STATCRUNCH
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 realworld projects and gain practical experience in data analytics
10 High Quality
lesson
hands on learning
Customized course structure
Flexible payment plan
By the End of the course Student will be able to :
Topic Covered
Statistics
RMarkdown
R
Time Series
Regression

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

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

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

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 (12hr)

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 pvalues

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