Statistics for Data Analysis using R

Intermediate
10 sections
52 topics
Duration: 12 weeks

N70000

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What you'll learn

A range of statistical concepts and techniques using the R programming language.

Course content

  • Overview of R and RStudio
  • Installing and setting up
  • Introduction to RStudio IDE
  • Basic R Commands and Syntax
  • Data types, variables, and operators
  • Functions and control structures in R
  • Introduction to Statistical Concepts
  • Descriptive statistics
  • Inferential statistics
  • Probability distributions

  • Summary Statistics and Visualization
  • Measures of central tendency and dispersion
  • Histograms, box plots, and scatter plots
  • Data Cleaning and Preprocessing
  • Handling missing data
  • Outlier detection and treatment

  • Probability Distributions in R
  • Normal distribution, binomial distribution, and others
  • Generating random numbers in R
  • Statistical Inference
  • Confidence intervals
  • Hypothesis testing
  • P-values and significance levels

  • Simple Linear Regression
  • Model fitting and interpretation
  • Residual analysis
  • Multiple Linear Regression
  • Including multiple predictors
  • Assumptions and diagnostics

  • T-tests in R
  • One-sample, two-sample, and paired t-tests
  • Analysis of Variance (ANOVA)
  • One-way ANOVA
  • Post-hoc tests and multiple comparisons

  • Wilcoxon Rank Sum and Signed Rank Tests
  • Kruskal-Wallis Test
  • Chi-square Test for Independence

  • Time Series Data in R
  • Handling time series data
  • Time series visualization
  • Time Series Decomposition and Forecasting
  • Seasonal decomposition of time series (STL)
  • Time series forecasting with ARIMA models

  • Applying Statistical Techniques to Real-world Data
  • Case Studies and Projects
  • Analyzing real-world datasets using R
  • Presenting findings and insights

  • Code optimization and efficiency in R
  • Best practices in statistical analysis
  • Version control and collaboration with Git

  • Developing a Statistical Analysis Project
  • Presenting and sharing the project results

Requirements

  • Basic knowledge of statistics and familiarity with programming concepts is recommended for this course.
  • Participants should have access to a computer with R installed to actively engage in hands-on exercises and assignments.

Description

Master statistical data analysis with R through hands-on courses. Learn essential R programming skills for data manipulation, exploration, and visualization. Gain expertise in statistical techniques and hypothesis testing to extract meaningful insights from diverse datasets

Who this course is for:

Persons who want to enhance their data analysis skills and learn how to use statistical techniques to derive meaningful insights from data.