The Statistics for Data Science course is designed to provide students with a comprehensive introduction to statistical methods commonly used in data science. This course will cover topics such as probability theory, statistical inference, hypothesis testing, and regression analysis. Students will also learn about the statistical programming language R and how it can be used to perform data analysis and create visualizations.Throughout the course, students will apply their knowledge of statistics to real-world data sets and practice using statistical techniques to draw meaningful insights and make data-driven decisions. By the end of the course, students will have a strong foundation in statistical theory and the practical skills needed to analyze and interpret data for a variety of applications in data science. This course is ideal for anyone who wants to pursue a career in data analysis or data science, as well as professionals who want to enhance their statistical knowledge and skills.
Schedule: Monday, Wednesday, Friday - 6pm - 9pm
Get trained by Industry expertsOur courses are delivered by professionals with years of experience having learned first-hand the best, in-demand techniques, concepts, and latest tools.
Official Certification curriculumOur curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
Tax CreditClaim up to 25% of tuition fees and education tax credit from your taxes.
Discount on Certification VoucherUpto 50 percent discount voucher will be provided.
24/7 Lab accessOur students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.
Statistics for Data Science
This module explains about the various types of data, levels of measurement, categorical variables, visualization techniques, numerical variables, histogram charts, cross tables and scatter plots.
This module provides information about the concepts of main measures of central tendency are mean, median and mode, skewness, variance, standard deviation and coefficient of variation, covariance, and correlation coefficient.
This module provides information about the types of data; levels of measurement; graphs and tables for categorical and numerical variables, and relationship between variables; measures of central tendency, asymmetry, variability, and relationship between variables.
This module explains about the Inferential statistics, what is a distribution, normal distribution, standard normal distribution, central limit theorem and the standard error.
This module explains about the estimators and estimates in decision making, confidence intervals used to calculate confidence intervals within a population with an unknown variance. A margin of error is important in Statistics.
This module explains about the calculating confidence intervals for two means with dependent samples, Confidence intervals, two means, dependent samples.
This module explains about the null and alternative hypotheses and the differences, establishing a rejection region and a significance level, rejection region and significance level and Type I error vs Type II error.
This module makes you to learn about the test for the mean, population variance known, p-value, test for the mean, dependent samples, and test for the population variance unknown.
This module explains about the regression analysis, Correlation and causation, linear regression model made easy, linear regression model, difference between correlation and regression, Correlation vs regression and geometrical representation of the linear regression model.
This module makes you to learn about the decomposing the linear regression model - understanding its nuts and bolts, Decomposition, R-squared, ordinary least squares setting and its practical applications, Studying regression tables, Regression tables. Exercise, multiple linear regression model and adjusted R-squared.
This module provides information about the OLS assumptions require linearity, no endogeneity, normality and homoscedasticity, no autocorrelation, and no multicollinearity.
While we encourage all interested applicants to apply, to enter our certification program you must be :
Interested in gaining IT knowledge and enter into real world IT domain, switching carrears in IT or applying for entry level positions
Statistics for data Science Certification.
Upon completing this cerification course you will: