R Programming: Advanced Analytics In R For Data Science from Udemy

What's inside

Learning objectives

Perform data preparation in r
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the median imputation method to replace missing records
Apply the factual analysis method to replace missing records
Understand how to use the which() function
Know how to reset the dataframe index
Work with the gsub() and sub() functions for replacing strings
Explain why na is a third type of logical constant
Deal with date-times in r
Convert date-times into posixct time format
Create, use, append, modify, rename, access and subset lists in r

Understand when to use [] and when to use [[]] or the $ sign when working with lists
Create a timeseries plot in r
Understand how the apply family of functions works
Recreate an apply statement with a for() loop
Use apply() when working with matrices
Use lapply() and sapply() when working with lists and vectors
Add your own functions into apply statements
Nest apply(), lapply() and sapply() functions within each other
Use the which.max() and which.min() functions
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Perform data preparation in r
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the median imputation method to replace missing records
Apply the factual analysis method to replace missing records
Understand how to use the which() function
Know how to reset the dataframe index
Work with the gsub() and sub() functions for replacing strings
Explain why na is a third type of logical constant
Deal with date-times in r
Convert date-times into posixct time format
Create, use, append, modify, rename, access and subset lists in r
Understand when to use [] and when to use [[]] or the $ sign when working with lists
Create a timeseries plot in r
Understand how the apply family of functions works
Recreate an apply statement with a for() loop
Use apply() when working with matrices
Use lapply() and sapply() when working with lists and vectors
Add your own functions into apply statements
Nest apply(), lapply() and sapply() functions within each other
Use the which.max() and which.min() functions
Show more
Show less

Syllabus

Welcome To The Course

Welcome Challenge!

Welcome to the Advanced R Programming Course!

Learning Paths

Hey there!

Since you're taking this Advanced Analytics in R course, I'm guessing you're quite comfortable with R and most likely familiar with the name Hadley Wickham (creator of GGPlot2 and countless other R packages).

Hadley is awesome! Is he not?

Well, then...

I've got a special surprise for you.

Earlier in 2020 Hadley joined me on the SuperDataScience podcast for a deep chat about R Programming, and today I'd like to share this interview with you:

https://www.superdatascience.com/podcast/hadley-wickham-talks-integration-and-future-of-python-and-r

Listen to it during your commute to work, a hike, or simply in the comfort of your home. You can find it via the link or by searching for the SuperDataScience Podcast on any podcast app or even Spotify and navigating to episode number #337.

In this episode you will learn:

Hadley’s R packages [8:26]
Better integrations between R and Python [20:11]
LinkedIn Q&A [33:34]
useR Conference vs. RStudio Conference [50:46]
LinkedIn Q&A: Career-related questions [1:01:06]
LinkedIn Q&A: Future-related questions [1:08:01]

I'm sure you're going to enjoy this inspiring interview!

- Kirill

Get the materials

Your Shortcut To Becoming A Better Data Scientist!

Study Tips For Success

Data Preparation

Welcome to this section. This is what you will learn!

Project Brief: Financial Review

Import Data into R

What are Factors (Refresher)

The Factor Variable Trap

FVT Example

gsub() and sub()

Dealing with Missing Data

What is an NA?

An Elegant Way To Locate Missing Data

Data Filters: which() for Non-Missing Data

Data Filters: is.na() for Missing Data

Removing records with missing data

Reseting the dataframe index

Replacing Missing Data: Factual Analysis Method

Replacing Missing Data: Median Imputation Method (Part 1)

Replacing Missing Data: Median Imputation Method (Part 2)

Replacing Missing Data: Median Imputation Method (Part 3)

Replacing Missing Data: Deriving Values Method

Visualizing results

Section Recap

Lists in R

Project Brief: Machine Utilization

Import Data Into R

Handling Date-Times in R

In this lecture, you will know what the lists in R programming language are and how to create them

Naming components of a list

Extracting components lists: [] vs [[]] vs $

Adding and deleting components

Subsetting a list

Creating A Timeseries Plot

"Apply" Family of Functions

Project Brief: Weather Patterns

Here you will know about 3 main functions from Apply family and will know how to use them

Using apply()

Recreating the apply function with loops (advanced topic)

Using lapply()

Combining lapply() with []

Adding your own functions

Using sapply()

Nesting apply() functions

which.max() and which.min() (advanced topic)

THANK YOU Video

Congratulations!! Don't forget your Prize :)

Huge Congrats for completing the challenge!

Bonus: How To UNLOCK Top Salaries (Live Training)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches techniques and tools that are highly relevant to data science and analytics

Includes a mix of media, such as videos, exercises, and case studies

Multimodal course format

Instructors are recognized for their work in data science and analytics

Provides hands-on labs and interactive exercises

May require students to come in with extensive background knowledge

Reviews summary

Adequate introduction to r

According to students, this course is an adequate introduction to R with detailed explanations and examples for beginners.

Course gives detailed explanations and examples.

