This course will introduce to R programming and it's implementation in data science. It also provides flavour of complete end to end journey of any data science project i.e. connection of R with data sources, manipulation and data engineering, exploratory analysis and visualisations, predictive modelling and connection to any other platform.
Basic Knowledge of Statistics will be helpful
She is a statistician passionate about leveraging data science for mining out meaningful insights for business. she has experience of working for different business industries like BFSI, edutech, e-commerce, aviation, telecom and foodtech. Her Professional Skills include Probability, Statistics, Machine Learning, PostgreSQL, R, SPSS, SAS, Machine learning, Hive, Elastic search, Kibana and Python. She is presently working with Thevalley.nl and has worked in past with organizations like Zomato, Alqimi and Transorg in Data Scientist roles. She was also a Research Associate at IIIT Delhi.
R Nuts and Bolts
Getting Data In and Out of R
Subsetting R Objects
Vectorized Operations
Dates and Times
Control Structures
apply Family of Functions
Sampling in R
Basics of Distribution of Data
EDA for Individual Variables:
EDA for Multiple Variables:
Case Study: EDA for Motor Trend Car Road Tests Dataset
Parameter Estimation
Parametric Testing of Hypothesis
Non-Parametric Testing of Hypothesis
Case Study: Parametric and Non-Parametric Tests
Model Building
Multicolloinearity
Parsimonious Modelling or Model Selection
Validation of Assumptions and Residual Analysis
Case Study: Regression Analysis for Motor Trend Car Road Tests Dataset
Estimating and eliminating the deterministic components if they are present in the model
Modeling the residual using Auto Regressive Integrated Moving Average (ARIMA) model
Case Study - Forecasting and Time Series Analysis for Air Passengers Data