Time Series Course (4 Courses, 7+ Projects)

Objectives

the training program of time series analysis is a bundle of many different tools and technologies that are going to be covered and taught at this training program. The curriculum is developed by keeping in mind different requirements of the industries as well as the individuals who are looking forward to learning something new and demanding too in nature. survival skills that individuals will learn in this training program are statistical tools, Microsoft Excel, machine learning, python, data analysis, forecasting tools and techniques, basics of the R language, and many other skills that are very much demanded in nature. With the knowledge of these skills when would be able to secure a job in the industry as a data analyst, business analyst, predictive analyst, machine learning analyst, consultant, and a pool of other opportunities that are open for the individual once they will complete this training program successfully.

Course Highlights

Many skills are going to be covered under this training program. Some of the core skills that one will learn through this program are as follows:

Under the module of statistical tools in Microsoft Excel when will learn about introduction to the statistical tools in Microsoft Excel, references, descriptive statistics, Central tendencies, mean, mode, and median the discovered of the three different parts.

Descriptive statistics that cover dispersion and standard deviation, data analysis tools, data analysis with the use of Central tendencies, correlation and regression, histograms in bar charts using data analysis, moving averages using data analysis, etc.

Under the module of machine learning which emphasis on statistical essentials one will learn about introduction to machine learning with the use of python, introduction to analytics with the use of machine learning, big data in machine learning, imaging trends in machine learning, data mining, supervisor unsupervised learning, basics of statistical sampling, sampling methods machine learning, technical terminology, the error of observation and nonobservation, systematic sampling, cluster sampling, different types of data, qualitative data and visualization, basics of statistics probability, relative frequency probability, joint probability, conditional probability, the concept of independence, basics of statistics random variables, probability distribution, cumulative probability distribution, basics of statistics distribution, geometric distribution, normal distribution, Matrix algebra, transpose of a matrix, properties of the matrix, mathematical expressions and computation, hypothesis testing, types of errors, critical value approach, p-value approach, confidence interval, types of hypothesis testing, normal and no normal distribution, normality test, t-test, a test of independence, regression, covariance, etc.

Under the module of statistics for data science using python, a participant would learn about introduction to data science, calculator mode, calculating dispersion means, basic techniques, testing methods, differences in NumPy packages, exclusive events, statistics for data science, analysis of test statistics, the output of the variables, best fit to the, etc.

Under this module of forecasting, a participant would learn about different forecasting tools and techniques that are used with the use of our language, predictive analysis, predictive analysis using SPSS, etc.

Project Highlights

There are many projects involved in this training program that helps to ensure the quality and effectiveness of the learning and skills that a transfer to the participants. Some of the core models later part of this training are as follows:

A project on Eviews that uses univariate time series modeling is a great opportunity for individuals to learn about univariate modeling. Under this one will learn about univariate time series modeling, Correloghlram analysis, estimation of output analysis and interpretation, interpretation of the ARMA model, etc.

A project on forecasting using R language which includes introduction to the business analytics forecasting, introduction to the forecasting, methods of forecasting, steps of forecasting, problems with forecasting, simple forecasting methods, transformation and adjustments, regression and multiple linear regression, time series decomposition, developing different models, etc.

A project on predictive modeling the use of SPSS tool that helps in understanding the introduction to the basics of SPSS tool, generation of output, data loading, etc.

A project on predictive modeling using Minitab that involves the use of reference files, introduction to the project, applications of predictive modeling, etc.