"I was a beginner in R. And I love all the tutorials with detail explanation and examples."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in R Programming: Advanced Analytics In R For Data Science with these activities:

Review R Basics

Show steps

Refresh your understanding of R's basic syntax and data structures to ensure a solid foundation for the course content.

Browse courses on R Programming

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Review online tutorials or documentation on R basics
Complete practice exercises to reinforce your understanding

Attend an R Bootcamp

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Immerse yourself in an R Bootcamp to gain hands-on experience and enhance your proficiency in R programming, which is crucial for the course.

Browse courses on R Programming

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Research and identify a reputable R Bootcamp
Enroll in the Bootcamp and attend all sessions
Complete the assignments and exercises

Replace missing data using a simple method

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Practice the median imputation method to gain confidence in replacing missing data.

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Import a dataset containing missing values
Identify the columns with missing data
Apply the median imputation method to the missing values

Five other activities

Expand to see all activities and additional details

Show all eight activities

Explore date-time manipulation in R

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Follow a tutorial to learn how to convert date-times into a specific time format in R, which is crucial for data analysis.

Show steps

Find a suitable tutorial on date-time manipulation in R
Follow the tutorial and complete the exercises
Apply the learned techniques to a small dataset

Work with Lists in R

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Collaborate with peers to create, manipulate, and extract data from Lists in R, enhancing your understanding of data structures.

Show steps

Form a study group with fellow learners
Choose a dataset and create a List from it
Practice accessing and modifying elements in the List
Discuss and compare different approaches to working with Lists

Apply the 'Apply' family of functions

Show steps

Gain proficiency in using the 'Apply' family of functions to perform operations on data, improving your efficiency in data manipulation.

Show steps

Understand the purpose and syntax of apply(), lapply(), and sapply()
Practice using these functions on vectors, matrices, and lists
Explore the use of anonymous functions within the 'Apply' family

Analyze Weather Patterns Using R

Show steps

Undertake a project to analyze weather patterns, combining your knowledge of R programming and data visualization to gain insights from real-world data.

Browse courses on Weather Analysis

Show steps

Gather weather data from a reliable source
Clean and prepare the data for analysis
Visualize the data using appropriate charts and graphs
Identify trends and patterns in the data
Present your findings in a clear and concise manner

Develop a Data Visualization Dashboard

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Create an interactive data visualization dashboard to showcase your understanding of data analysis and presentation techniques.

Browse courses on Data Visualization

Show steps

Choose a dataset and identify key metrics
Design and develop the dashboard using R and appropriate visualization libraries
Deploy the dashboard and share it with others

Career center

Learners who complete R Programming: Advanced Analytics In R For Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is a professional who uses data to solve business problems. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Data Scientist who wants to succeed in the field.

See salaries and explore the career path for Data Scientist

Statistician

A Statistician is a professional who collects, analyzes, interprets, and presents data. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Statistician who wants to succeed in the field.

See salaries and explore the career path for Statistician

Data Analyst

A Data Analyst is a professional who cleans, processes, and analyzes data to draw conclusions and inform decision-making. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Data Analyst who wants to succeed in the field.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer is a professional who builds and maintains data pipelines. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Data Engineer who wants to succeed in the field.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

A Machine Learning Engineer is a professional who designs and develops machine learning models. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Machine Learning Engineer who wants to succeed in the field.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

An Operations Research Analyst is a professional who uses mathematical and statistical models to solve business problems. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Operations Research Analyst who wants to succeed in the field.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Quantitative Analyst who wants to succeed in the field.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

A Financial Analyst is a professional who analyzes financial data to make investment decisions. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Financial Analyst who wants to succeed in the field.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

A Market Research Analyst is a professional who conducts research to identify and understand customer needs. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Market Research Analyst who wants to succeed in the field.

See salaries and explore the career path for Market Research Analyst

Business Analyst

A Business Analyst is a professional who analyzes business data to identify opportunities and improve decision-making. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Business Analyst who wants to succeed in the field.

See salaries and explore the career path for Business Analyst

Actuary

An Actuary is a professional who uses mathematical and statistical models to assess risk. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Actuary who wants to succeed in the field.

See salaries and explore the career path for Actuary

Epidemiologist

An Epidemiologist is a professional who studies the distribution and determinants of health-related states or events in specified populations. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Epidemiologist who wants to succeed in the field.

See salaries and explore the career path for Epidemiologist

Biostatistician

A Biostatistician is a professional who applies statistical methods to biological data. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Biostatistician who wants to succeed in the field.

See salaries and explore the career path for Biostatistician

Data Miner

A Data Miner is a professional who uses data mining techniques to extract knowledge from data. This course can help you build the skills necessary to succeed in this role by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills are essential for any Data Miner who wants to succeed in the field.

See salaries and explore the career path for Data Miner

Information Systems Manager

An Information Systems Manager is a professional who plans, implements, and manages information systems. This course may be useful by providing a strong foundation in R programming and data analysis techniques. You will learn how to prepare data for analysis, work with date-times, use the Apply family of functions, and more. These skills can be helpful for any Information Systems Manager who wants to succeed in the field.

See salaries and explore the career path for Information Systems Manager