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: What's 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
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Prag Robotics
Robotics & A.I.
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Frequently Asked Questions
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Can I take my courses with me wherever I go?
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