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Person Analyzing Statistics

Introduction to R and Statistic

A practical approach to learn statistics with R

10 hours of 1:1 lessons

Course Overview 

Learn R programming and data analytics in this comprehensive 10hr course. Master data manipulation, cleaning, and visualization techniques using R. Explore statistical analysis, Regression and time series analysis. Develop advanced visualization skills. Apply your knowledge to real-world projects and gain practical experience in data analytics

10 High Quality 



hands on learning

Customized course structure

Flexible payment plan

By the End of the course Student will be able to

Topic Covered




Time Series


  1. Develop ability to collect, organize, and analyze data using appropriate statistical methods.

  2. Proficiency in R programming language, including data manipulation, cleaning, and visualization techniques.

  3. Understanding of statistical analysis concepts and how to apply them using R, including hypothesis testing, regression analysis, and analysis of variance.

  4. Knowledge of predictive modeling and techniques for  regression and time series tasks using R.

What we will be covering in 20 Lesson :


Session 1: Introduction to R and Data Analytics       (1 hr)

  • Introduction to R programming language

  • RStudio and R environment setup

  • Basic data types and data structures in R

  • Importing and exporting data in R

Session 2: Data Manipulation and Cleaning in R      (1-2hr)

  • Data manipulation techniques: subsetting, filtering, and transforming data

  • Handling missing values and outliers

  • Data cleaning and preprocessing

  • Exploratory data analysis (EDA) using R

Session 3: Data Visualization in R                               (2 hr)

  • Introduction to data visualization in R

  • Basic plotting with base R graphics

  • Advanced data visualization with ggplot2

  • Customizing plots, adding labels, and annotations

Session 4: Statistical Analysis in R                           (4 hr)

  • Descriptive statistics and inferential statistics in R

  • Hypothesis testing and p-values

  • Analysis of variance (ANOVA)

  • Regression analysis: linear regression, logistic regression

Session 7: Time Series Analysis in R                          (2 hr)                 

  • Introduction to time series data

  • Time series decomposition and trend analysis

  • ARIMA models for time series forecasting

  • Seasonal and trend forecasting


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