Statistical Analysis Methods (using R)
Description
To be very good data scientists, it is important to know the statistical logic behind any analytics solution that you provide. In this course, you will learn the essentials of statistics in a practical way with real world examples.
COURSE OBJECTIVES
•To learn the required theories and concepts of statistics
•To learn how to prepare your data for statistical analysis
•To learn programming in R
•To learn how to do exploratory analysis in R
•To know how to identify hypothesis in data science projects
•To know how to run different statistical tests to do hypothesis testing
•To undestand how to interpret the p-value and confidence interval
•To can install and run a program using Hadoop!
•To know the best practices on implementing Big Data
•To learn how to build a Big Data strategy in your own organization
COURSE SYLLABUS
•Installing R and R-Studio
•Data preparation and cleaning methods in R
•Data types in R
•Categorical data analysis
•Introduction to probability
•Hypothesis testing
•T-Test and Anova test
•Inference: p-values and confidence intervals
•Descriptive Statistics
•Exploratory analysis
•Drawing different graphs
Registration fee :
€ 690
Target audience
- Data Scientists
- Business Analysts
- IT staff
- Computer Science and IT Students
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Requirements
- Basic knowledge on data analytics
- Basic programming
- experience in R