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

Ready to take your R Programming skills to the next level?

Want to truly become proficient at Data Science and Analytics with R?

This course is for you.

Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the

In this course, you will learn:

How to prepare data for analysis in R
How to perform the median imputation method in R
How to work with date-times in R
What Lists are and how to use them
What the Apply family of functions is
How to use apply(), lapply() and sapply() instead of loops
How to nest your own functions within apply-type functions
How to nest apply(), lapply() and sapply() functions within each other
And much, much more.

The more you learn, the better you will get. After every module, you will have a robust set of skills to take with you into your Data Science career.

We prepared real-life case studies.

In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.

In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.

In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.

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

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

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

Traffic lights

Read about what's good

what should give you pause

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

Advanced r for data analytics

According to learners, this course provides a largely positive experience for those looking to deepen their skills in R for data science. Students found the course content, particularly the sections on data preparation and the Apply family of functions, to be highly valuable and well-explained. The practical examples and real-world case studies were frequently highlighted as strengths, helping solidify understanding and providing immediately applicable skills. While some learners noted that a solid foundation in R is necessary to keep up with the pace, the course is widely regarded as an excellent step towards becoming proficient in advanced R analytics.

Course is advanced; requires prior R experience.

"This course is definitely not for absolute beginners. You need a good understanding of R basics before starting."

"Be sure you are comfortable with R syntax and data structures before attempting this course; it moves quickly."

"The 'Advanced' in the title is accurate. Having prior experience made it much easier to grasp the concepts and keep pace."

"If you've completed a beginner R course, this is a logical next step, but don't jump in cold."

Strong focus on preparing data for analysis.

"The modules on data preparation, especially handling missing data and date-times, were very practical."

"Learning about methods like median imputation was directly applicable to the data cleaning tasks I face regularly."

"Cleaning and preparing the financial data was a solid introduction to common data wrangling challenges."

"The coverage of gsub(), sub(), which(), and is.na() for data manipulation was thorough and useful."

Deep dive into apply functions is beneficial.

"The section on the apply family of functions was particularly illuminating; I finally understand how to use lapply and sapply effectively."

"Understanding apply(), lapply(), and sapply() instead of loops is a major takeaway and significantly improved my R efficiency."

"The nested apply examples were advanced but very helpful for understanding complex operations."

"This module alone was worth the course price for me, clarifying concepts I struggled with before."

Concepts are explained clearly by the instructor.

"The instructor's explanations were clear and easy to follow, making complex topics understandable."

"Kirill does an excellent job of breaking down the material."

"I appreciated the step-by-step approach the instructor took when explaining code and methods."

"The teaching style is engaging, keeping me motivated throughout the modules."

Real-world case studies and examples are helpful.

"The hands-on exercises and case studies were very useful; working with financial data helped connect concepts to real-world applications."

"I particularly liked the case studies. They were challenging yet practical, giving a good feel for actual data analysis tasks."

"The course provides practical tools and strategies that I could apply immediately to my work."

"Using real data sets made the learning much more engaging and relevant than just theoretical 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

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

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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 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 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

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

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

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

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

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

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

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

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.

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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