Bootcamp on Data Science using R language

Bootcamp on Data Science using R language, available at $19.99, with 51 lectures, 5 quizzes.

You will learn about Definition of Data Science Data Collection & Pre-processing Statistics Predictive Modelling This course is ideal for individuals who are Anyone interested in the field of Data Science It is particularly useful for Anyone interested in the field of Data Science.

Enroll now: Bootcamp on Data Science using R language

Summary

Title: Bootcamp on Data Science using R language

Price: $19.99

Number of Lectures: 51

Number of Quizzes: 5

Number of Published Lectures: 51

Number of Published Quizzes: 5

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: ?4,499

Quality Status: approved

Status: Live

What You Will Learn

Definition of Data Science

Data Collection & Pre-processing

Statistics

Predictive Modelling

Who Should Attend

Anyone interested in the field of Data Science

Target Audiences

Anyone interested in the field of Data Science

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!!!

Course Curriculum

Chapter 1: About the Program

Lecture 1: Course Introduction

Lecture 2: Course Outline

Chapter 2: Introduction to Data Science

Lecture 1: What is Data Science?

Lecture 2: What is Data?

Lecture 3: Whats the Job with Data

Lecture 4: Data Science Tools & Technologies

Lecture 5: Data Science Process Flow

Lecture 6: Applications of Data Science

Chapter 3: Foundations of R

Lecture 1: Introduction to R Language

Lecture 2: Installation of R Language and R Studio

Lecture 3: Handling R Environment

Lecture 4: Setting Working Directory

Lecture 5: Data Types and Variables

Lecture 6: Arithmetic Operations

Lecture 7: Data Frames

Chapter 4: Data Collection

Lecture 1: Data Science Methodology

Lecture 2: Data Collection Techniques

Lecture 3: Introduction to Web Scraping

Lecture 4: Web Scraping Using R Language

Chapter 5: Data Pre-processing

Lecture 1: Significance of Data Pre-processing

Lecture 2: Checking Data Formats

Lecture 3: Handling Missing Data

Lecture 4: Handling Categorical Data

Lecture 5: Outlier Analysis

Lecture 6: Data Scaling

Chapter 6: Descriptive Statistics

Lecture 1: Significance of Statistics in Data Science

Lecture 2: Descriptive Statistics Tools for Data Science

Lecture 3: Measure of Central Tendency

Lecture 4: Variation in Data

Lecture 5: Association of Variables

Chapter 7: Inferential Statistics

Lecture 1: What is Inferential Statistics?

Lecture 2: Confidence Intervals

Lecture 3: Confidence Intervals in R Language

Lecture 4: Student T-Distribution

Lecture 5: T-Test in R Language

Lecture 6: Hypothesis Testing

Lecture 7: Hypothesis Testing in R Language

Chapter 8: Predictive Modelling

Lecture 1: What is Predictive Analytics?

Lecture 2: Introduction to Linear Regression

Lecture 3: Simple Linear Regression in R Language

Lecture 4: Introduction to Multiple Linear Regression

Lecture 5: Multiple Linear Regression in R Language

Chapter 9: Classification

Lecture 1: Introduction to Classification Models

Lecture 2: Introduction to Logistic Regression

Lecture 3: Implementation of Logistic Regression

Lecture 4: Introduction to Random Forest Classification

Lecture 5: Random Forest Classification in R Language

Chapter 10: Dimensionality Reduction

Lecture 1: Introduction to Dimensionality Reduction

Lecture 2: Introduction to Principle Component Analysis

Lecture 3: Principle Component Analysis in R Language

Chapter 11: About the Program

Lecture 1: Course Conclusion

Instructors

Prag Robotics
Robotics & A.I.

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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