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Data Analyst in R 

  • Offered byDataquest

Data Analyst in R
 at 
Dataquest 
Overview

Learn R, one of the most popular programming languages among data scientists, and set yourself up for success as a highly qualified and in-demand data analyst

Duration

4 months

Total fee

Free

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Beginner

Official Website

Explore Free Course External Link Icon

Credential

Certificate

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Data Analyst in R
 at 
Dataquest 
Course details

Who should do this course?
  • For anyone looking for a new and exciting career in data analysis. Regardless of prior experience or education level, learn all the skills and tools you need to acquire and land the dream data analyst position
What are the course deliverables?
  • Basic and intermediate R programming
  • Data analysis, cleaning, and visualization
  • Data structures and processing
  • Control flow, iteration, and functions
  • SQL queries
  • Web scraping using APIs and the web
  • Statistics, probabilities, and hypothesis testing
  • Machine learning and linear modeling
More about this course
  • This career path consists of courses that include Intro to Data Analysis in R through advanced topics like Advanced Data Cleaning in R and Web Scraping in R
  • Students will be writing real code and answering practice problems to continuously validate and apply their new skills
  • At the end of each course, they will complete a guided project to enhance your resume and reinforce what they have learned
  • After completing the entire path, they will have the skills necessary to become a data analyst in R
  • At any time, learner can redo practice problems and lessons if they want to review or keep their skills sharp

Data Analyst in R
 at 
Dataquest 
Curriculum

Part 1: Introduction to R

Introduction to Data Analysis in R

Data Structures in R

Control Flow, Iteration, and Functions in R

Specialized Data Processing in R

Part 2: Data Visualization in R

Introduction to Data Visualization in R

Part 3: Data Cleaning in R

Introduction to Data Cleaning in R

Advanced Data Cleaning in R

Part 4: Working with Data Sources

SQL Fundamentals

Intermediate SQL in R

Introduction to APIs in R

Introduction to Web Scraping in R

Part 5: Probability and Statistics

Introduction to Statistics in R

Intermediate Statistics in R

Introduction to Probability in R

Conditional Probability in R

Hypothesis Testing in R

Part 6: Predictive Modeling and Machine Learning in R

Linear Regression Modeling in R

Introduction to Machine Learning in R

Part 7: Shiny Applications in R

Introduction to Interactive Web Applications in Shiny

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Data Analyst in R
 at 
Dataquest 

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