Statistical Analysis Training (15 Courses, 10+ Projects)

Objectives

With the help of this training program on statistical analysis, the individuals would be able to understand the core concepts and terminologies related to statistical analysis that can help them understand the industry requirements too. The training program is divided by keeping in mind the industry requirements and the individual needs and focuses on scattering the best possible solution for both. There are varieties of skill that is going to be taught under the training program like statistics, linear algebra, probability, gradient descent, predictive analysis, color models, classifiers, random forest, grid search, affinity propagation model, etc.

With the help of these skills, the participants would be able to who started their career as a data analyst, statistical analyst, software test engineer, quality assurance engineer, test analyst, test lead, analytics manager and many other diverse opportunities that are open for the individual’s ones this training program is completed successfully.

Course Highlights

Several skills are covered under this training program keeping in mind the industry requirements and individual needs.some of the core skills that are going to be covered under the program are as follows: Statistical tools in Microsoft Excel is one of the basic skill that is going to be covered in this training which includes an introduction to statistical tools in Excel, descriptive statistics, Central tendencies, mean, mode, median that is divided into three different sections. Descriptive statistics that use dispersion and standard deviation, data analysis tools, Central tendencies, and dispersion using data analysis, correlation, and regression, histogram using data analysis, moving averages using data analysis, etc. Another skill that is covered in machine learning and the essentials of statistics. Under this module, one will learn about introduction to machine learning with Python, analytics in machine learning, big data in machine learning, emerging trends in machine learning, data mining, supervised and unsupervised learning, basics of statistical sampling which includes sampling method in machine learning, technical terminologies, the error of observation and nonobservation, systematic sampling, cluster sampling, basics of statistics data types and visualization, qualitative data visualization, basics of statistical probability, relative frequency probability, joint probability, conditional probability, the concept of independence, total probability, random variables, the probability with distribution, probability distribution. Matrix algebra with the use of mathematical expressions and computation, transpose of a matrix, properties of the matrix, determinants, hypothesis testing, hypothesis testing types, etc. Statistics for data science using python module covers an in-depth study of python and statistics which includes an introduction to data science, calculating mode, correlation functions, basics of reservation techniques, the summation of elements, methods of testing, exclusive events, statistics for data science, analyzing the test statistics, the output of the variables, filtering the models, ordinary least square regression techniques, etc. Under the module of statistic essential for analytics when will learn about introduction to elements of statistics, a random number in Excel, variable and types of variables, quantitative and qualitative research, understanding of ordinal scales, different graphical techniques, bar chart using R, pie charts, entering value in two variables, histogram bar charts, scatter plots, sampling techniques, etc. Hands-on of a language that uses statistics and business analytics. visualization and graphical analysis using tabular software. Splunk fundamentals basic concepts of the same technology.

Project Highlights

Many projects are part of this training program projects are as follows: A project on predictive modeling using SPSS which includes importing data sets in the text and CSV and many other different file formats, understanding statistics, correlation techniques, basic correlation theory, interpretation, implementation, data editor, linear regression models, multiple regression models, logistic regression, multinomial regression, etc. A project on predictive modeling using Minitab which includes mini types and its specification to predictive modeling, Anova testing using Minitab, correlation techniques, regression models, predictive modeling using MS Excel, etc. A project on predictive modeling using SAS which includes predictive modeling training, basics of SAS, etc. A project on R language that uses card purchase protection techniques and output generation using different data sets of text and CSV file format.