assumed from certain parameters for influencing the employee and the organization at all times. Some of these parameters could be probable such as retirement age or unforeseeable such as company performance, external funding, management shakeup, etc.

Decision Tree Case Study Using R is next in which the versatile Machine Learning algorithm of the decision tree can perform both classification and regression tasks. This is known to be a very powerful algorithm, capable of fitting complex datasets. It will help the trainee to fit complex problems on the datasheet easily.

Project Highlights

Project on ML – Predicting Prices is the first project on how machine learning can help predict prices using various factors. The detailed information about the projects will be explained by a video tutorial of almost two hours long.

Project on R – Forecasting using R is the next project where forecasting of data will be prepared using R programming so that data can be visualized; it includes patterns, unusual observations, and changes over time are recorded altogether.

Project on R – Telecom Customer Churn will be the next project where you need to predict employee turnover which can be predicted by customer churn using the telecom dataset. Different methods of analysis tools can be used.

Project on R – Predictive Model is the next in which using different concepts of R programming to build a predictive model that can be used to make the statistical and math computation easy and better than machine learning. Details will be explained using the video tutorials.

Project on ML – Churn Prediction Model is next in which we have to predict the percentage of customers that discontinue using a company’s products or services during a particular period using machine learning. The detailed information about the projects will be explained by a video tutorial of almost one hour long.

Project on Python Data Science will be the last project in this training program in which you need to make a project on data science using python programming as it is an open-source, understandable, high-level language and which provides a great approach for object-oriented programming so how it can be used.