Data Science with R & Python
Duration : 2 Days
About the Program
At the end of this module the participants should get a detailed understanding of how clearly define a problem statement, How to fetch the data, exploratory analysis and if required do some pre-processing of the data, Build the model, validate it and finally decide on an optimal model, Test the model and deploy it in production using applications like Shiny. This training will use R as base Analysis tool and parallelly discuss on Python.
In this session we will cover:
- What is Data Science
- Why we need Statistical Analysis Tools for Data Science?
- Languages/Applications in R (3.x) & RStudio (1.x)
- Understanding Python Language and the Python Ecosystem
- Installation and configuration of Anaconda (Python 3.x ), R(3.x) &RStudio (1.x)
- Basic Statistical Analysis and Hypothesis Testing
- Development Life cycle
- Data extraction – Exploratory Analysis – DataWrangling&Preparation–Model building – Model Tuning and testing – Deployment of Model
- Algorithms
- Linear Regression and Logistic Regression
- Decision Trees
- Time Series Analysis
Case Study
- One for Regression and
- One forClassification
