Explore Our Comprehensive Training Curriculum on Data Science

Introduction to R and RStudio
Overview of R Programming
Environmental Setup of R

R packages and libraries
Basic data manipulation in R

Data formats (Excel, CSV, etc.)
Reading and writing data files

Data visualization principles
Creating bar chart and dot plot
Creating histogram and box plot
Customizing plots

Introduction to the dplyr package
Data filtering, sorting, and summarizing
Data reshaping and pivoting

Merging and joining data
Data Transformation

Computing basic statistics
Descriptive Statistics
Data Munging Basics
Comparing means of two samples

Linear regression
Logical Regression
Hypothesis Testing
Non-parametric tests

Identifying and handling missing data
Reshaping data

Introduction to Machine learning
Splitting data into training and testing sets
Supervised vs. unsupervised learning
Linear and logistic regression

Decision trees and random forests
k-means clustering
Evaluation metrices for classification and clustering

Python Basics

Introduction to Python

Get Started – Hello World program

IDLE for Compiling & Running program

Basic Data Types and Assignments

Identifiers and Indentation

Data Operations

Sequence Types: Tuples, Lists

Operators and Expressions

Dictionary and Sets

Control Structure

Functions

Variable Scope – Global, Local, and Non-Local

Logging and Debugging

Modules and Packages

Python execution environment & tools

Python Intermediate

Lambda Functions

Comprehensions: List, Set, and Dictionary

Extending Built-in Types

Custom Collection Classes

Iterators

Decorators

Generators

Streams

Context Manager

Functional Tools

Python Advanced

Files Handling and Globbing

Exception Handling

Date, Time, and Calendar APIs

Command Line Frameworks

Regular Expressions and Parsing

OOPS Concepts

Classes and Objects

Instance and Instantiation

User Defined Classes

Member Variables and Methods

Constructors and Destructors

Super Class

Default Attributes and Methods

Inheritance and Override Methods

Polymorphism

Function and Operator Overloading

Abstract Classes

Multiple Inheritance

Pseudo Private Attributes and Functions

__getattribute__ Method

Encapsulation and Intercepting Attribute Access

Class and Static Method

Properties and Descriptors

Coding Style & Guidelines

Unit Testing

Documentation and Best Practices

Distributing Applications

• Python Basics

  • Types

  • Control Flow

  • Organizing Code

  • Reading and Writing Files

  • Object-Oriented Programming (OOP)

  NumPy & 2D Plotting Library

  • N-dimensional Data Structure

  • Creating Arrays

  • Indexing Arrays

  • Array Operations and Manipulations

  • Plotting with Matplotlib

  Python Pandas & Data Analysis

  • Tabular Datasets

  • Data Aggregation & Exploration

  • Labelling Data

  • Handling Missing Values & Time Series

  Accessing Data from Multiple Sources

  • Local Files (.txt, .csv, .xls, .json)

  • Remote Files

  • Web Scraping (.html)

  • Using Read Table Method

  Data Preparation & Cleaning

  • Pandas Data Structures: Series & DataFrames

  • Indexing, Slicing, Fancy & Boolean Indexing

  • Data Wrangling

  • Adding, Dropping, Selecting, Combining Rows & Columns

  Data Visualization

  • Structure of a Figure

  • Scatter, Line, Box, Bar, Histogram Plots

  • Customizing Plots

  Data Analysis

  • Split-Apply-Combine with DataFrames

  • Summarization & Aggregation

  • Group By Method

  • Reshaping, Pivoting, Transforming

  • Simple & Rolling Statistics

  Python Data Science

  • Linear Regression

  • Support Vector Machine (SVM)

  • K-Nearest Neighbors (KNN)

  • Logistic Regression

  • Decision Tree

  • K-Means

  • Random Forest

  • Naive Bayes

  • Dimensional Reduction Algorithms

  • Gradient Boosting Algorithms

• Introduction to R

  • Introduction to R and RStudio

  • Overview of R Programming

  • Environmental Setup of R

  Fundamentals of R

  • Features of R

  • Variables in R

  • Constants in R

  • Operators in R

  • Datatypes and R Objects

  Vectors

  • Creating Vectors

  • Accessing Elements of a Vector

  • Control Statements

  Functions in R

  • Formal and Actual Arguments

  • Named Arguments

  • Global and Local Variables

  Matrices and Strings

  • Creating Matrices

  • Accessing Elements of a Matrix

  • Creating Strings

  • String Manipulation

  Arrays in R

  • Creating Arrays

  • Accessing Array Elements

  • Calculations Across Array Elements

  R Factors

  • Understanding Factors

  • Modifying Factors

  • Factors in Data Frames

  Data Frames in R

  • Creating Data Frames

  • Operations on Data Frames

  • Accessing Data Frames

  • Creating Data Frames from Various Sources

  Data Visualization in R

  • Creating Bar Chart and Dot Plot

  • Creating Histogram and Box Plot

  • Customizing Plots

  STRINGR Package and DPLYR Package

  • Important Functions in stringr

  • Regular Expressions

  • Load Data into DataFrame

  • Viewing the Data

  • Selecting Columns

  • Selecting Rows