Data science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, manufacturing, advertising, retail, and others. Companies are searching for data scientists. This specialised field demands multiple skills not easy to obtain through conventional curricula.
Introduction to Data Science using R lives up to its name. It highlights basic principles of data science and focuses on developing the understanding and the capabilities you need to fully appreciate the insights data can provide us today. You'll apply the R programming language and statistical analysis techniques to carefully-explained examples. This course will cover the elements that make up data science so that you understand the basic concepts and become confident in applying data science to real world data challenges.
Who Should Take This Course?
Have you tried learning data science and R from books or online, but have been discouraged? If so, this is the course for you.
This course is for technology professionals, business professionals, analysts, journalists, or anyone interested in understanding what data science is and wants to learn how to use R for data analysis and modelling. This is also a great opportunity for recent university graduates who would like to explore data science as a career possibility.
This 3 day course course teaches the basic skills needed by anyone seriously interested in data science and learning R.
An Introduction to Data Science
Component Parts of Data Science - Engineering a Data Science solution
Data Science Life Cycle – A strategy to approach any data analytics problems
Overview and Introduction to R
Getting started and working with data
Reading and writing data with R
Programming efficiently in R
Descriptive Statistics and Introduction to Probability Distributions
Visualizing data and Exploratory Data Analysis
Real World Data Challenge – Part 1
Statistics and Modelling in R
Fit a model to data in R
Explore data sets with models
Basic statistical tests, power, and sample size functions
Correlation and Regression
Pearson, Spearman, Kendall correlations.
Linear Regression – residuals, fitted values, predictions and confidence intervals.
Analysis of Variance
Real World Data Challenge – Part 2
An undergraduate level of mathematics with some elementary statistics is required and some familiarity with basic programming languages and environments is desirable as some of the course exercises will involve scripting in R. Also, some hands on business experience will help but is not essential.
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