Useful for ISS
A fair question, and worth answering properly because you will be asked it.
R was built by statisticians, for statistics. Python is a general-purpose
language that grew a data stack. That difference shows: a linear model in R is
lm(y ~ x, data) and the summary already contains coefficients, standard
errors, t-values, p-values and R². In Python you assemble that yourself.
R dominates in academic statistics, biostatistics, clinical trials and econometrics. Python dominates in production machine learning and engineering. The employable answer is that you can read and write both — and that is why this course sits beside Python for Data Analysis and Visualization rather than replacing it.
ggplot2 is also, plainly, the best plotting library in either language, and
it is worth learning for that alone.
Introduce the data science process, lifecycle, and applications in real-world domains.
Build proficiency in R programming for data manipulation, exploration, and visualization.
Train students in handling structured, unstructured, and time-based data effectively.
Familiarize with basic machine learning and statistical modeling using R.
Perform data wrangling, cleaning and visualization with dplyr, tidyr,
ggplot2.
Build and evaluate basic machine learning models such as regression and clustering.
Apply data science techniques to practical case studies.
Definition and applications; the Data Analytics Life Cycle; the toolkit and the team; exploratory data analysis; feature engineering and data transformation.
UNIT 2R and RStudio; data types and structures; operators; control structures and the apply family; functions and packages; reading CSV, Excel, JSON and XML.
UNIT 3The pipe and the five dplyr verbs; tidyr reshaping; missing data; dates and times; ggplot2 — grammar of graphics, geometries, scales, faceting and export.
UNIT 4Simple and multiple regression; confusion matrix, precision, recall, ROC and AUC; K-Means clustering; text mining and TF-IDF; recommender systems; ethics in data science.
UNIT 5Time series — decomposition, stationarity, differencing, ACF/PACF, ARIMA and forecasting; interactive plots with plotly; building web applications with R Shiny.
PRACTICEExam-style questions with fully worked solutions.
LABEvery prescribed lab experiment, with code and expected output.