Bootcamp On Data Science Using R Language

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Bootcamp On Data Science Using R Language

Published 12/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.11 GB | Duration: 7h 13m

Building Data Science Pipelines

What you'll learn
Definition of Data Science
Data Collection & Pre-processing
Statistics
Predictive Modelling

Requirements
None

Description
Data science is a multidisciplinary field that uses a combination of techniques, algorithms, processes, and systems to extract meaningful insights and knowledge from structured and unstructured data. Data science is of significant importance in today's world due to its transformative impact on various aspects of business, research, and decision-making. It incorporates elements of statistics, computer science, domain expertise, and data analysis to analyse and interpret complex data. Data science enables organizations to make informed decisions based on data analysis rather than relying solely on intuition or experience. This leads to more accurate and effective decision-making processes. During this course, students will learn the entire process of developing a data science project. During this course, students will learn the nuances of Data science, data collection, data cleaning, data visualization, Significance of statistics and Machine learning etc. We will be using r programming language to develop data pipelines. R is a programming language and environment specifically designed for statistical computing and graphics. It is open-source and widely used by statisticians, data scientists, researchers, and analysts for data analysis, statistical modelling, and visualization. R has a rich ecosystem of packages and libraries that extend its functionality. These packages cover a wide range of domains, from machine learning and data manipulation to bioinformatics and finance. So, let's buckle up!!!

Overview
Section 1: About the Program

Lecture 1 Course Introduction

Lecture 2 Course Outline

Section 2: Introduction to Data Science

Lecture 3 What is Data Science?

Lecture 4 What is Data?

Lecture 5 What's the Job with Data

Lecture 6 Data Science Tools & Technologies

Lecture 7 Data Science Process Flow

Lecture 8 Applications of Data Science

Section 3: Foundations of R

Lecture 9 Introduction to R Language

Lecture 10 Installation of R Language and R Studio

Lecture 11 Handling R Environment

Lecture 12 Setting Working Directory

Lecture 13 Data Types and Variables

Lecture 14 Arithmetic Operations

Lecture 15 Data Frames

Section 4: Data Collection

Lecture 16 Data Science Methodology

Lecture 17 Data Collection Techniques

Lecture 18 Introduction to Web Scraping

Lecture 19 Web Scraping Using R Language

Section 5: Data Pre-processing

Lecture 20 Significance of Data Pre-processing

Lecture 21 Checking Data Formats

Lecture 22 Handling Missing Data

Lecture 23 Handling Categorical Data

Lecture 24 Outlier Analysis

Lecture 25 Data Scaling

Section 6: Descriptive Statistics

Lecture 26 Significance of Statistics in Data Science

Lecture 27 Descriptive Statistics Tools for Data Science

Lecture 28 Measure of Central Tendency

Lecture 29 Variation in Data

Lecture 30 Association of Variables

Section 7: Inferential Statistics

Lecture 31 What is Inferential Statistics?

Lecture 32 Confidence Intervals

Lecture 33 Confidence Intervals in R Language

Lecture 34 Student T-Distribution

Lecture 35 T-Test in R Language

Lecture 36 Hypothesis Testing

Lecture 37 Hypothesis Testing in R Language

Section 8: Predictive Modelling

Lecture 38 What is Predictive Analytics?

Lecture 39 Introduction to Linear Regression

Lecture 40 Simple Linear Regression in R Language

Lecture 41 Introduction to Multiple Linear Regression

Lecture 42 Multiple Linear Regression in R Language

Section 9: Classification

Lecture 43 Introduction to Classification Models

Lecture 44 Introduction to Logistic Regression

Lecture 45 Implementation of Logistic Regression

Lecture 46 Introduction to Random Forest Classification

Lecture 47 Random Forest Classification in R Language

Section 10: Dimensionality Reduction

Lecture 48 Introduction to Dimensionality Reduction

Lecture 49 Introduction to Principle Component Analysis

Lecture 50 Principle Component Analysis in R Language

Section 11: About the Program

Lecture 51 Course Conclusion

Anyone interested in the field of Data Science


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